(define-module (guix-cran packages m)
  #: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 java)
  #:use-module (gnu packages maths)
  #:use-module (gnu packages gcc)
  #:use-module (gnu packages bioconductor)
  #:use-module (gnu packages pkg-config)
  #:use-module (gnu packages multiprecision)
  #:use-module (gnu packages cmake)
  #:use-module (gnu packages version-control)
  #:use-module (gnu packages python-xyz)
  #:use-module (gnu packages python-science)
  #:use-module (gnu packages python)
  #:use-module (gnu packages tls)
  #:use-module (gnu packages compression)
  #:use-module (gnu packages web)
  #:use-module (gnu packages machine-learning)
  #:use-module (gnu packages xml)
  #:use-module (gnu packages haskell-xyz)
  #:use-module (gnu packages tbb)
  #:use-module (gnu packages geo)
  #:use-module (gnu packages imagemagick)
  #: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 l)
  #:use-module (guix-cran packages k)
  #:use-module (guix-cran packages j)
  #:use-module (guix-cran packages i)
  #:use-module (guix-cran packages h)
  #:use-module (guix-cran packages g)
  #:use-module (guix-cran packages f)
  #:use-module (guix-cran packages e)
  #:use-module (guix-cran packages d)
  #:use-module (guix-cran packages c)
  #:use-module (guix-cran packages b)
  #:use-module (guix-cran packages a))

(define-public r-mzipmed
  (package
    (name "r-mzipmed")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mzipmed" version))
       (sha256
        (base32 "18k7ffq9mkslkxz4j0xj3xh0050gkysp2cls6w7825warflgclwz"))))
    (properties `((upstream-name . "mzipmed")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-matrixstats r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mzipmed")
    (synopsis "Mediation using MZIP Model")
    (description
     "We implement functions allowing for mediation analysis to be performed in cases
where the mediator is a count variable with excess zeroes.  First a function is
provided allowing users to perform analysis for zero-inflated count variables
using the marginalized zero-inflated Poisson (MZIP) model (Long et al.  2014
<DOI:10.1002/sim.6293>).  Using the counterfactual approach to mediation and
MZIP we can obtain natural direct and indirect effects for the overall
population.  Using delta method processes variance estimation can be performed
instantaneously.  Alternatively, bootstrap standard errors can be used.  We also
provide functions for cases with exposure-mediator interactions with four-way
decomposition of total effect.")
    (license license:expat)))

(define-public r-mycran
  (package
    (name "r-mycran")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "myCRAN" version))
       (sha256
        (base32 "1b92jyk2nwlqx0sx05b1gj3ivy33rjz1394gazl0xm98jj1c1vkx"))))
    (properties `((upstream-name . "myCRAN")))
    (build-system r-build-system)
    (propagated-inputs (list r-pkgsearch r-cranlogs))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=myCRAN")
    (synopsis "Graph of Daily and Cumulative Downloads of your Packages")
    (description
     "Plot the daily and cumulative number of downloads of your packages.  It is
designed to be slightly more convenient than the several similar programs.  If
you want to run this each morning, you do not need to keep typing in the names
of your packages.  Also, this combines the daily and cumulative counts in one
run, you do not need to run separate programs to get both types of information.")
    (license license:gpl2+)))

(define-public r-mycor
  (package
    (name "r-mycor")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mycor" version))
       (sha256
        (base32 "00hqmvga22bv43833s974s7ky7lbry3r2bb5kza7kvkn1p6773rg"))))
    (properties `((upstream-name . "mycor")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cardiomoon/mycor")
    (synopsis "Automatic Correlation and Regression Test in a 'data.frame'")
    (description
     "Perform correlation and linear regression test among the numeric fields in a
data.frame automatically and make plots using pairs or lattice::parallelplot.")
    (license license:cc0)))

(define-public r-mycobacrvr
  (package
    (name "r-mycobacrvr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mycobacrvR" version))
       (sha256
        (base32 "1hrn2v6wvw8xllqd1bhxb50hj9icinfrar6k9jgl0wjcsn024fc1"))))
    (properties `((upstream-name . "mycobacrvR")))
    (build-system r-build-system)
    (home-page "https://mycobacteriarv.igib.res.in/download.html")
    (synopsis
     "Integrative Immunoinformatics for Mycobacterial Diseases in R Platform")
    (description
     "The @code{mycobacrvR} package contains utilities to provide detailed information
for B cell and T cell epitopes for predicted adhesins from various servers such
as ABCpred, Bcepred, Bimas, Propred, @code{NetMHC} and IEDB. Please refer the
URL below to download data files (data_@code{mycobacrvR.zip}) used in functions
of this package.")
    (license license:gpl2)))

(define-public r-myclim
  (package
    (name "r-myclim")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "myClim" version))
       (sha256
        (base32 "0ss1wvvl46nkw93zgl0p89z7y7sqgfw1kqkbhvyalhyyxvvjy5nm"))))
    (properties `((upstream-name . "myClim")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-vroom
                             r-viridis
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-progress
                             r-plotly
                             r-lubridate
                             r-ggplot2
                             r-ggforce
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "http://labgis.ibot.cas.cz/myclim/index.html")
    (synopsis "Microclimatic Data Processing")
    (description
     "Handling the microclimatic data in R. The @code{myClim} workflow begins at the
reading data primary from microclimatic dataloggers, but can be also reading of
meteorological station data from files.  Cleaning time step, time zone settings
and metadata collecting is the next step of the work flow.  With @code{myClim}
tools one can crop, join, downscale, and convert microclimatic data formats,
sort them into localities, request descriptive characteristics and compute
microclimatic variables.  Handy plotting functions are provided with smart
defaults.")
    (license license:gpl2+)))

(define-public r-mychisq
  (package
    (name "r-mychisq")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mychisq" version))
       (sha256
        (base32 "0667ldi1qm47cpdr0j7gv7c6f8s5wj7naqcgv9harrsmm4vsr4w6"))))
    (properties `((upstream-name . "Mychisq")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Mychisq")
    (synopsis "Chi-Squared Test for Goodness of Fit and Independence Test")
    (description
     "The chi-squared test for goodness of fit and independence test.")
    (license license:gpl3)))

(define-public r-mycaas
  (package
    (name "r-mycaas")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mycaas" version))
       (sha256
        (base32 "1gjpn8mz8vp3410s6fbl3ndkasfzywzmyp33qzf5f29sirk2cn4m"))))
    (properties `((upstream-name . "mycaas")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rpref r-rlang r-igraph r-ggplot2))
    (home-page "https://cran.r-project.org/package=mycaas")
    (synopsis "My Computerized Adaptive Assessment")
    (description
     "Implementation of adaptive assessment procedures based on Knowledge Space Theory
(KST, Doignon & Falmagne, 1999 <ISBN:9783540645016>) and Formal Psychological
Assessment (FPA, Spoto, Stefanutti & Vidotto, 2010 <doi:10.3758/BRM.42.1.342>)
frameworks.  An adaptive assessment is a type of evaluation that adjusts the
difficulty and nature of subsequent questions based on the test taker's
responses to previous ones.  The package contains functions to perform and
simulate an adaptive assessment.  Moreover, it is integrated with two Shiny
interfaces, making it both accessible and user-friendly.  The package has been
partially funded by the European Union - @code{NextGenerationEU} and by the
Ministry of University and Research (MUR), National Recovery and Resilience Plan
(NRRP), Mission 4, Component 2, Investment 1.5, project âRAISE - Robotics and
AI for Socio-economic Empowermentâ (ECS00000035).")
    (license license:expat)))

(define-public r-my-stepwise
  (package
    (name "r-my-stepwise")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "My.stepwise" version))
       (sha256
        (base32 "10wka6kzk8krwdsyjfnn78dfqflq4zg3vjkk0i2ywjm5vap6aqa5"))))
    (properties `((upstream-name . "My.stepwise")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-lmtest r-car))
    (home-page "https://cran.r-project.org/package=My.stepwise")
    (synopsis "Stepwise Variable Selection Procedures for Regression Analysis")
    (description
     "The stepwise variable selection procedure (with iterations between the forward
and backward steps) can be used to obtain the best candidate final regression
model in regression analysis.  All the relevant covariates are put on the
variable list to be selected.  The significance levels for entry (SLE) and for
stay (SLS) are usually set to 0.15 (or larger) for being conservative.  Then,
with the aid of substantive knowledge, the best candidate final regression model
is identified manually by dropping the covariates with p value > 0.05 one at a
time until all regression coefficients are significantly different from 0 at the
chosen alpha level of 0.05.")
    (license license:gpl3+)))

(define-public r-mxsem
  (package
    (name "r-mxsem")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mxsem" version))
       (sha256
        (base32 "0mhy8nbg9xagsdacyy1q0ri0j6jdsidsmcx12k736axi0805x0m1"))))
    (properties `((upstream-name . "mxsem")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-openmx r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://jhorzek.github.io/mxsem/")
    (synopsis "Specify 'OpenMx' Models with a 'lavaan'-Style Syntax")
    (description
     "This package provides a lavaan'-like syntax for @code{OpenMx} models.  The
syntax supports definition variables, bounds, and parameter transformations.
This allows for latent growth curve models with person-specific measurement
occasions, moderated nonlinear factor analysis and much more.")
    (license license:gpl3+)))

(define-public r-mxnorm
  (package
    (name "r-mxnorm")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mxnorm" version))
       (sha256
        (base32 "01845m5h4rd38n0fll2k2d9hw6j9dbjgs7kmwy30g9lkffvv73s8"))))
    (properties `((upstream-name . "mxnorm")))
    (build-system r-build-system)
    (propagated-inputs (list r-uwot
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-reticulate
                             r-psych
                             r-magrittr
                             r-lme4
                             r-ksamples
                             r-kernsmooth
                             r-glue
                             r-ggplot2
                             r-fossil
                             r-fda
                             r-e1071
                             r-dplyr
                             r-data-table
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mxnorm")
    (synopsis "Apply Normalization Methods to Multiplexed Images")
    (description
     "This package implements methods to normalize multiplexed imaging data, including
statistical metrics and visualizations to quantify technical variation in this
data type.  Reference for methods listed here: Harris, C., Wrobel, J., &
Vandekar, S. (2022).  mxnorm: An R Package to Normalize Multiplexed Imaging
Data.  Journal of Open Source Software, 7(71), 4180, <doi:10.21105/joss.04180>.")
    (license license:expat)))

(define-public r-mxmmod
  (package
    (name "r-mxmmod")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mxmmod" version))
       (sha256
        (base32 "0x4m6y4ngb3vm9xpvwvn1zcbch2fhz8pb267hg2sb3kk5m504xxq"))))
    (properties `((upstream-name . "mxmmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-openmx))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mxmmod")
    (synopsis "Measurement Model of Derivatives in 'OpenMx'")
    (description
     "This package provides a convenient interface in @code{OpenMx} for building
Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of Derivatives
(MMOD).")
    (license license:asl2.0)))

(define-public r-mxm
  (package
    (name "r-mxm")
    (version "1.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MXM" version))
       (sha256
        (base32 "15r4jnf1l2nrf67xkpvzg2h7yxxhjlkr454n691ssss9z0zpsgcl"))))
    (properties `((upstream-name . "MXM")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-survival
                             r-rfast2
                             r-rfast
                             r-relations
                             r-quantreg
                             r-ordinal
                             r-nnet
                             r-mass
                             r-lme4
                             r-knitr
                             r-hmisc
                             r-geepack
                             r-foreach
                             r-energy
                             r-dplyr
                             r-doparallel
                             r-coxme
                             r-bigmemory))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "http://mensxmachina.org")
    (synopsis
     "Feature Selection (Including Multiple Solutions) and Bayesian Networks")
    (description
     "Many feature selection methods for a wide range of response variables, including
minimal, statistically-equivalent and equally-predictive feature subsets.
Bayesian network algorithms and related functions are also included.  The
package name MXM stands for \"Mens @code{eX} Machina\", meaning \"Mind from the
Machine\" in Latin.  References: a) Lagani, V. and Athineou, G. and Farcomeni, A.
and Tsagris, M. and Tsamardinos, I. (2017).  Feature Selection with the R
Package MXM: Discovering Statistically Equivalent Feature Subsets.  Journal of
Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>.  b) Tsagris, M.,
Lagani, V. and Tsamardinos, I. (2018).  Feature selection for high-dimensional
temporal data.  BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>.  c)
Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018).
Constraint-based causal discovery with mixed data.  International Journal of
Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>.  d)
Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018).
Efficient feature selection on gene expression data: Which algorithm to use?
@code{BioRxiv}. <doi:10.1101/431734>.  e) Tsagris, M. (2019).  Bayesian Network
Learning with the PC Algorithm: An Improved and Correct Variation.  Applied
Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>.  f)
Tsagris, M. and Tsamardinos, I. (2019).  Feature selection with the R package
MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>.  g)
Borboudakis, G. and Tsamardinos, I. (2019).  Forward-Backward Selection with
Early Dropping.  Journal of Machine Learning Research 20: 1-39.  h) The
gamma-OMP algorithm for feature selection with application to gene expression
data.  IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2):
1214-1224. <doi:10.1109/TCBB.2020.3029952>.")
    (license license:gpl2)))

(define-public r-mxkssd
  (package
    (name "r-mxkssd")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mxkssd" version))
       (sha256
        (base32 "19zz5y5gz84a7qicaj3v2j6l558vr6w8lcy67b3kmkq64mnnirda"))))
    (properties `((upstream-name . "mxkssd")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mxkssd")
    (synopsis "Efficient Mixed-Level k-Circulant Supersaturated Designs")
    (description
     "Generates efficient balanced mixed-level k-circulant supersaturated designs by
interchanging the elements of the generator vector.  Attempts to generate a
supersaturated design that has @code{EfNOD} efficiency more than user specified
efficiency level (mef).  Displays the progress of generation of an efficient
mixed-level k-circulant design through a progress bar.  The progress of 100 per
cent means that one full round of interchange is completed.  More than one full
round (typically 4-5 rounds) of interchange may be required for larger designs.
For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011).
Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs,
Journal of Statistical Theory and Practice, 5:4, 627-648,
<doi:10.1080/15598608.2011.10483735>.")
    (license license:gpl2+)))

(define-public r-mxfda
  (package
    (name "r-mxfda")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mxfda" version))
       (sha256
        (base32 "1fzbnsfyqav87lhc8w910wzqilw429x8swm5s6a4991p5dy19pvr"))))
    (properties `((upstream-name . "mxfda")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatentropy
                             r-simdesign
                             r-rlang
                             r-reshape2
                             r-refund
                             r-purrr
                             r-mgcv
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/julia-wrobel/mxfda/")
    (synopsis "Functional Data Analysis Package for Spatial Single Cell Data")
    (description
     "This package provides methods and tools for deriving spatial summary functions
from single-cell imaging data and performing functional data analyses.
Functions can be applied to other single-cell technologies such as spatial
transcriptomics.  Functional regression and functional principal component
analysis methods are in the refund package
<https://cran.r-project.org/package=refund> while calculation of the spatial
summary functions are from the spatstat package <https://spatstat.org/>.")
    (license license:expat)))

(define-public r-mwtensor
  (package
    (name "r-mwtensor")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mwTensor" version))
       (sha256
        (base32 "113asdl3hm1kk31x29x89bz3zxccrhbh64xl2fcgds1mkw0gr3mn"))))
    (properties `((upstream-name . "mwTensor")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtensor
                             r-nntensor
                             r-mass
                             r-itensor
                             r-igraph
                             r-cctensor))
    (home-page "https://github.com/rikenbit/mwTensor")
    (synopsis "Multi-Way Component Analysis")
    (description
     "For single tensor data, any matrix factorization method can be specified the
matricised tensor in each dimension by Multi-way Component Analysis (MWCA).  An
originally extended MWCA is also implemented to specify and decompose multiple
matrices and tensors simultaneously (@code{CoupledMWCA}).  See the reference
section of @code{GitHub} README.md
<https://github.com/rikenbit/@code{mwTensor>}, for details of the methods.")
    (license license:expat)))

(define-public r-mwshiny
  (package
    (name "r-mwshiny")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mwshiny" version))
       (sha256
        (base32 "0c1a0d9qnaszffhzanmk31mdxqi7j3rn6bk7a3dwg4bq1da3rlgl"))))
    (properties `((upstream-name . "mwshiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mwshiny")
    (synopsis "'Shiny' for Multiple Windows")
    (description
     "This package provides a simple function, @code{mwsApp}(), that runs a shiny app
spanning multiple, connected windows.  This uses all standard shiny conventions,
and depends only on the shiny package.")
    (license license:expat)))

(define-public r-mwright
  (package
    (name "r-mwright")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MWright" version))
       (sha256
        (base32 "1c57f0wnlhl4gqy17vi93r4fknycvv67b9bifgcvabws390jpx7q"))))
    (properties `((upstream-name . "MWright")))
    (build-system r-build-system)
    (propagated-inputs (list r-cubature))
    (home-page "https://cran.r-project.org/package=MWright")
    (synopsis "Mainardi-Wright Family of Distributions")
    (description
     "This package implements random number generation, plotting, and estimation
algorithms for the two-parameter one-sided and two-sided M-Wright
(Mainardi-Wright) family.  The M-Wright distributions naturally generalize the
widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric
(Airy and Gaussian or normal) models.  These are widely studied in
time-fractional differential equations.  References: Cahoy and Minkabo (2017)
<doi:10.3233/MAS-170388>; Cahoy (2012) <doi:10.1007/s00180-011-0269-x>; Cahoy
(2012) <doi:10.1080/03610926.2010.543299>; Cahoy (2011); Mainardi, Mura, and
Pagnini (2010) <doi:10.1155/2010/104505>.")
    (license license:gpl3+)))

(define-public r-mwlaxeref
  (package
    (name "r-mwlaxeref")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mwlaxeref" version))
       (sha256
        (base32 "1rpm2l10xipggva5ghlvg0h9w0nvwkpc9a2ai1j4skp3f30zgczz"))))
    (properties `((upstream-name . "mwlaxeref")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mwlaxeref")
    (synopsis "Cross-References Lake Identifiers Between Different Data Sets")
    (description
     "Handy helper package for cross-referencing lake identifiers among different data
sets in the Midwestern United States.  There are multiple different state,
regional, and federal agencies that have different identifiers on lakes.  This
package helps you to go between them.")
    (license license:expat)))

(define-public r-mwcsr
  (package
    (name "r-mwcsr")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mwcsr" version))
       (sha256
        (base32 "09pw4x9kbmbk3cnvh1xxwfan6kc4bmab52jly9fm3b0nji3apbkj"))))
    (properties `((upstream-name . "mwcsr")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rcpp r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ctlab/mwcsr")
    (synopsis
     "Solvers for Maximum Weight Connected Subgraph Problem and Its Variants")
    (description
     "Algorithms for solving various Maximum Weight Connected Subgraph Problems,
including variants with budget constraints, cardinality constraints, weighted
edges and signals.  The package represents an R interface to high-efficient
solvers based on relax-and-cut approach (Ãlvarez-Miranda E., Sinnl M. (2017)
<doi:10.1016/j.cor.2017.05.015>) mixed-integer programming (Loboda A., Artyomov
M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>) and simulated
annealing.")
    (license license:expat)))

(define-public r-mwa
  (package
    (name "r-mwa")
    (version "0.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mwa" version))
       (sha256
        (base32 "0f994xpylqhhwwr3rv0y5y68989ssy7q4pb3hawq0ww0l3wj9lmd"))))
    (properties `((upstream-name . "mwa")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava r-mass r-cem))
    (home-page "https://cran.r-project.org/package=mwa")
    (synopsis "Causal Inference in Spatiotemporal Event Data")
    (description
     "Implementation of Matched Wake Analysis (mwa) for studying causal relationships
in spatiotemporal event data, introduced by Schutte and Donnay (2014)
<doi:10.1016/j.polgeo.2014.03.001>.")
    (license license:lgpl3)))

(define-public r-mvtsplot
  (package
    (name "r-mvtsplot")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvtsplot" version))
       (sha256
        (base32 "0n7lfxr3hq32fpg0vv9v0426k4dlgadfcjrjg7kj3abahykx7zl9"))))
    (properties `((upstream-name . "mvtsplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer))
    (home-page "https://github.com/rdpeng/mvtsplot")
    (synopsis "Multivariate Time Series Plot")
    (description
     "This package provides a function for plotting multivariate time series data.")
    (license license:gpl2+)))

(define-public r-mvtmeta
  (package
    (name "r-mvtmeta")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvtmeta" version))
       (sha256
        (base32 "0s0s5922c5rs8cqk100nci3akqnhbvibm2n2gn8049v3qcyfajyb"))))
    (properties `((upstream-name . "mvtmeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-gtools))
    (home-page "https://cran.r-project.org/package=mvtmeta")
    (synopsis "Multivariate Meta-Analysis")
    (description
     "This package provides functions to run fixed effects or random effects
multivariate meta-analysis.")
    (license license:gpl3)))

(define-public r-mvtests
  (package
    (name "r-mvtests")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVTests" version))
       (sha256
        (base32 "02rl7f249cis4jxqpszwmxr9fc483yhrni0rdyiwamv5q5j1bifz"))))
    (properties `((upstream-name . "MVTests")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrcov r-robustbase r-mvtnorm))
    (home-page "https://cran.r-project.org/package=MVTests")
    (synopsis "Multivariate Hypothesis Tests")
    (description
     "Multivariate hypothesis tests and the confidence intervals.  It can be used to
test the hypothesizes about mean vector or vectors (one-sample, two independent
samples, paired samples), covariance matrix (one or more matrices), and the
correlation matrix.  Moreover, it can be used for robust Hotelling T^2 test at
one sample case in high dimensional data.  For this package, we have benefited
from the studies Rencher (2003), Nel and Merwe (1986) <DOI:
10.1080/03610928608829342>, Tatlidil (1996), Tsagris (2014), Villasenor Alva and
Estrada (2009) <DOI: 10.1080/03610920802474465>.")
    (license license:gpl2)))

(define-public r-mvt
  (package
    (name "r-mvt")
    (version "0.3-81")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVT" version))
       (sha256
        (base32 "0g4h0r17sn5xmdkln4sllwmqp1r5wc8y6idfilbz7zp9bz6i53zj"))))
    (properties `((upstream-name . "MVT")))
    (build-system r-build-system)
    (propagated-inputs (list r-fastmatrix))
    (home-page "http://mvt.mat.utfsm.cl/")
    (synopsis "Estimation and Testing for the Multivariate t-Distribution")
    (description
     "Routines to perform estimation and inference under the multivariate
t-distribution <doi:10.1007/s10182-022-00468-2>.  Currently, the following
methodologies are implemented: multivariate mean and covariance estimation,
hypothesis testing about equicorrelation and homogeneity of variances, the
Wilson-Hilferty transformation, QQ-plots with envelopes and random variate
generation.")
    (license license:gpl3)))

(define-public r-mvsusy
  (package
    (name "r-mvsusy")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvSUSY" version))
       (sha256
        (base32 "1yvgx439m37ifdviaiykhpxrfw7k43dnvpxc56532w7vwxbvpcd7"))))
    (properties `((upstream-name . "mvSUSY")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppalgos r-ggsci r-ggplot2 r-data-table))
    (home-page "https://wtschacher.github.io/mvSUSY/")
    (synopsis "Multivariate Surrogate Synchrony")
    (description
     "Multivariate Surrogate Synchrony ('@code{mvSUSY}') estimates the synchrony
within datasets that contain more than two time series. @code{mvSUSY} was
developed from Surrogate Synchrony ('SUSY') with respect to implementing
surrogate controls, and extends synchrony estimation to multivariate data.
@code{mvSUSY} works as described in Meier & Tschacher (2021).")
    (license license:gpl2)))

(define-public r-mvst
  (package
    (name "r-mvst")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvst" version))
       (sha256
        (base32 "0zfy7lgghmx6ziybxp3w1dar49bhnpzxjmdkzdnkcicrc4yjdl7w"))))
    (properties `((upstream-name . "mvst")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-mvtnorm r-mnormt r-mcmcpack))
    (home-page "https://cran.r-project.org/package=mvst")
    (synopsis "Bayesian Inference for the Multivariate Skew-t Model")
    (description
     "Estimates the multivariate skew-t and nested models, as described in the
articles Liseo, B., Parisi, A. (2013).  Bayesian inference for the multivariate
skew-normal model: a population Monte Carlo approach.  Comput.  Statist.  Data
Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017).
Objective Bayesian analysis for the multivariate skew-t model.  Statistical
Methods & Applications <doi: 10.1007/s10260-017-0404-0>.")
    (license license:gpl3)))

(define-public r-mvslouch
  (package
    (name "r-mvslouch")
    (version "2.7.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvSLOUCH" version))
       (sha256
        (base32 "1l11dza28m5l6iam90z2vvmpabp91s3rmnb7b1x78c77ss1r7gig"))))
    (properties `((upstream-name . "mvSLOUCH")))
    (build-system r-build-system)
    (propagated-inputs (list r-pcmbase
                             r-ouch
                             r-mvtnorm
                             r-matrixcalc
                             r-matrix
                             r-ape
                             r-abind))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=mvSLOUCH")
    (synopsis
     "Multivariate Stochastic Linear Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses")
    (description
     "Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data
from species related by a common evolutionary history.  See K. Bartoszek, J,
Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012)
<doi:10.1016/j.jtbi.2012.08.005>.  The suggested PCM@code{BaseCpp} package
(which significantly speeds up the likelihood calculations) can be obtained from
<https://github.com/venelin/PCM@code{BaseCpp/>}.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file LICENCE")))))

(define-public r-mvshapirotest
  (package
    (name "r-mvshapirotest")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvShapiroTest" version))
       (sha256
        (base32 "0zcv5l28gwipkmymk12l4wcj9v047pr8k8q5avljdrs2a37f74v1"))))
    (properties `((upstream-name . "mvShapiroTest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mvShapiroTest")
    (synopsis "Generalized Shapiro-Wilk test for multivariate normality")
    (description
     "This package implements the generalization of the Shapiro-Wilk test for
multivariate normality proposed by Villasenor-Alva and Gonzalez-Estrada (2009).")
    (license license:gpl3+)))

(define-public r-mvs
  (package
    (name "r-mvs")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvs" version))
       (sha256
        (base32 "1fkkxrx5mraa2zlmgq9fgka2rvzn95yzaqby8slhyfpsrzxqwm4y"))))
    (properties `((upstream-name . "mvs")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet r-foreach))
    (home-page "https://cran.r-project.org/package=mvs")
    (synopsis "Methods for High-Dimensional Multi-View Learning")
    (description
     "This package provides methods for high-dimensional multi-view learning based on
the multi-view stacking (MVS) framework.  For technical details on the MVS and
stacked penalized logistic regression (@code{StaPLR}) methods see Van Loon,
Fokkema, Szabo, & De Rooij (2020) <doi:10.1016/j.inffus.2020.03.007> and Van
Loon et al. (2022) <doi:10.3389/fnins.2022.830630>.")
    (license license:gpl2)))

(define-public r-mvrsquared
  (package
    (name "r-mvrsquared")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvrsquared" version))
       (sha256
        (base32 "00i86rdhmia7c8h8n9ck8yn4wr65ra4z245bsqanchj5bsza6p0i"))))
    (properties `((upstream-name . "mvrsquared")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppthread r-rcpparmadillo r-rcpp r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/TommyJones/mvrsquared")
    (synopsis
     "Compute the Coefficient of Determination for Vector or Matrix Outcomes")
    (description
     "Compute the coefficient of determination for outcomes in n-dimensions.  May be
useful for multidimensional predictions (such as a multinomial model) or
calculating goodness of fit from latent variable models such as probabilistic
topic models like latent Dirichlet allocation or deterministic topic models like
latent semantic analysis.  Based on Jones (2019) <@code{arXiv:1911.11061>}.")
    (license license:expat)))

(define-public r-mvr
  (package
    (name "r-mvr")
    (version "1.33.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVR" version))
       (sha256
        (base32 "1ygz47a2p5r5axql5zsxvsn25pcqyxz6mpw50zfmzrcqdrfa3bg8"))))
    (properties `((upstream-name . "MVR")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod))
    (home-page "https://github.com/jedazard/MVR")
    (synopsis "Mean-Variance Regularization")
    (description
     "This is a non-parametric method for joint adaptive mean-variance regularization
and variance stabilization of high-dimensional data.  It is suited for handling
difficult problems posed by high-dimensional multivariate datasets (p >> n
paradigm).  Among those are that the variance is often a function of the mean,
variable-specific estimators of variances are not reliable, and tests statistics
have low powers due to a lack of degrees of freedom.  Key features include: (i)
Normalization and/or variance stabilization of the data, (ii) Computation of
mean-variance-regularized t-statistics (F-statistics to follow), (iii)
Generation of diverse diagnostic plots, (iv) Computationally efficient
implementation using C/C++ interfacing and an option for parallel computing to
enjoy a faster and easier experience in the R environment.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mvquickgraphs
  (package
    (name "r-mvquickgraphs")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVQuickGraphs" version))
       (sha256
        (base32 "1937w8y866bsn9kn6a98c91akhm9ddc5dr9wqm51lkbjyz1zm0j1"))))
    (properties `((upstream-name . "MVQuickGraphs")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotrix))
    (home-page "https://cran.r-project.org/package=MVQuickGraphs")
    (synopsis "Quick Multivariate Graphs")
    (description
     "This package provides functions used for graphing in multivariate contexts.
These functions are designed to support produce reasonable graphs with minimal
input of graphing parameters.  The motivation for these functions was to support
students learning multivariate concepts and R - there may be other functions and
packages better-suited to practical data analysis.  For details about the
ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153).")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mvquad
  (package
    (name "r-mvquad")
    (version "1.0-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvQuad" version))
       (sha256
        (base32 "053qrqmd946y1qi1zn6kag41gxn9wdlp0zffw5ya5yyllxfzgszx"))))
    (properties `((upstream-name . "mvQuad")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/weiserc/mvQuad/")
    (synopsis "Methods for Multivariate Quadrature")
    (description
     "This package provides methods to construct multivariate grids, which can be used
for multivariate quadrature.  This grids can be based on different quadrature
rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
quadrature (Gauss-Hermite, Gauss-Legendre, ...).  For the construction of the
multidimensional grid the product-rule or the combination- technique can be
applied.")
    (license license:gpl3)))

(define-public r-mvprobit
  (package
    (name "r-mvprobit")
    (version "0.1-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvProbit" version))
       (sha256
        (base32 "1iy249mc5fi72l0lcvjrn40fn6q1m8hj8njcl0li2z6lzydifkd4"))))
    (properties `((upstream-name . "mvProbit")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-misctools r-maxlik r-bayesm r-abind))
    (home-page "http://www.sampleSelection.org")
    (synopsis "Multivariate Probit Models")
    (description
     "This package provides tools for estimating multivariate probit models,
calculating conditional and unconditional expectations, and calculating marginal
effects on conditional and unconditional expectations.")
    (license license:gpl2+)))

(define-public r-mvpd
  (package
    (name "r-mvpd")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvpd" version))
       (sha256
        (base32 "10k82g2izv72k99j99mjljg2hxb66pzhgfqal2j5z3cj3gmkjabg"))))
    (properties `((upstream-name . "mvpd")))
    (build-system r-build-system)
    (propagated-inputs (list r-stabledist
                             r-mvtnorm
                             r-matrixstats
                             r-matrix
                             r-libstable4u
                             r-cubature))
    (home-page "https://github.com/swihart/mvpd")
    (synopsis
     "Multivariate Product Distributions for Elliptically Contoured Distributions")
    (description
     "Estimates multivariate subgaussian stable densities and probabilities as well as
generates random variates using product distribution theory.  A function for
estimating the parameters from data to fit a distribution to data is also
provided, using the method from Nolan (2013) <doi:10.1007/s00180-013-0396-7>.")
    (license license:expat)))

(define-public r-mvpbt
  (package
    (name "r-mvpbt")
    (version "1.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVPBT" version))
       (sha256
        (base32 "19i8sgmdndvxiars2za605f3lcxw7cy2ff22n2llkpl5196r3mzl"))))
    (properties `((upstream-name . "MVPBT")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvmeta r-metafor r-mass r-mada))
    (home-page "https://cran.r-project.org/package=MVPBT")
    (synopsis
     "Publication Bias Tests for Meta-Analysis of Diagnostic Accuracy Test")
    (description
     "Generalized Egger tests for detecting publication bias in meta-analysis for
diagnostic accuracy test (Noma (2020) <doi:10.1111/biom.13343>, Noma (2022)
<doi:10.48550/@code{arXiv.2209.07270>}).  These publication bias tests are
generally more powerful compared with the conventional univariate publication
bias tests and can incorporate correlation information between the outcome
variables.")
    (license license:gpl3)))

(define-public r-mvp
  (package
    (name "r-mvp")
    (version "1.0-14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvp" version))
       (sha256
        (base32 "0dzf5h5ng0vx6nw8j8q9ydksicyxy940p363n367gbs56c8x47dk"))))
    (properties `((upstream-name . "mvp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-partitions
                             r-numbers
                             r-mpoly
                             r-magrittr
                             r-magic
                             r-disordr
                             r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/RobinHankin/mvp")
    (synopsis "Fast Symbolic Multivariate Polynomials")
    (description
     "Fast manipulation of symbolic multivariate polynomials using the Map class of
the Standard Template Library.  The package uses print and coercion methods from
the mpoly package (Kahle 2013, \"Multivariate polynomials in R\", The R Journal,
5(1):162), but offers speed improvements.  It is comparable in speed to the
spray package for sparse arrays, but retains the symbolic benefits of mpoly'.
To cite the package in publications, use Hankin 2022
<doi:10.48550/ARXIV.2210.15991>.  Uses @code{disordR} discipline.")
    (license license:gpl2+)))

(define-public r-mvoutlier
  (package
    (name "r-mvoutlier")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvoutlier" version))
       (sha256
        (base32 "1ll6zsy8svcsb3h60p0z2zpbgn919j9w0mamsdvvzi87dyy4hars"))))
    (properties `((upstream-name . "mvoutlier")))
    (build-system r-build-system)
    (propagated-inputs (list r-sgeostat r-robustbase))
    (home-page "http://cstat.tuwien.ac.at/filz/")
    (synopsis "Multivariate Outlier Detection Based on Robust Methods")
    (description
     "Various methods for multivariate outlier detection: arw, a Mahalanobis-type
method with an adaptive outlier cutoff value; locout, a method incorporating
local neighborhood; pcout, a method for high-dimensional data;
mvoutlier.@code{CoDa}, a method for compositional data.  References are provided
in the corresponding help files.")
    (license license:gpl3+)))

(define-public r-mvord
  (package
    (name "r-mvord")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvord" version))
       (sha256
        (base32 "08b4q7rbfq1b1ll2xlw1d28m744xng5b6b0xglxvhrxxm1wj3xac"))))
    (properties `((upstream-name . "mvord")))
    (build-system r-build-system)
    (propagated-inputs (list r-ucminf
                             r-pbivnorm
                             r-optimx
                             r-numderiv
                             r-mvtnorm
                             r-mnormt
                             r-minqa
                             r-matrix
                             r-mass
                             r-dfoptim
                             r-bb))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://github.com/lauravana/mvord")
    (synopsis "Multivariate Ordinal Regression Models")
    (description
     "This package provides a flexible framework for fitting multivariate ordinal
regression models with composite likelihood methods.  Methodological details are
given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.")
    (license license:gpl3)))

(define-public r-mvntestchar
  (package
    (name "r-mvntestchar")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVNtestchar" version))
       (sha256
        (base32 "0w5008zknrl5r7l4dmj2fbm7vvg2f1wynzf38kabns4j7hb1nmxw"))))
    (properties `((upstream-name . "MVNtestchar")))
    (build-system r-build-system)
    (propagated-inputs (list r-knitr r-hmisc r-ggplot2))
    (native-inputs (list r-markdown r-knitr))
    (home-page "https://cran.r-project.org/package=MVNtestchar")
    (synopsis
     "Test for Multivariate Normal Distribution Based on a Characterization")
    (description
     "This package provides a test of multivariate normality of an unknown sample that
does not require estimation of the nuisance parameters, the mean and covariance
matrix.  Rather, a sequence of transformations removes these nuisance parameters
and results in a set of sample matrices that are positive definite.  These
matrices are uniformly distributed on the space of positive definite matrices in
the unit hyper-rectangle if and only if the original data is multivariate normal
(Fairweather, 1973, Doctoral dissertation, University of Washington).  The
package performs a goodness of fit test of this hypothesis.  In addition to the
test, functions in the package give visualizations of the support region of
positive definite matrices for bivariate samples.")
    (license license:gpl2+)))

(define-public r-mvntest
  (package
    (name "r-mvntest")
    (version "1.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvnTest" version))
       (sha256
        (base32 "0p37skzpljzz19x1hwh2hbxqins19zkbz7nlgfws5pgp9apazafq"))))
    (properties `((upstream-name . "mvnTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=mvnTest")
    (synopsis "Goodness of Fit Tests for Multivariate Normality")
    (description
     "Routines for assessing multivariate normality.  Implements three Wald's type
chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests;
Doornik-Hansen test, Royston test and Henze-Zirkler test.")
    (license license:gpl2+)))

(define-public r-mvnpermute
  (package
    (name "r-mvnpermute")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvnpermute" version))
       (sha256
        (base32 "0jszhq2mfqm7k7zvmn1m286gnafpng6ybmvmwzmzzwla9f7sacjx"))))
    (properties `((upstream-name . "mvnpermute")))
    (build-system r-build-system)
    (home-page "https://github.com/markabney/MVNpermute")
    (synopsis "Generate New Multivariate Normal Samples from Permutations")
    (description
     "Given a vector of multivariate normal data, a matrix of covariates and the data
covariance matrix, generate new multivariate normal samples that have the same
covariance matrix based on permutations of the transformed data residuals.")
    (license license:gpl3+)))

(define-public r-mvnormtest
  (package
    (name "r-mvnormtest")
    (version "0.1-9-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvnormtest" version))
       (sha256
        (base32 "1hxxy08ldm2dczjfb34q1xk8yfs54pdishq4hnws8xy0cjh4xngg"))))
    (properties `((upstream-name . "mvnormtest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mvnormtest")
    (synopsis "Normality Test for Multivariate Variables")
    (description
     "Generalization of Shapiro-Wilk test for multivariate variables.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mvnormaltest
  (package
    (name "r-mvnormaltest")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvnormalTest" version))
       (sha256
        (base32 "1hbapcmj1rzrdzlbkay94f3r2388sk60zizh48vbbkc83qlm7xbr"))))
    (properties `((upstream-name . "mvnormalTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-nortest r-moments r-copula))
    (home-page "https://cran.r-project.org/package=mvnormalTest")
    (synopsis "Powerful Tests for Multivariate Normality")
    (description
     "This package provides a simple informative powerful test (@code{mvnTest}()) for
multivariate normality proposed by Zhou and Shao (2014)
<doi:10.1080/02664763.2013.839637>, which combines kurtosis with Shapiro-Wilk
test that is easy for biomedical researchers to understand and easy to implement
in all dimensions.  This package also contains some other multivariate normality
tests including Fattorini's FA test (@code{faTest}()), Mardia's skewness and
kurtosis test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's
test (msk()), Roystonâs H test (msw()), and Villasenor-Alva and
Gonzalez-Estrada's test (msw()).  Empirical power calculation functions for
these tests are also provided.  In addition, this package includes some
functions to generate several types of multivariate distributions mentioned in
Zhou and Shao (2014).")
    (license license:gpl2+)))

(define-public r-mvnmle
  (package
    (name "r-mvnmle")
    (version "0.1-11.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvnmle" version))
       (sha256
        (base32 "06ysl7ff0gy5iiggaicrk1x3sav86iz45zks6ybd3k9516qjjn39"))))
    (properties `((upstream-name . "mvnmle")))
    (build-system r-build-system)
    (home-page "https://github.com/indenkun/mvnmle")
    (synopsis "ML Estimation for Multivariate Normal Data with Missing Values")
    (description
     "Finds the Maximum Likelihood (ML) Estimate of the mean vector and
variance-covariance matrix for multivariate normal data with missing values.")
    (license license:gpl2+)))

(define-public r-mvnimpute
  (package
    (name "r-mvnimpute")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvnimpute" version))
       (sha256
        (base32 "0131dm8ma7qrzqsfdkdcp1vh0jcz3am3mry6x96m8fm3npi4dldz"))))
    (properties `((upstream-name . "mvnimpute")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-rlang
                             r-reshape2
                             r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-laplacesdemon
                             r-ggplot2))
    (home-page "https://github.com/hli226/mvnimpute")
    (synopsis "Simultaneously Impute the Missing and Censored Values")
    (description
     "Implementing a multiple imputation algorithm for multivariate data with missing
and censored values under a coarsening at random assumption (Heitjan and Rubin,
1991<doi:10.1214/aos/1176348396>).  The multiple imputation algorithm is based
on the data augmentation algorithm proposed by Tanner and Wong
(1987)<doi:10.1080/01621459.1987.10478458>.  The Gibbs sampling algorithm is
adopted to to update the model parameters and draw imputations of the coarse
data.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mvnggrad
  (package
    (name "r-mvnggrad")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvngGrAd" version))
       (sha256
        (base32 "17pzrppbl3ijkqmzlca321q3ngnjrkn0kn647gdfxmd84qkbcq8s"))))
    (properties `((upstream-name . "mvngGrAd")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mvngGrAd")
    (synopsis "Moving Grid Adjustment in Plant Breeding Field Trials")
    (description
     "Package for moving grid adjustment in plant breeding field trials.")
    (license license:gpl2+)))

(define-public r-mvnbayesian
  (package
    (name "r-mvnbayesian")
    (version "0.0.8-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVNBayesian" version))
       (sha256
        (base32 "1iaxyp480v91887cn4w3l74907wvbzs3ay4cjgzd8xk2dp47w8bg"))))
    (properties `((upstream-name . "MVNBayesian")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-mvtnorm))
    (home-page "https://github.com/CubicZebra/MVNBayesian")
    (synopsis "Bayesian Analysis Framework for MVN (Mixture) Distribution")
    (description
     "This package provides tools of Bayesian analysis framework using the method
suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for multivariate
normal (MVN) distribution and multivariate normal mixture (@code{MixMVN})
distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b)
generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo
(MCMC) for (Mix)MVN distribution.")
    (license license:gpl2)))

(define-public r-mvna
  (package
    (name "r-mvna")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvna" version))
       (sha256
        (base32 "10i42hjm5bk62c20pmxhya283yzw1902ivkflmyqy79jri266dxf"))))
    (properties `((upstream-name . "mvna")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (home-page "https://cran.r-project.org/package=mvna")
    (synopsis
     "Nelson-Aalen Estimator of the Cumulative Hazard in Multistate Models")
    (description
     "Computes the Nelson-Aalen estimator of the cumulative transition hazard for
arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.")
    (license license:expat)))

(define-public r-mvn
  (package
    (name "r-mvn")
    (version "5.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVN" version))
       (sha256
        (base32 "014s07nxq014j5hkbd6iwwqykcw8si5sah57gi3n0wb51sv8ip1k"))))
    (properties `((upstream-name . "MVN")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych
                             r-plyr
                             r-nortest
                             r-moments
                             r-mass
                             r-energy
                             r-car
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MVN")
    (synopsis "Multivariate Normality Tests")
    (description
     "This package performs multivariate normality tests and graphical approaches and
implements multivariate outlier detection and univariate normality of marginal
distributions through plots and tests, and performs multivariate Box-Cox
transformation (Korkmaz et al, (2014),
<https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).")
    (license license:gpl2+)))

(define-public r-mvmorph
  (package
    (name "r-mvmorph")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvMORPH" version))
       (sha256
        (base32 "1ga9ngbanavlix5jhr32924lz45mxwdinx0vrbxh7b594iwb385y"))))
    (properties `((upstream-name . "mvMORPH")))
    (build-system r-build-system)
    (propagated-inputs (list r-subplex
                             r-spam
                             r-phytools
                             r-pbmcapply
                             r-glassofast
                             r-corpcor
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JClavel/mvMORPH")
    (synopsis
     "Multivariate Comparative Tools for Fitting Evolutionary Models to Morphometric Data")
    (description
     "Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and
Shifts) models of continuous traits evolution on trees and time series.
@code{mvMORPH} also proposes high-dimensional multivariate comparative tools
(linear models using Generalized Least Squares and multivariate tests) based on
penalized likelihood.  See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>,
Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020)
<DOI:10.1093/sysbio/syaa010>.")
    (license license:gpl2+)))

(define-public r-mvmonitoring
  (package
    (name "r-mvmonitoring")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvMonitoring" version))
       (sha256
        (base32 "1s8djfn89qdyph3gahh5gsf1pdyn4k07p2a1rs9ajzjqv9dznc4p"))))
    (properties `((upstream-name . "mvMonitoring")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-robustbase
                             r-rlang
                             r-plyr
                             r-lazyeval
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gabrielodom/mvMonitoring")
    (synopsis
     "Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring")
    (description
     "Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data
generated from a continuous-time multivariate industrial or natural process.
Employ PCA-based dimension reduction to extract linear combinations of relevant
features, reducing computational burdens.  For a description of ADPCA, see
<doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.  The
multi-state application of ADPCA is from a manuscript under current revision
entitled \"Multi-State Multivariate Statistical Process Control\" by Odom,
Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018.")
    (license license:gpl2)))

(define-public r-mvmise
  (package
    (name "r-mvmise")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvMISE" version))
       (sha256
        (base32 "0lnc5g3ksl5cvwylh8p8baxyvq3z32a2i337q7lhg00j031ccy5c"))))
    (properties `((upstream-name . "mvMISE")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lme4))
    (home-page "https://github.com/randel/mvMISE")
    (synopsis
     "General Framework of Multivariate Mixed-Effects Selection Models")
    (description
     "Offers a general framework of multivariate mixed-effects models for the joint
analysis of multiple correlated outcomes with clustered data structures and
potential missingness proposed by Wang et al. (2018)
<doi:10.1093/biostatistics/kxy022>.  The missingness of outcome values may
depend on the values themselves (missing not at random and non-ignorable), or
may depend on only the covariates (missing at random and ignorable), or both.
This package provides functions for two models: 1) @code{mvMISE_b}() allows
correlated outcome-specific random intercepts with a factor-analytic structure,
and 2) @code{mvMISE_e}() allows the correlated outcome-specific error terms with
a graphical lasso penalty on the error precision matrix.  Both functions are
motivated by the multivariate data analysis on data with clustered structures
from labelling-based quantitative proteomic studies.  These models and functions
can also be applied to univariate and multivariate analyses of clustered data
with balanced or unbalanced design and no missingness.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mvmeta
  (package
    (name "r-mvmeta")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvmeta" version))
       (sha256
        (base32 "08zki4bxd7l5hpa9dly9jw796kn4rfv6v6gmx21yvfppyryrvmvd"))))
    (properties `((upstream-name . "mvmeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-mixmeta))
    (home-page "http://www.ag-myresearch.com/package-mvmeta")
    (synopsis "Multivariate and Univariate Meta-Analysis and Meta-Regression")
    (description
     "Collection of functions to perform fixed and random-effects multivariate and
univariate meta-analysis and meta-regression.")
    (license license:gpl2+)))

(define-public r-mvmesh
  (package
    (name "r-mvmesh")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvmesh" version))
       (sha256
        (base32 "1y6p7xp9fv0xswjdc6py1xqlx1ys1iyr559s9wkyja4hzcpya2my"))))
    (properties `((upstream-name . "mvmesh")))
    (build-system r-build-system)
    (propagated-inputs (list r-simplicialcubature r-rgl r-rcdd r-geometry
                             r-abind))
    (home-page "https://cran.r-project.org/package=mvmesh")
    (synopsis "Multivariate Meshes and Histograms in Arbitrary Dimensions")
    (description
     "Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and
tubes.  Directional and other multivariate histograms are provided.")
    (license license:gpl3+)))

(define-public r-mvmapit
  (package
    (name "r-mvmapit")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvMAPIT" version))
       (sha256
        (base32 "1ab4snxbw9p80cs7d6fnxl7cv20l1h2b243q2nj50yh6xirx2a0h"))))
    (properties `((upstream-name . "mvMAPIT")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-testthat
                             r-rcppspdlog
                             r-rcppprogress
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-logging
                             r-harmonicmeanp
                             r-foreach
                             r-dplyr
                             r-compquadform
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lcrawlab/mvMAPIT")
    (synopsis "Multivariate Genome Wide Marginal Epistasis Test")
    (description
     "Epistasis, commonly defined as the interaction between genetic loci, is known to
play an important role in the phenotypic variation of complex traits.  As a
result, many statistical methods have been developed to identify genetic
variants that are involved in epistasis, and nearly all of these approaches
carry out this task by focusing on analyzing one trait at a time.  Previous
studies have shown that jointly modeling multiple phenotypes can often
dramatically increase statistical power for association mapping.  In this
package, we present the multivariate MArginal @code{ePIstasis} Test
('@code{mvMAPIT}') â a multi-outcome generalization of a recently proposed
epistatic detection method which seeks to detect marginal epistasis or the
combined pairwise interaction effects between a given variant and all other
variants.  By searching for marginal epistatic effects, one can identify genetic
variants that are involved in epistasis without the need to identify the exact
partners with which the variants interact â thus, potentially alleviating much
of the statistical and computational burden associated with conventional
explicit search based methods.  Our proposed @code{mvMAPIT} builds upon this
strategy by taking advantage of correlation structure between traits to improve
the identification of variants involved in epistasis.  We formulate
@code{mvMAPIT} as a multivariate linear mixed model and develop a multi-trait
variance component estimation algorithm for efficient parameter inference and
P-value computation.  Together with reasonable model approximations, our
proposed approach is scalable to moderately sized genome-wide association
studies.  Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>.  Stamp et
al. (2023) <doi:10.1093/g3journal/jkad118>.")
    (license license:gpl3+)))

(define-public r-mvlswimpute
  (package
    (name "r-mvlswimpute")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvLSWimpute" version))
       (sha256
        (base32 "1caf843cf139f3fiy3nsq6g83xfz70c20zlfwk4pg5jv4rh0jiyg"))))
    (properties `((upstream-name . "mvLSWimpute")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-wavethresh
                             r-mvlsw
                             r-imputets
                             r-binhf))
    (home-page "https://cran.r-project.org/package=mvLSWimpute")
    (synopsis
     "Imputation Methods for Multivariate Locally Stationary Time Series")
    (description
     "Implementation of imputation techniques based on locally stationary wavelet time
series forecasting methods from Wilson, R. E. et al. (2021)
<doi:10.1007/s11222-021-09998-2>.")
    (license license:gpl2)))

(define-public r-mvlsw
  (package
    (name "r-mvlsw")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvLSW" version))
       (sha256
        (base32 "0qg6bn0din22fns0cb4m0dfzgg0slrkfimpx7fslnvncfmx36h66"))))
    (properties `((upstream-name . "mvLSW")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts r-wavethresh r-fields))
    (home-page "https://cran.r-project.org/package=mvLSW")
    (synopsis "Multivariate, Locally Stationary Wavelet Process Estimation")
    (description
     "This package provides tools for analysing multivariate time series with
wavelets.  This includes: simulation of a multivariate locally stationary
wavelet (@code{mvLSW}) process from a multivariate evolutionary wavelet spectrum
(@code{mvEWS}); estimation of the @code{mvEWS}, local coherence and local
partial coherence.  See Park, Eckley and Ombao (2014)
<doi:10.1109/TSP.2014.2343937> for details.")
    (license license:gpl3+)))

(define-public r-mvlm
  (package
    (name "r-mvlm")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVLM" version))
       (sha256
        (base32 "1zcj405dc4jbiqw6p0fcbam8yc9d6yjpmrx5wjw5zjvig3iqb91k"))))
    (properties `((upstream-name . "MVLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/dmcartor/MVLM")
    (synopsis "Multivariate Linear Model with Analytic p-Values")
    (description
     "Allows a user to conduct multivariate multiple regression using analytic
p-values rather than classic approximate F-tests.")
    (license license:gpl2+)))

(define-public r-mvisage
  (package
    (name "r-mvisage")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVisAGe" version))
       (sha256
        (base32 "1plrzzj7cr2hxk1npadvjnk7sanhilh99l5vrkjjh3l6li2ym09b"))))
    (properties `((upstream-name . "MVisAGe")))
    (build-system r-build-system)
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=MVisAGe")
    (synopsis "Compute and Visualize Bivariate Associations")
    (description
     "Pearson and Spearman correlation coefficients are commonly used to quantify the
strength of bivariate associations of genomic variables.  For example,
correlations of gene-level DNA copy number and gene expression measurements may
be used to assess the impact of DNA copy number changes on gene expression in
tumor tissue.  M@code{VisAGe} enables users to quickly compute and visualize the
correlations in order to assess the effect of regional genomic events such as
changes in DNA copy number or DNA methylation level.  Please see Walter V, Du Y,
Danilova L, Hayward MC, Hayes DN, 2018.  Cancer Research
<doi:10.1158/0008-5472.CAN-17-3464>.")
    (license license:gpl3)))

(define-public r-mvinfluence
  (package
    (name "r-mvinfluence")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvinfluence" version))
       (sha256
        (base32 "0yzp8sybmmr2nfa0g0v14kb2fqa5ayi0awlrb05vbyxvr5pg4f8h"))))
    (properties `((upstream-name . "mvinfluence")))
    (build-system r-build-system)
    (propagated-inputs (list r-heplots r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/friendly/mvinfluence")
    (synopsis
     "Influence Measures and Diagnostic Plots for Multivariate Linear Models")
    (description
     "Computes regression deletion diagnostics for multivariate linear models and
provides some associated diagnostic plots.  The diagnostic measures include
hat-values (leverages), generalized Cook's distance, and generalized squared
studentized residuals.  Several types of plots to detect influential
observations are provided.")
    (license license:gpl2)))

(define-public r-mvhtests
  (package
    (name "r-mvhtests")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvhtests" version))
       (sha256
        (base32 "1820d8gd2rmszqp3kd5d01c5v2znxm1l6dm7cjr4mn51v1rxzhhk"))))
    (properties `((upstream-name . "mvhtests")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast2 r-rfast r-foreach r-emplik r-doparallel))
    (home-page "https://cran.r-project.org/package=mvhtests")
    (synopsis "Multivariate Hypothesis Tests")
    (description
     "Hypothesis tests for multivariate data.  Tests for one and two mean vectors,
multivariate analysis of variance, tests for one, two or more covariance
matrices.  References include: Mardia K.V., Kent J.T. and Bibby J.M. (1979).
Multivariate Analysis.  ISBN: 978-0124712522.  London: Academic Press.")
    (license license:gpl2+)))

(define-public r-mvhist
  (package
    (name "r-mvhist")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvhist" version))
       (sha256
        (base32 "057yhkivcfjm9yazx0x8rn2xysgyc6y7nlcxpjl00yfi7wcjpmlh"))))
    (properties `((upstream-name . "mvhist")))
    (build-system r-build-system)
    (propagated-inputs (list r-simplicialcubature r-rgl r-rcdd r-mvmesh))
    (home-page "https://cran.r-project.org/package=mvhist")
    (synopsis "Multivariate Histograms")
    (description
     "Tabulate and plot directional and other multivariate histograms.")
    (license license:gpl3+)))

(define-public r-mvgps
  (package
    (name "r-mvgps")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvGPS" version))
       (sha256
        (base32 "1aanrv84c012ijyqql0pw2qlckrpv3wmfv16yhr465xb3rkzz4vj"))))
    (properties `((upstream-name . "mvGPS")))
    (build-system r-build-system)
    (propagated-inputs (list r-weightit
                             r-sp
                             r-rdpack
                             r-matrixnormal
                             r-mass
                             r-geometry
                             r-gbm
                             r-cobalt
                             r-cbps))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/williazo/mvGPS")
    (synopsis
     "Causal Inference using Multivariate Generalized Propensity Score")
    (description
     "This package provides methods for estimating and utilizing the multivariate
generalized propensity score (@code{mvGPS}) for multiple continuous exposures
described in Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>.  The
methods allow estimation of a dose-response surface relating the joint
distribution of multiple continuous exposure variables to an outcome.  Weights
are constructed assuming a multivariate normal density for the marginal and
conditional distribution of exposures given a set of confounders.  Confounders
can be different for different exposure variables.  The weights are designed to
achieve balance across all exposure dimensions and can be used to estimate
dose-response surfaces.")
    (license license:expat)))

(define-public r-mvglmmrank
  (package
    (name "r-mvglmmrank")
    (version "1.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvglmmRank" version))
       (sha256
        (base32 "09mkjx3xrpnv681d07z91g60fq7bna6142b2hh9xvh5nxcqixdnf"))))
    (properties `((upstream-name . "mvglmmRank")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=mvglmmRank")
    (synopsis
     "Multivariate Generalized Linear Mixed Models for Ranking Sports Teams")
    (description
     "Maximum likelihood estimates are obtained via an EM algorithm with either a
first-order or a fully exponential Laplace approximation as documented by
Broatch and Karl (2018) <doi:10.48550/@code{arXiv.1710.05284>}, Karl, Yang, and
Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by Karl (2012)
<doi:10.1515/1559-0410.1471>.  Karl and Zimmerman
<doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home
field effect estimator from a mixed model can be biased under nonrandom
scheduling.")
    (license license:gpl2)))

(define-public r-mvgb
  (package
    (name "r-mvgb")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvgb" version))
       (sha256
        (base32 "1qlbhs9lyzrgfiyqyhfnpwfzb2snjmbxv1rk5cg7h00hsdhdc890"))))
    (properties `((upstream-name . "mvgb")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://github.com/swihart/mvgb")
    (synopsis
     "Multivariate Probabilities of Scale Mixtures of Multivariate Normal Distributions via the Genz and Bretz (2002) QRSVN Method")
    (description
     "Generates multivariate subgaussian stable probabilities using the QRSVN
algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394> but by
sampling positive stable variates not chi/sqrt(nu).")
    (license license:lgpl2.1+)))

(define-public r-mvgam
  (package
    (name "r-mvgam")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvgam" version))
       (sha256
        (base32 "03nirq7wn3rp9lwkagvqcjrzihq0vmj8dijpn350j8cxvcdvbscl"))))
    (properties `((upstream-name . "mvgam")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-smooth
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-posterior
                             r-pbapply
                             r-mvnfast
                             r-mgcv
                             r-matrix
                             r-marginaleffects
                             r-magrittr
                             r-loo
                             r-insight
                             r-ggplot2
                             r-dplyr
                             r-brms
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nicholasjclark/mvgam")
    (synopsis "Multivariate (Dynamic) Generalized Additive Models")
    (description
     "Fit Bayesian Dynamic Generalized Additive Models to sets of time series.  Users
can build dynamic nonlinear State-Space models that can incorporate
semiparametric effects in observation and process components, using a wide range
of observation families.  Estimation is performed using Markov Chain Monte Carlo
with Hamiltonian Monte Carlo in the software Stan'.  References: Clark & Wells
(2022) <doi:10.1111/2041-210X.13974>.")
    (license license:expat)))

(define-public r-mvet
  (package
    (name "r-mvet")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVET" version))
       (sha256
        (base32 "0gmawz2vhyvbj5jg8s26cmf02dxyn6gqhvzik5g4kcwpfwkdfwfy"))))
    (properties `((upstream-name . "MVET")))
    (build-system r-build-system)
    (propagated-inputs (list r-gridextra r-ggplot2))
    (home-page "https://github.com/YeonSeok-Choi/MVET")
    (synopsis "Multivariate Estimates and Tests")
    (description
     "Multivariate estimation and testing, currently a package for testing parametric
data.  To deal with parametric data, various multivariate normality tests and
outlier detection are performed and visualized using the ggplot2 package.
Homogeneity tests for covariance matrices are also possible, as well as the
Hotelling's T-square test and the multivariate analysis of variance test.  We
are exploring additional tests and visualization techniques, such as profile
analysis and randomized complete block design, to be made available in the
future and making them easily accessible to users.")
    (license license:expat)))

(define-public r-mverse
  (package
    (name "r-mverse")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mverse" version))
       (sha256
        (base32 "1qj77s98j05j8d1k1w3h56c7z2i1j973mflhklxhb3xxc7plyk3y"))))
    (properties `((upstream-name . "mverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-multiverse
                             r-magrittr
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-dplyr
                             r-cowplot
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mverseanalysis/mverse/")
    (synopsis "Tidy Multiverse Analysis Made Simple")
    (description
     "Extends multiverse package (Sarma A., Kale A., Moon M., Taback N., Chevalier F.,
Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>, which allows users
perform to create explorable multiverse analysis in R. This extension provides
an additional level of abstraction to the multiverse package with the aim of
creating user friendly syntax to researchers, educators, and students in
statistics.  The mverse syntax is designed to allow piping and takes hints from
the tidyverse grammar.  The package allows users to define and inspect
multiverse analysis using familiar syntax in R.")
    (license license:gpl3+)))

(define-public r-mvdfa
  (package
    (name "r-mvdfa")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvDFA" version))
       (sha256
        (base32 "118m08j6d8vwpk04g6prqmcxc899v2a8mbs59j53cs612aipk0vp"))))
    (properties `((upstream-name . "mvDFA")))
    (build-system r-build-system)
    (propagated-inputs (list r-robper
                             r-pracma
                             r-pbapply
                             r-mvtnorm
                             r-longmemo
                             r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jpirmer/mvDFA")
    (synopsis "Multivariate Detrended Fluctuation Analysis")
    (description
     "This R package provides an implementation of multivariate extensions of a
well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA;
Peng et al., 1995<doi:10.1063/1.166141>), for multivariate time series:
multivariate DFA (@code{mvDFA}).  Several coefficients are implemented that take
into account the correlation structure of the multivariate time series to
varying degrees.  These coefficients may be used to analyze long memory and
changes in the dynamic structure that would by univariate DFA. Therefore, this R
package aims to extend and complement the original univariate DFA (Peng et al.,
1995) for estimating the scaling properties of nonstationary time series.")
    (license license:gpl3)))

(define-public r-mvdalab
  (package
    (name "r-mvdalab")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvdalab" version))
       (sha256
        (base32 "0iblpg5yi9ig4hi6ff84m9pfgyrpkyfc9m9m98dpngpbhxfqnvhq"))))
    (properties `((upstream-name . "mvdalab")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn
                             r-reshape2
                             r-plyr
                             r-penalized
                             r-moments
                             r-mass
                             r-ggplot2
                             r-car))
    (home-page "https://cran.r-project.org/package=mvdalab")
    (synopsis "Multivariate Data Analysis Laboratory")
    (description
     "An open-source implementation of latent variable methods and multivariate
modeling tools.  The focus is on exploratory analyses using dimensionality
reduction methods including low dimensional embedding, classical multivariate
statistical tools, and tools for enhanced interpretation of machine learning
methods (i.e.  intelligible models to provide important information for
end-users).  Target domains include extension to dedicated applications e.g. for
manufacturing process modeling, spectroscopic analyses, and data mining.")
    (license license:gpl3)))

(define-public r-mvctm
  (package
    (name "r-mvctm")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvctm" version))
       (sha256
        (base32 "0810bmk748cvbls4djspqvkk65j605djqicqg4rkwy2a799l3rgc"))))
    (properties `((upstream-name . "mvctm")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatialnp
                             r-rfit
                             r-quantreg
                             r-nlme
                             r-mnm
                             r-formula))
    (home-page "https://cran.r-project.org/package=mvctm")
    (synopsis "Multivariate Variance Components Tests for Multilevel Data")
    (description
     "Permutation tests for variance components for 2-level, 3-level and 4-level data
with univariate or multivariate responses.")
    (license license:gpl3)))

(define-public r-mvcor
  (package
    (name "r-mvcor")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvcor" version))
       (sha256
        (base32 "0i9g2h05cwhkiaryzb2ghr1ix3i27bpxmvnay5vgrc36k5dmllyr"))))
    (properties `((upstream-name . "mvcor")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast))
    (home-page "https://cran.r-project.org/package=mvcor")
    (synopsis "Correlation Coefficients for Multivariate Data")
    (description
     "Correlation coefficients for multivariate data, namely the squared correlation
coefficient and the RV coefficient (multivariate generalization of the squared
Pearson correlation coefficient).  References include Mardia K.V., Kent J.T. and
Bibby J.M. (1979). \"Multivariate Analysis\".  ISBN: 978-0124712522.  London:
Academic Press.")
    (license license:gpl2+)))

(define-public r-mvcauchy
  (package
    (name "r-mvcauchy")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvcauchy" version))
       (sha256
        (base32 "0vnf5q71hkj6nqljm73rcmvr7ir6zhp4qwrjvi49d6zxpdrs8yx1"))))
    (properties `((upstream-name . "mvcauchy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast2 r-rfast))
    (home-page "https://cran.r-project.org/package=mvcauchy")
    (synopsis "Multivariate Cauchy Distribution")
    (description
     "The Cauchy distribution is a special case of the t distribution when the degrees
of freedom are equal to 1.  The functions are related to the multivariate Cauchy
distribution and include simulation, computation of the density, maximum
likelihood estimation, contour plot of the bivariate Cauchy distribution, and
discriminant analysis.  References include: Nadarajah S. and Kotz S. (2008).
\"Estimation methods for the multivariate t distribution\".  Acta Applicandae
Mathematicae, 102(1): 99--118. <doi:10.1007/s10440-008-9212-8>, and Kanti V.
Mardia, John T. Kent and John M. Bibby (1979). \"Multivariate analysis\",
ISBN:978-0124712522.  Academic Press, London.")
    (license license:gpl2+)))

(define-public r-mvbutils
  (package
    (name "r-mvbutils")
    (version "2.8.232")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mvbutils" version))
       (sha256
        (base32 "0awd0jy492ha321c20kmmgl20kqphdmrmswakc0rq6h8z9d55kdg"))))
    (properties `((upstream-name . "mvbutils")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mvbutils")
    (synopsis
     "Workspace Organization, Code and Documentation Editing, Package Prep and Editing, Etc")
    (description
     "Hierarchical workspace tree, code editing and backup, easy package prep, editing
of packages while loaded, per-object lazy-loading, easy documentation, macro
functions, and miscellaneous utilities.  Needed by debug package.")
    (license license:gpl2+)))

(define-public r-mvbinary
  (package
    (name "r-mvbinary")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MvBinary" version))
       (sha256
        (base32 "1lrrxvphxv4gmlazx67q4bck9373nqylqgkwcjzwb52291a3zyiv"))))
    (properties `((upstream-name . "MvBinary")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv))
    (home-page "https://cran.r-project.org/package=MvBinary")
    (synopsis
     "Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution")
    (description
     "Modelling Multivariate Binary Data with Blocks of Specific One-Factor
Distribution.  Variables are grouped into independent blocks.  Each variable is
described by two continuous parameters (its marginal probability and its
dependency strength with the other block variables), and one binary parameter
(positive or negative dependency).  Model selection consists in the estimation
of the repartition of the variables into blocks.  It is carried out by the
maximization of the BIC criterion by a deterministic (faster) algorithm or by a
stochastic (more time consuming but optimal) algorithm.  Tool functions
facilitate the model interpretation.")
    (license license:gpl2+)))

(define-public r-mvar-pt
  (package
    (name "r-mvar-pt")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVar.pt" version))
       (sha256
        (base32 "0095jhb5zfl4nix9n26mb1xdrc4fgi8mvn1l3z4m90mzrk39vsk2"))))
    (properties `((upstream-name . "MVar.pt")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MVar.pt")
    (synopsis "Analise multivariada (brazilian portuguese)")
    (description
     "Analise multivariada, tendo funcoes que executam analise de correspondencia
simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise
de correlacao canonica (CCA), analise fatorial (FA), escalonamento
multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA),
analise de cluster hierarquico e nao hierarquico, regressao linear simples e
multipla, analise de multiplos fatores (MFA) para dados quantitativos,
qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot,
projection pursuit (PP), grant tour e outras funcoes uteis para a analise
multivariada.")
    (license license:gpl3)))

(define-public r-mvar
  (package
    (name "r-mvar")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVar" version))
       (sha256
        (base32 "1d1gh8wxwy3pddamhfgiy1l89swi8i9mi5i782cwd3gi2q6w6jf3"))))
    (properties `((upstream-name . "MVar")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MVar")
    (synopsis "Multivariate Analysis")
    (description
     "Multivariate analysis, having functions that perform simple correspondence
analysis (CA) and multiple correspondence analysis (MCA), principal components
analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA),
multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis
(QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple
linear regression, multiple factor analysis (MFA) for quantitative, qualitative,
frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP),
grant tour method and other useful functions for the multivariate analysis.")
    (license license:gpl3)))

(define-public r-mva
  (package
    (name "r-mva")
    (version "1.0-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MVA" version))
       (sha256
        (base32 "1xd75qah05jrljl72gr8xzhfj43mnf821jnjv38gflavkvyn89mv"))))
    (properties `((upstream-name . "MVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-hsaur2))
    (home-page "http://dx.doi.org/10.1007/978-1-4419-9650-3")
    (synopsis "An Introduction to Applied Multivariate Analysis with R")
    (description
     "Functions, data sets, analyses and examples from the book `An Introduction to
Applied Multivariate Analysis with R (Brian S. Everitt and Torsten Hothorn,
Springer, 2011).")
    (license license:gpl2)))

(define-public r-muvr2
  (package
    (name "r-muvr2")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MUVR2" version))
       (sha256
        (base32 "1w5y8vc0bxbgrn276f7yvjkq27nmlwpxnyswmqiqp87wqwjq2ni4"))))
    (properties `((upstream-name . "MUVR2")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger
                             r-randomforest
                             r-psych
                             r-proc
                             r-mgcv
                             r-magrittr
                             r-glmnet
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-caret))
    (home-page "https://github.com/MetaboComp/MUVR2")
    (synopsis "Multivariate Methods with Unbiased Variable Selection")
    (description
     "Predictive multivariate modelling for metabolomics.  Types: Classification and
regression.  Methods: Partial Least Squares, Random Forest ans Elastic Net Data
structures: Paired and unpaired Validation: repeated double cross-validation
(Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al.
(2009)<doi:10.1002/cem.1225>) Variable selection: Performed internally, through
tuning in the inner cross-validation loop.")
    (license license:gpl3)))

(define-public r-muvicp
  (package
    (name "r-muvicp")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MuViCP" version))
       (sha256
        (base32 "1wkiwdz4bblxf1zr57khljqkga9ks9aj1lnidvmhib94q6b8fnf4"))))
    (properties `((upstream-name . "MuViCP")))
    (build-system r-build-system)
    (propagated-inputs (list r-sm r-mass r-gtools))
    (home-page "https://cran.r-project.org/package=MuViCP")
    (synopsis
     "MultiClass Visualizable Classification using Combination of Projections")
    (description
     "An ensemble classifier for multiclass classification.  This is a novel
classifier that natively works as an ensemble.  It projects data on a large
number of matrices, and uses very simple classifiers on each of these
projections.  The results are then combined, ideally via Dempster-Shafer
Calculus.")
    (license license:gpl3)))

(define-public r-mutualinf
  (package
    (name "r-mutualinf")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mutualinf" version))
       (sha256
        (base32 "1k878nvsgs05krzndabi7aghzq2fzf0ncqj2sxy4czx4gf0ii3zv"))))
    (properties `((upstream-name . "mutualinf")))
    (build-system r-build-system)
    (propagated-inputs (list r-runner r-data-table))
    (home-page "https://github.com/RafaelFuentealbaC/mutualinf")
    (synopsis "Computation and Decomposition of the Mutual Information Index")
    (description
     "The Mutual Information Index (M) introduced to social science literature by
Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup
segregation measure that is highly decomposable and that according to Frankel
and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo
(2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit
Decomposability and Strong Group Decomposability properties.  This package
allows computing and decomposing the total index value into its \"between\" and
\"within\" terms.  These last terms can also be decomposed into their
contributions, either by group or unit characteristics.  The factors that
produce each \"within\" term can also be displayed at the user's request.  The
results can be computed considering a variable or sets of variables that define
separate clusters.")
    (license license:gpl3)))

(define-public r-mutsignatures
  (package
    (name "r-mutsignatures")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mutSignatures" version))
       (sha256
        (base32 "0skw92633mwckkkgkncfv09wvnr910nf4wdbqz0a61cl7mxlsr25"))))
    (properties `((upstream-name . "mutSignatures")))
    (build-system r-build-system)
    (propagated-inputs (list r-proxy
                             r-pracma
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://www.data-pulse.com/dev_site/mutsignatures/")
    (synopsis
     "Decipher Mutational Signatures from Somatic Mutational Catalogs")
    (description
     "Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair
processes.  This computational framework is aimed at deciphering DNA mutational
signatures operating in cancer.  The framework includes modules that support raw
data import and processing, mutational signature extraction, and results
interpretation and visualization.  The framework accepts widely used file
formats storing information about DNA variants, such as Variant Call Format
files.  The framework performs Non-Negative Matrix Factorization to extract
mutational signatures explaining the observed set of DNA mutations.
Bootstrapping is performed as part of the analysis.  The framework supports
parallelization and is optimized for use on multi-core systems.  The software
was described by Fantini D et al (2020) <doi:10.1038/s41598-020-75062-0> and is
based on a custom R-based implementation of the original MATLAB WTSI framework
by Alexandrov LB et al (2013) <doi:10.1016/j.celrep.2012.12.008>.")
    (license license:gpl2)))

(define-public r-mutossgui
  (package
    (name "r-mutossgui")
    (version "0.1-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mutossGUI" version))
       (sha256
        (base32 "13mq7yjbvnfgmiss95wxys3scmrb6b036g8rcxs1rn1q5wy3sg7b"))))
    (properties `((upstream-name . "mutossGUI")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava
                             r-plotrix
                             r-mutoss
                             r-multcomp
                             r-jgr
                             r-javagd
                             r-commonjavajars))
    (home-page "http://mutoss.r-forge.r-project.org/")
    (synopsis "Graphical User Interface for the MuToss Project")
    (description
     "This package provides a graphical user interface for the @code{MuToss} Project.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mutationtypes
  (package
    (name "r-mutationtypes")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mutationtypes" version))
       (sha256
        (base32 "0yyhx9a1vr2kv38m6spxam39gpysi7a2manil0x1zlzw0fp9x8hw"))))
    (properties `((upstream-name . "mutationtypes")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-cli r-assertions))
    (home-page "https://github.com/selkamand/mutationtypes")
    (synopsis
     "Validate and Convert Mutational Impacts Using Standard Genomic Dictionaries")
    (description
     "Check concordance of a vector of mutation impacts with standard dictionaries
such as Sequence Ontology (SO) <http://www.sequenceontology.org/>, Mutation
Annotation Format (MAF)
<https://docs.gdc.cancer.gov/Encyclopedia/pages/Mutation_Annotation_Format_TCGAv2/>
or Prediction and Annotation of Variant Effects (PAVE)
<https://github.com/hartwigmedical/hmftools/tree/master/pave>.  It enables
conversion between SO/PAVE and MAF terms and selection of the most severe
consequence where multiple ampersand (&) delimited impacts are given.")
    (license license:lgpl3+)))

(define-public r-musicnmr
  (package
    (name "r-musicnmr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "musicNMR" version))
       (sha256
        (base32 "14p382bcgpr7qbjpnpf8z173vbjq9svq8fifmffqrcxddvyfa6bx"))))
    (properties `((upstream-name . "musicNMR")))
    (build-system r-build-system)
    (propagated-inputs (list r-seewave))
    (home-page "https://cran.r-project.org/package=musicNMR")
    (synopsis
     "Conversion of Nuclear Magnetic Resonance Spectra in Audio Files")
    (description
     "This package provides a collection of functions for converting and visualization
the free induction decay of mono dimensional nuclear magnetic resonance (NMR)
spectra into an audio file.  It facilitates the conversion of Bruker datasets in
files WAV. The sound of NMR signals could provide an alternative to the current
representation of the individual metabolic fingerprint and supply equally
significant information.  The package includes also NMR spectra of the urine
samples provided by four healthy donors.  Based on Cacciatore S, Saccenti E,
Piccioli M. Hypothesis: the sound of the individual metabolic phenotype?
Acoustic detection of NMR experiments.  OMICS. 2015;19(3):147-56.
<doi:10.1089/omi.2014.0131>.")
    (license license:gpl2+)))

(define-public r-musica
  (package
    (name "r-musica")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "musica" version))
       (sha256
        (base32 "0cfzfar706l0xdb0n11m18mayj5rrplvf4qry36vgxsiaxcba90r"))))
    (properties `((upstream-name . "musica")))
    (build-system r-build-system)
    (propagated-inputs (list r-qmap r-magrittr r-lubridate r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=musica")
    (synopsis "Multiscale Climate Model Assessment")
    (description
     "This package provides functions allowing for (1) easy aggregation of
multivariate time series into custom time scales, (2) comparison of statistical
summaries between different data sets at multiple time scales (e.g. observed and
bias-corrected data), (3) comparison of relations between variables and/or
different data sets at multiple time scales (e.g. correlation of precipitation
and temperature in control and scenario simulation) and (4) transformation of
time series at custom time scales.")
    (license license:gpl2)))

(define-public r-music
  (package
    (name "r-music")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "music" version))
       (sha256
        (base32 "0fvj51h10pn5nbpv59z32jmfmm9xc76rg5rs3fiwh737q1fq5481"))))
    (properties `((upstream-name . "music")))
    (build-system r-build-system)
    (propagated-inputs (list r-crayon r-audio))
    (home-page "https://github.com/egenn/music")
    (synopsis "Learn and Experiment with Music Theory")
    (description
     "An aid for learning and using music theory.  You can build chords, scales, and
chord progressions using 12-note equal temperament tuning (12-ET) or
user-defined tuning.  Includes functions to visualize notes on a piano using
ASCII plots in the console and to plot waveforms using base graphics.  It allows
simple playback of notes and chords using the audio package.")
    (license license:gpl3+)))

(define-public r-musclesynergies
  (package
    (name "r-musclesynergies")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "musclesyneRgies" version))
       (sha256
        (base32 "1d99icy9hal9q716wpcl1xz2qjhqpshnxp761yqln0wina0241rj"))))
    (properties `((upstream-name . "musclesyneRgies")))
    (build-system r-build-system)
    (propagated-inputs (list r-umap
                             r-signal
                             r-reshape2
                             r-proxy
                             r-plyr
                             r-gridextra
                             r-ggplot2
                             r-fnn))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/alesantuz/musclesyneRgies")
    (synopsis "Extract Muscle Synergies from Electromyography")
    (description
     "This package provides a framework to factorise electromyography (EMG) data.
Tools are provided for raw data pre-processing, non negative matrix
factorisation, classification of factorised data and plotting of obtained
outcomes.  In particular, reading from ASCII files is supported, along with
wide-used filtering approaches to process EMG data.  All steps include one or
more sensible defaults that aim at simplifying the workflow.  Yet, all functions
are largely tunable at need.  Example data sets are included.")
    (license license:expat)))

(define-public r-mus
  (package
    (name "r-mus")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MUS" version))
       (sha256
        (base32 "0xid624rz0mvd03iy7ydvmx7gkscvsg4kdbaf2nji54ahai3pmn3"))))
    (properties `((upstream-name . "MUS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MUS")
    (synopsis
     "Monetary Unit Sampling and Estimation Methods, Widely Used in Auditing")
    (description
     "Sampling and evaluation methods to apply Monetary Unit Sampling (or in older
literature Dollar Unit Sampling) during an audit of financial statements.")
    (license license:gpl2+)))

(define-public r-murty
  (package
    (name "r-murty")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "muRty" version))
       (sha256
        (base32 "0v7wfb6347h4inf9jcbp6n4xfsqjzgj0i5sdfgqmbw0d5pcpnvhz"))))
    (properties `((upstream-name . "muRty")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve r-clue))
    (home-page "https://github.com/arg0naut91/muRty")
    (synopsis "Murty's Algorithm for k-Best Assignments")
    (description
     "Calculates k-best solutions and costs for an assignment problem following the
method outlined in Murty (1968) <doi:10.1287/opre.16.3.682>.")
    (license license:expat)))

(define-public r-murphydiagram
  (package
    (name "r-murphydiagram")
    (version "0.12.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "murphydiagram" version))
       (sha256
        (base32 "0f699cdfsl4f9w9f18cxkdlq1vyhsv83ynl6ck0r3wwh8f1zsmnd"))))
    (properties `((upstream-name . "murphydiagram")))
    (build-system r-build-system)
    (home-page "https://sites.google.com/site/fk83research/code")
    (synopsis "Murphy Diagrams for Forecast Comparisons")
    (description
     "Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles
and Expectiles: Consistent Scoring Functions, Choquet Representations, and
Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).")
    (license license:gpl3)))

(define-public r-murl
  (package
    (name "r-murl")
    (version "0.1-13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "muRL" version))
       (sha256
        (base32 "1fx9v5z5n83q4hiwsh359ffrcr0n07hym6cz4rjb4f51h96x893v"))))
    (properties `((upstream-name . "muRL")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-maps))
    (home-page "https://www.ryantmoore.org/software.murl.html")
    (synopsis "Mailmerge using R, LaTeX, and the Web")
    (description
     "This package provides mailmerge methods for reading spreadsheets of addresses
and other relevant information to create standardized but customizable letters.
Provides a method for mapping US ZIP codes, including those of letter
recipients.  Provides a method for parsing and processing html code from online
job postings of the American Political Science Association.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-munsellinterpol
  (package
    (name "r-munsellinterpol")
    (version "3.0-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "munsellinterpol" version))
       (sha256
        (base32 "15314dr42sikw8i1qdi5rw8r52whgxsda6m2jbldxdc67vlwmcwd"))))
    (properties `((upstream-name . "munsellinterpol")))
    (build-system r-build-system)
    (propagated-inputs (list r-spacesxyz r-spacesrgb r-rootsolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=munsellinterpol")
    (synopsis "Interpolate Munsell Renotation Data from Hue/Chroma to CIE/RGB")
    (description
     "This package provides methods for interpolating data in the Munsell color system
following the ASTM D-1535 standard.  Hues and chromas with decimal values can be
interpolated and converted to/from the Munsell color system and CIE @code{xyY},
CIE XYZ, CIE Lab, CIE Luv, or RGB. Includes ISCC-NBS color block lookup.  Based
on the work by Paul Centore, \"The Munsell and Kubelka-Munk Toolbox\".")
    (license license:gpl3+)))

(define-public r-munfold
  (package
    (name "r-munfold")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "munfold" version))
       (sha256
        (base32 "17zizx9r0f8dxb7dkgn1nn0fp7ydy6r155p1zfz0v93jc26lc1hb"))))
    (properties `((upstream-name . "munfold")))
    (build-system r-build-system)
    (propagated-inputs (list r-memisc r-mass))
    (home-page "http://www.elff.eu/software/munfold/")
    (synopsis "Metric Unfolding")
    (description
     "Multidimensional unfolding using Schoenemann's algorithm for metric and
Procrustes rotation of unfolding results.")
    (license license:gpl2)))

(define-public r-mulvariaterandomforestvarimp
  (package
    (name "r-mulvariaterandomforestvarimp")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MulvariateRandomForestVarImp" version))
       (sha256
        (base32 "0gbr44rwwb4nq2s4ix1ckfdjahsfx9sjfbww5wjcm1v68w6bk5ca"))))
    (properties `((upstream-name . "MulvariateRandomForestVarImp")))
    (build-system r-build-system)
    (propagated-inputs (list r-multivariaterandomforest r-mass))
    (home-page "https://github.com/Megatvini/VIM/")
    (synopsis "Variable Importance Measures for Multivariate Random Forests")
    (description
     "Calculates two sets of post-hoc variable importance measures for multivariate
random forests.  The first set of variable importance measures are given by the
sum of mean split improvements for splits defined by feature j measured on
user-defined examples (i.e., training or testing samples).  The second set of
importance measures are calculated on a per-outcome variable basis as the sum of
mean absolute difference of node values for each split defined by feature j
measured on user-defined examples (i.e., training or testing samples).  The user
can optionally threshold both sets of importance measures to include only splits
that are statistically significant as measured using an F-test.")
    (license license:gpl3+)))

(define-public r-multxpert
  (package
    (name "r-multxpert")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multxpert" version))
       (sha256
        (base32 "1lq5lgdjaaacipkjqxz6jxsm6b8zfbjsf7rs135a7n6w7qh35pvw"))))
    (properties `((upstream-name . "multxpert")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "http://multxpert.com/wiki/MultXpert_package")
    (synopsis "Common Multiple Testing Procedures and Gatekeeping Procedures")
    (description
     "Implementation of commonly used p-value-based and parametric multiple testing
procedures (computation of adjusted p-values and simultaneous confidence
intervals) and parallel gatekeeping procedures based on the methodology
presented in the book \"Multiple Testing Problems in Pharmaceutical Statistics\"
(edited by Alex Dmitrienko, Ajit C. Tamhane and Frank Bretz) published by
Chapman and Hall/CRC Press 2009.")
    (license license:gpl2)))

(define-public r-multsurvtests
  (package
    (name "r-multsurvtests")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultSurvTests" version))
       (sha256
        (base32 "04crh4hbfhl8cr0faawv71lyabyilhxpg6i8sr9ki80982mbzcnd"))))
    (properties `((upstream-name . "MultSurvTests")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/lukketotte/MultSurvTests")
    (synopsis "Permutation Tests for Multivariate Survival Analysis")
    (description
     "Multivariate version of the two-sample Gehan and logrank tests, as described in
L.J Wei & J.M Lachin (1984) and Persson et al. (2019).")
    (license license:expat)))

(define-public r-multregcmp
  (package
    (name "r-multregcmp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultRegCMP" version))
       (sha256
        (base32 "01gb6a5kj8gdf57g9c9xzxid5h437g9y625w2d8wfjil97c012x4"))))
    (properties `((upstream-name . "MultRegCMP")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr
                             r-progress
                             r-mvnfast
                             r-ggplot2
                             r-cowplot
                             r-bayesplot))
    (home-page "https://cran.r-project.org/package=MultRegCMP")
    (synopsis
     "Bayesian Multivariate Conway-Maxwell-Poisson Regression Model for Correlated Count Data")
    (description
     "Fits a Bayesian Regression Model for multivariate count data.  This model
assumes that the data is distributed according to the Conway-Maxwell-Poisson
distribution, and for each response variable it is associate different
covariates.  This model allows to account for correlations between the counts by
using latent effects based on the Chib and Winkelmann (2001)
<http://www.jstor.org/stable/1392277> proposal.")
    (license license:expat)))

(define-public r-multpois
  (package
    (name "r-multpois")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multpois" version))
       (sha256
        (base32 "16y3dw9hsslafxznqpmnksvsf0wrnddfgzyghz1b34yxrbvsq5yg"))))
    (properties `((upstream-name . "multpois")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-lme4 r-dplyr r-dfidx r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wobbrock/multpois/")
    (synopsis
     "Analyze Nominal Response Data with the Multinomial-Poisson Trick")
    (description
     "Dichotomous responses having two categories can be analyzed with stats::glm() or
lme4::glmer() using the family=binomial option.  Unfortunately, polytomous
responses with three or more unordered categories cannot be analyzed similarly
because there is no analogous family=multinomial option.  For between-subjects
data, nnet::multinom() can address this need, but it cannot handle random
factors and therefore cannot handle repeated measures.  To address this gap, we
implement the multinomial-Poisson trick (Baker 1994) <doi:10.2307/2348134>,
which transforms nominal response data into counts for each categorical
alternative.  These counts are then analyzed using (mixed) Poisson regression.
Omnibus analyses of variance can be run along with post hoc pairwise
comparisons.  For users wishing to analyze nominal responses from surveys or
experiments, the functions in this package essentially act as though
stats::glm() or lme4::glmer() had a family=multinomial option.")
    (license license:gpl2+)))

(define-public r-multordrs
  (package
    (name "r-multordrs")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultOrdRS" version))
       (sha256
        (base32 "1f23im0m77y8aqdnmkcgdgfx77z84wlwih1wv74by15m4ysj4l2h"))))
    (properties `((upstream-name . "MultOrdRS")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=MultOrdRS")
    (synopsis "Model Multivariate Ordinal Responses Including Response Styles")
    (description
     "In the case of multivariate ordinal responses, parameter estimates can be
severely biased if personal response styles are ignored.  This packages provides
methods to account for personal response styles and to explain the effects of
covariates on the response style, as proposed by Schauberger and Tutz 2021
<doi:10.1177/1471082X20978034>.  The method is implemented both for the
multivariate cumulative model and the multivariate adjacent categories model.")
    (license license:gpl2+)))

(define-public r-multnonparam
  (package
    (name "r-multnonparam")
    (version "1.3.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultNonParam" version))
       (sha256
        (base32 "0p66pqairn0k3ciz31ykb2kk0mr4jyzvarkf1knlh3dkvagh7pvj"))))
    (properties `((upstream-name . "MultNonParam")))
    (build-system r-build-system)
    (propagated-inputs (list r-icsnp))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=MultNonParam")
    (synopsis "Multivariate Nonparametric Methods")
    (description
     "This package provides a collection of multivariate nonparametric methods,
selected in part to support an MS level course in nonparametric statistical
methods.  Methods include adjustments for multiple comparisons, implementation
of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to
produce a confidence region, some permutation tests for linear models, and some
algorithms for calculating exact probabilities associated with one- and two-
stage testing involving Mann-Whitney-Wilcoxon statistics.  Supported by grant
NSF DMS 1712839.  See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.")
    (license license:gpl2)))

(define-public r-multiwayvcov
  (package
    (name "r-multiwayvcov")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiwayvcov" version))
       (sha256
        (base32 "1znsmca16jac8s5z0j64cck27pvf88anm1mfblabqchqqcz1ykfc"))))
    (properties `((upstream-name . "multiwayvcov")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-boot))
    (home-page "http://sites.google.com/site/npgraham1/research/code")
    (synopsis "Multi-Way Standard Error Clustering")
    (description
     "Exports two functions implementing multi-way clustering using the method
suggested by Cameron, Gelbach, & Miller (2011) and cluster (or block)
bootstrapping for estimating variance-covariance matrices.  Normal one and
two-way clustering matches the results of other common statistical packages.
Missing values are handled transparently and rudimentary parallelization support
is provided.")
    (license license:bsd-2)))

(define-public r-multiwayregression
  (package
    (name "r-multiwayregression")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiwayRegression" version))
       (sha256
        (base32 "1w0yd3nwrqpk5nhnrcrd06fbdhg4kizx2p8mpspqnw8qcn970pfl"))))
    (properties `((upstream-name . "MultiwayRegression")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MultiwayRegression")
    (synopsis "Perform Tensor-on-Tensor Regression")
    (description
     "This package provides functions to predict one multi-way array (i.e., a tensor)
from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP)
factorization and a ridge (L_2) penalty [Lock, EF (2018)
<doi:10.1080/10618600.2017.1401544>].  Also includes functions to sample from
the Bayesian posterior of a tensor-on-tensor model.")
    (license license:gpl3)))

(define-public r-multiway
  (package
    (name "r-multiway")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiway" version))
       (sha256
        (base32 "1y9cykarr2aysdrnyab5aklsrc5id5b58881q03lcb5kc385v3gv"))))
    (properties `((upstream-name . "multiway")))
    (build-system r-build-system)
    (propagated-inputs (list r-cmls))
    (home-page "https://cran.r-project.org/package=multiway")
    (synopsis "Component Models for Multi-Way Data")
    (description
     "Fits multi-way component models via alternating least squares algorithms with
optional constraints.  Fit models include N-way Canonical Polyadic
Decomposition, Individual Differences Scaling, Multiway Covariates Regression,
Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker
Factor Analysis.")
    (license license:gpl2+)))

(define-public r-multiwave
  (package
    (name "r-multiwave")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiwave" version))
       (sha256
        (base32 "19581kjcyw86cnzhvhhpn997fixm811ggmvb8jkb3dsxg8vp60jc"))))
    (properties `((upstream-name . "multiwave")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=multiwave")
    (synopsis "Estimation of Multivariate Long-Memory Models Parameters")
    (description
     "Computation of an estimation of the long-memory parameters and the long-run
covariance matrix using a multivariate model (Lobato (1999)
<doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007)
<doi:10.1016/j.jeconom.2006.01.003>).  Two semi-parametric methods are
implemented: a Fourier based approach (Shimotsu (2007)
<doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based approach (Achard and
Gannaz (2016) <doi:10.1111/jtsa.12170>).")
    (license license:gpl2+)))

(define-public r-multiview
  (package
    (name "r-multiview")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiview" version))
       (sha256
        (base32 "1kf16w4wbj84383hvqxhrqlvghyra22p7k9vs03axqxqkhl8krhz"))))
    (properties `((upstream-name . "multiview")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcppeigen
                             r-rcpp
                             r-rcolorbrewer
                             r-matrix
                             r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=multiview")
    (synopsis "Cooperative Learning for Multi-View Analysis")
    (description
     "Cooperative learning combines the usual squared error loss of predictions with
an agreement penalty to encourage the predictions from different data views to
agree.  By varying the weight of the agreement penalty, we get a continuum of
solutions that include the well-known early and late fusion approaches.
Cooperative learning chooses the degree of agreement (or fusion) in an adaptive
manner, using a validation set or cross-validation to estimate test set
prediction error.  In the setting of cooperative regularized linear regression,
the method combines the lasso penalty with the agreement penalty (Ding, D., Li,
S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).")
    (license license:gpl2)))

(define-public r-multiverse
  (package
    (name "r-multiverse")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiverse" version))
       (sha256
        (base32 "070d4giyf14kphb987nd7v2pry1y4h8w4mk8hl01z0128z2ks3h1"))))
    (properties `((upstream-name . "multiverse")))
    (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-tidyselect
                             r-tidyr
                             r-tibble
                             r-styler
                             r-rstudioapi
                             r-rlang
                             r-readr
                             r-r6
                             r-purrr
                             r-magrittr
                             r-knitr
                             r-jsonlite
                             r-furrr
                             r-formatr
                             r-evaluate
                             r-dplyr
                             r-distributional
                             r-collections
                             r-berryfunctions))
    (native-inputs (list r-knitr))
    (home-page "https://mucollective.github.io/multiverse/")
    (synopsis "Create 'multiverse analysis' in R")
    (description
     "Implement multiverse style analyses (Steegen S., Tuerlinckx F, Gelman A.,
Vanpaemal, W., 2016) <doi:10.1177/1745691616658637> to show the robustness of
statistical inference.  Multiverse analysis is a philosophy of statistical
reporting where paper authors report the outcomes of many different statistical
analyses in order to show how fragile or robust their findings are.  The
multiverse package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman
J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm> allows users to concisely and
flexibly implement multiverse-style analysis, which involve declaring alternate
ways of performing an analysis step, in R and R Notebooks.")
    (license license:gpl3+)))

(define-public r-multivator
  (package
    (name "r-multivator")
    (version "1.1-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multivator" version))
       (sha256
        (base32 "1r9v15bg0kyhq5xqzkpj6ilz745q6k3j46zh1n68pf17zmi0k0ww"))))
    (properties `((upstream-name . "multivator")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mathjaxr r-emulator))
    (home-page "https://github.com/RobinHankin/multivator")
    (synopsis "Multivariate Emulator")
    (description
     "This package provides a multivariate generalization of the emulator package.")
    (license license:gpl2)))

(define-public r-multivarsel
  (package
    (name "r-multivarsel")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiVarSel" version))
       (sha256
        (base32 "18wcw80m5knv6hbzczjsx3lf7sn9n84z12zz844agp6234im163p"))))
    (properties `((upstream-name . "MultiVarSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-glmnet))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=MultiVarSel")
    (synopsis "Variable Selection in a Multivariate Linear Model")
    (description
     "It performs variable selection in a multivariate linear model by estimating the
covariance matrix of the residuals then use it to remove the dependence that may
exist among the responses and eventually performs variable selection by using
the Lasso criterion.  The method is described in the paper Perrot-DockÃ¨s et al.
(2017) <@code{arXiv:1704.00076>}.")
    (license license:gpl2+)))

(define-public r-multivarmi
  (package
    (name "r-multivarmi")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiVarMI" version))
       (sha256
        (base32 "1mlaprg3cfmfwrq7wh64fmyv1wpdnil7wpcrcg3921qydk65py6z"))))
    (properties `((upstream-name . "MultiVarMI")))
    (build-system r-build-system)
    (propagated-inputs (list r-poisnonnor
                             r-norm
                             r-moments
                             r-matrix
                             r-corrtoolbox
                             r-corpcor
                             r-binordnonnor))
    (home-page "https://cran.r-project.org/package=MultiVarMI")
    (synopsis "Multiple Imputation for Multivariate Data")
    (description
     "Fully parametric Bayesian multiple imputation framework for massive multivariate
data of different variable types as seen in Demirtas, H. (2017)
<doi:10.1007/978-981-10-3307-0_8>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-multivarious
  (package
    (name "r-multivarious")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multivarious" version))
       (sha256
        (base32 "1lgb7agfdq4fiwg2c2f6an2bx7krl84n79d7744rc514rgl0klp0"))))
    (properties `((upstream-name . "multivarious")))
    (build-system r-build-system)
    (propagated-inputs (list r-svd
                             r-rsvd
                             r-rspectra
                             r-rlang
                             r-purrr
                             r-proxy
                             r-pls
                             r-matrixstats
                             r-matrix
                             r-irlba
                             r-glmnet
                             r-fitdistrplus
                             r-corpcor
                             r-chk))
    (native-inputs (list r-knitr))
    (home-page "https://bbuchsbaum.github.io/multivarious/")
    (synopsis "Extensible Data Structures for Multivariate Analysis")
    (description
     "This package provides a set of basic and extensible data structures and
functions for multivariate analysis, including dimensionality reduction
techniques, projection methods, and preprocessing functions.  The aim of this
package is to offer a flexible and user-friendly framework for multivariate
analysis that can be easily extended for custom requirements and specific data
analysis tasks.")
    (license license:expat)))

(define-public r-multivariaterandomforest
  (package
    (name "r-multivariaterandomforest")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultivariateRandomForest" version))
       (sha256
        (base32 "0mww4x1hqraiyvmw2f9s6h180kplmj36hb7zfg7xlkmgdf4apk8d"))))
    (properties `((upstream-name . "MultivariateRandomForest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-bootstrap))
    (home-page "https://cran.r-project.org/package=MultivariateRandomForest")
    (synopsis "Models Multivariate Cases Using Random Forests")
    (description
     "Models and predicts multiple output features in single random forest considering
the linear relation among the output features, see details in Rahman et al
(2017)<doi:10.1093/bioinformatics/btw765>.")
    (license license:gpl2+)))

(define-public r-multivariateanalysis
  (package
    (name "r-multivariateanalysis")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultivariateAnalysis" version))
       (sha256
        (base32 "1hphl6swqkfjqbybkcpvf0bda6jf434v7l6pqzlviqzlnww94riv"))))
    (properties `((upstream-name . "MultivariateAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi
                             r-plotly
                             r-pcamixdata
                             r-nbclust
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-ggdendro
                             r-factoextra
                             r-ecodist
                             r-crayon
                             r-corrplot
                             r-candisc
                             r-biotools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultivariateAnalysis")
    (synopsis "Pacote Para Analise Multivariada")
    (description
     "Package with multivariate analysis methodologies for experiment evaluation.  The
package estimates dissimilarity measures, builds dendrograms, obtains MANOVA,
principal components, canonical variables, etc. (Pacote com metodologias de
analise multivariada para avaliaÃ§Ã£o de experimentos.  O pacote estima medidas
de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes
principais, variaveis canonicas, etc.).")
    (license license:gpl3)))

(define-public r-multivariance
  (package
    (name "r-multivariance")
    (version "2.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multivariance" version))
       (sha256
        (base32 "1jbv1rhdy6jgvyhwz25hry3xmnxfsl8pwf0j4rj9r8rg6ik8rv1w"))))
    (properties `((upstream-name . "multivariance")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-microbenchmark r-igraph))
    (home-page "https://cran.r-project.org/package=multivariance")
    (synopsis "Measuring Multivariate Dependence Using Distance Multivariance")
    (description
     "Distance multivariance is a measure of dependence which can be used to detect
and quantify dependence of arbitrarily many random vectors.  The necessary
functions are implemented in this packages and examples are given.  It includes:
distance multivariance, distance multicorrelation, dependence structure
detection, tests of independence and copula versions of distance multivariance
based on the Monte Carlo empirical transform.  Detailed references are given in
the package description, as starting point for the theoretic background we refer
to: B. BÃ¶ttcher, Dependence and Dependence Structures: Estimation and
Visualization Using the Unifying Concept of Distance Multivariance.  Open
Statistics, Vol.  1, No.  1 (2020), <doi:10.1515/stat-2020-0001>.")
    (license license:gpl3)))

(define-public r-multivar
  (package
    (name "r-multivar")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multivar" version))
       (sha256
        (base32 "1jyz921m16g24gjmscsz2mi78ng3c5hzhbcankdq9x1i7kdcqawi"))))
    (properties `((upstream-name . "multivar")))
    (build-system r-build-system)
    (propagated-inputs (list r-vars
                             r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-glmnet
                             r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=multivar")
    (synopsis
     "Penalized Estimation of Multiple-Subject Vector Autoregressive (multi-VAR) Models")
    (description
     "This package provides functions for simulating, estimating and forecasting
stationary Vector Autoregressive (VAR) models for multiple subject data using
the penalized multi-VAR framework in Fisher, Kim and Pipiras (2020)
<@code{arXiv:2007.05052>}.")
    (license license:gpl2+)))

(define-public r-multius
  (package
    (name "r-multius")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiUS" version))
       (sha256
        (base32 "0wdvrkl6xvk8xpkm9n0hd5pjks3350ms09jrfqnnxyf3dsbs68dc"))))
    (properties `((upstream-name . "multiUS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-gplots))
    (home-page "https://cran.r-project.org/package=multiUS")
    (synopsis
     "Functions for the Courses Multivariate Analysis and Computer Intensive Methods")
    (description
     "This package provides utility functions for multivariate analysis (factor
analysis, discriminant analysis, and others).  The package is primary written
for the course Multivariate analysis and for the course Computer intensive
methods at the masters program of Applied Statistics at University of Ljubljana.")
    (license license:gpl2+)))

(define-public r-multitool
  (package
    (name "r-multitool")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multitool" version))
       (sha256
        (base32 "1z8yby5prfmp0p9gia8pmr54sw017rqbpblhxk6qxz3w9vnl4zp8"))))
    (properties `((upstream-name . "multitool")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-performance
                             r-parameters
                             r-moments
                             r-lme4
                             r-glue
                             r-ggplot2
                             r-ggdist
                             r-future
                             r-furrr
                             r-flextable
                             r-dplyr
                             r-diagrammer
                             r-correlation
                             r-clipr))
    (native-inputs (list r-knitr))
    (home-page "https://ethan-young.github.io/multitool/")
    (synopsis "Run Multiverse Style Analyses")
    (description
     "Run the same analysis over a range of arbitrary data processing decisions.
multitool provides an interface for creating alternative analysis pipelines and
turning them into a grid of all possible pipelines.  Using this grid as a
blueprint, you can model your data across all possible pipelines and summarize
the results.")
    (license license:expat)))

(define-public r-multistatm
  (package
    (name "r-multistatm")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiStatM" version))
       (sha256
        (base32 "1d2dzchg6xmaazr6sajxq7f5bkx24m70w593wbmn49xxq8fbf1hp"))))
    (properties `((upstream-name . "MultiStatM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-mass r-arrangements))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultiStatM")
    (synopsis "Multivariate Statistical Methods")
    (description
     "Algorithms to build set partitions and commutator matrices and their use in the
construction of multivariate d-Hermite polynomials; estimation and derivation of
theoretical vector moments and vector cumulants of multivariate distributions;
conversion formulae for multivariate moments and cumulants.  Applications to
estimation and derivation of multivariate measures of skewness and kurtosis;
estimation and derivation of asymptotic covariances for d-variate Hermite
polynomials, multivariate moments and cumulants and measures of skewness and
kurtosis.  The formulae implemented are discussed in Terdik (2021,
ISBN:9783030813925), \"Multivariate Statistical Methods\".")
    (license license:gpl3)))

(define-public r-multispatialccm
  (package
    (name "r-multispatialccm")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multispatialCCM" version))
       (sha256
        (base32 "044spqh37yz4b40bx6h2dz8lzjz4jf505mgagxxg1bn1spxk9bi2"))))
    (properties `((upstream-name . "multispatialCCM")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=multispatialCCM")
    (synopsis "Multispatial Convergent Cross Mapping")
    (description
     "The multispatial convergent cross mapping algorithm can be used as a test for
causal associations between pairs of processes represented by time series.  This
is a combination of convergent cross mapping (CCM), described in Sugihara et
al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et
al., 2008, American Naturalist, 171, 71â80.  The algorithm allows CCM to be
implemented on data that are not from a single long time series.  Instead, data
can come from many short time series, which are stitched together using
bootstrapping.")
    (license license:gpl2+)))

(define-public r-multisom
  (package
    (name "r-multisom")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multisom" version))
       (sha256
        (base32 "0msxmrj4iawxg4vf4r7kj26zalxz4di2w4nxgxiakiig4g4ggy9z"))))
    (properties `((upstream-name . "multisom")))
    (build-system r-build-system)
    (propagated-inputs (list r-kohonen r-class))
    (home-page
     "https://sites.google.com/site/malikacharrad/research/multisom-package")
    (synopsis "Clustering a Data Set using Multi-SOM Algorithm")
    (description
     "This package implements two versions of the algorithm namely: stochastic and
batch.  The package determines also the best number of clusters and offers to
the user the best clustering scheme from different results.")
    (license license:gpl2)))

(define-public r-multiskew
  (package
    (name "r-multiskew")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiSkew" version))
       (sha256
        (base32 "1s0k3z1vgdz1cxad007p246ghwx282w06inhrjjscbwliz1vg5ib"))))
    (properties `((upstream-name . "MultiSkew")))
    (build-system r-build-system)
    (propagated-inputs (list r-maxskew))
    (home-page "https://cran.r-project.org/package=MultiSkew")
    (synopsis "Measures, Tests and Removes Multivariate Skewness")
    (description
     "Computes the third multivariate cumulant of either the raw, centered or
standardized data.  Computes the main measures of multivariate skewness,
together with their bootstrap distributions.  Finally, computes the least skewed
linear projections of the data.")
    (license license:gpl2)))

(define-public r-multisitemediation
  (package
    (name "r-multisitemediation")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultisiteMediation" version))
       (sha256
        (base32 "0kkw6zxw29qmgd9nlgqjlyh99icxzkkqnpzzanxx8pn7m2ww0wkf"))))
    (properties `((upstream-name . "MultisiteMediation")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-psych r-mass r-lme4 r-ggplot2))
    (home-page "https://github.com/Xu-Qin/MultisiteMediation")
    (synopsis "Causal Mediation Analysis in Multisite Trials")
    (description
     "Multisite causal mediation analysis using the methods proposed by Qin and Hong
(2017) <doi:10.3102/1076998617694879>, Qin, Hong, Deutsch, and Bein (2019)
<doi:10.1111/rssa.12446>, and Qin, Deutsch, and Hong (2021)
<doi:10.1002/pam.22268>.  It enables causal mediation analysis in multisite
trials, in which individuals are assigned to a treatment or a control group at
each site.  It allows for estimation and hypothesis testing for not only the
population average but also the between-site variance of direct and indirect
effects transmitted through one single mediator or two concurrent (conditionally
independent) mediators.  This strategy conveniently relaxes the assumption of no
treatment-by-mediator interaction while greatly simplifying the outcome model
specification without invoking strong distributional assumptions.  This package
also provides a function that can further incorporate a sample weight and a
nonresponse weight for multisite causal mediation analysis in the presence of
complex sample and survey designs and non-random nonresponse, to enhance both
the internal validity and external validity.  The package also provides a
weighting-based balance checking function for assessing the remaining overt
bias.")
    (license license:gpl2)))

(define-public r-multisite-accuracy
  (package
    (name "r-multisite-accuracy")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multisite.accuracy" version))
       (sha256
        (base32 "0d2i34pphvilzmjvqpxnp76d2dzy4ncg6xvxdg7752gcnp5j1mvl"))))
    (properties `((upstream-name . "multisite.accuracy")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-proc
                             r-metafor
                             r-logistf
                             r-lmertest
                             r-lme4
                             r-coxme))
    (home-page "https://cran.r-project.org/package=multisite.accuracy")
    (synopsis "Estimation of Accuracy in Multisite Machine-Learning Models")
    (description
     "The effects of the site may severely bias the accuracy of a multisite
machine-learning model, even if the analysts removed them when fitting the model
in the training set and applying the model in the test set (Solanes et al.,
Neuroimage 2023, 265:119800).  This simple R package estimates the accuracy of a
multisite machine-learning model unbiasedly, as described in (Solanes et al.,
Psychiatry Research: Neuroimaging 2021, 314:111313).  It currently supports the
estimation of sensitivity, specificity, balanced accuracy (for binary or
multinomial variables), the area under the curve, correlation, mean squarer
error, and hazard ratio for binomial, multinomial, gaussian, and survival
(time-to-event) outcomes.")
    (license license:gpl3)))

(define-public r-multisensi
  (package
    (name "r-multisensi")
    (version "2.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multisensi" version))
       (sha256
        (base32 "0f8i1jvrnmdsws5w7fz9k5pb5yfl5b9hnfd8aq4zj1r6m90kb6sz"))))
    (properties `((upstream-name . "multisensi")))
    (build-system r-build-system)
    (propagated-inputs (list r-sensitivity r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=multisensi")
    (synopsis "Multivariate Sensitivity Analysis")
    (description
     "This package provides functions to perform sensitivity analysis on a model with
multivariate output.")
    (license (license:fsdg-compatible "CeCILL-2"))))

(define-public r-multiselect
  (package
    (name "r-multiselect")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiselect" version))
       (sha256
        (base32 "1cda38zq7c1r56wdfpr5dg0jfw4kzi9p7jq59qm04j461j9ag3q6"))))
    (properties `((upstream-name . "multiselect")))
    (build-system r-build-system)
    (propagated-inputs (list r-hmisc))
    (home-page "https://cran.r-project.org/package=multiselect")
    (synopsis
     "Selecting Combinations of Predictors by Leveraging Multiple AUCs for an Ordered Multilevel Outcome")
    (description
     "Uses multiple AUCs to select a combination of predictors when the outcome has
multiple (ordered) levels and the focus is discriminating one particular level
from the others.  This method is most naturally applied to settings where the
outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017)
<http://biostats.bepress.com/uwbiostat/paper423/>.).")
    (license license:gpl2)))

(define-public r-multiscaledtm
  (package
    (name "r-multiscaledtm")
    (version "0.8.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiscaleDTM" version))
       (sha256
        (base32 "13kslxxbyil5y4ci5s9r0n0ymsrh8c65wvsdzhcgafh9ldr6l1ww"))))
    (properties `((upstream-name . "MultiscaleDTM")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-shiny
                             r-rgl
                             r-rcpparmadillo
                             r-rcpp
                             r-raster
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://ailich.github.io/MultiscaleDTM/")
    (synopsis "Multi-Scale Geomorphometric Terrain Attributes")
    (description
     "Calculates multi-scale geomorphometric terrain attributes from regularly gridded
digital terrain models using a variable focal windows size (Ilich et al. (2023)
<doi:10.1111/tgis.13067>).")
    (license license:gpl3+)))

(define-public r-multis
  (package
    (name "r-multis")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultIS" version))
       (sha256
        (base32 "02ca1r8qfsl1b8rfawdn4xrlwhg5yab260rrsx8ckyv3v75knk8f"))))
    (properties `((upstream-name . "MultIS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmutil
                             r-rlang
                             r-reshape2
                             r-rcolorbrewer
                             r-powerlaw
                             r-plyr
                             r-ltm
                             r-igraph
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-clvalid
                             r-clv
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultIS")
    (synopsis
     "Reconstruction of Clones from Integration Site Readouts and Visualization")
    (description
     "This package provides tools necessary to reconstruct clonal affiliations from
temporally and/or spatially separated measurements of viral integration sites.
For this means it utilizes correlations present in the relative readouts of the
integration sites.  Furthermore, facilities for filtering of the data and
visualization of different steps in the pipeline are provided with the package.")
    (license license:lgpl2.0+)))

(define-public r-multirr
  (package
    (name "r-multirr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiRR" version))
       (sha256
        (base32 "1jrhx3nlqwsv3i6r8fs142llw88qad41rsh0sj1pv1gb928zpvl3"))))
    (properties `((upstream-name . "MultiRR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lme4))
    (home-page "https://cran.r-project.org/package=MultiRR")
    (synopsis "Bias, Precision, and Power for Multi-Level Random Regressions")
    (description
     "Calculates bias, precision, and power for multi-level random regressions.
Random regressions are types of hierarchical models in which data are structured
in groups and (regression) coefficients can vary by groups.  Tools to estimate
model performance are designed mostly for scenarios where (regression)
coefficients vary at just one level. @code{MultiRR} provides simulation and
analytical tools (based on lme4') to study model performance for random
regressions that vary at more than one level (multi-level random regressions),
allowing researchers to determine optimal sampling designs.")
    (license license:gpl2)))

(define-public r-multiroc
  (package
    (name "r-multiroc")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiROC" version))
       (sha256
        (base32 "15ydbxpkdyvsc55di13jvwh9p6dzxc1yj93w1cwsd8jp2y10wsd3"))))
    (properties `((upstream-name . "multiROC")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-magrittr r-boot))
    (home-page "https://cran.r-project.org/package=multiROC")
    (synopsis
     "Calculating and Visualizing ROC and PR Curves Across Multi-Class Classifications")
    (description
     "This package provides tools to solve real-world problems with multiple classes
classifications by computing the areas under ROC and PR curve via
micro-averaging and macro-averaging.  The vignettes of this package can be found
via <https://github.com/@code{WandeRum/multiROC>}.  The methodology is described
in V. Van Asch (2013)
<https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et
al. (2011)
<http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.")
    (license license:gpl3)))

(define-public r-multirobust
  (package
    (name "r-multirobust")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiRobust" version))
       (sha256
        (base32 "15l23a2mkpcxc3y93f761c5fwcj5h13yma4qdfdlr8fiqkvc3acg"))))
    (properties `((upstream-name . "MultiRobust")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MultiRobust")
    (synopsis "Multiply Robust Methods for Missing Data Problems")
    (description
     "Multiply robust estimation for population mean (Han and Wang 2013)
<doi:10.1093/biomet/ass087>, regression analysis (Han 2014)
<doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and
quantile regression (Han et al.  2019) <doi:10.1111/rssb.12309>.")
    (license license:gpl2+)))

(define-public r-multirng
  (package
    (name "r-multirng")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiRNG" version))
       (sha256
        (base32 "189b2n5fy4sl63xifhlk88kahkn0qkh9szrkmzlq40vrwsbfximn"))))
    (properties `((upstream-name . "MultiRNG")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MultiRNG")
    (synopsis "Multivariate Pseudo-Random Number Generation")
    (description
     "Pseudo-random number generation for 11 multivariate distributions: Normal, t,
Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial,
Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart.  The details of
the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-multiridge
  (package
    (name "r-multiridge")
    (version "1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiridge" version))
       (sha256
        (base32 "0nxs00bglkr234nxsxn0djvi8cxavzc2nl2nl3zmnqa9aw2aikza"))))
    (properties `((upstream-name . "multiridge")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-snowfall r-proc r-mgcv))
    (home-page "https://cran.r-project.org/package=multiridge")
    (synopsis "Fast Cross-Validation for Multi-Penalty Ridge Regression")
    (description
     "Multi-penalty linear, logistic and cox ridge regression, including estimation of
the penalty parameters by efficient (repeated) cross-validation and marginal
likelihood maximization.  Multiple high-dimensional data types that require
penalization are allowed, as well as unpenalized variables.  Paired and
preferential data types can be specified.  See Van de Wiel et al. (2021),
<@code{arXiv:2005.09301>}.")
    (license license:gpl3+)))

(define-public r-multirich
  (package
    (name "r-multirich")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multirich" version))
       (sha256
        (base32 "1vv4jhhq46wp6znfv9c47q9z4smn1c560wnbp77qfhiix098wzfj"))))
    (properties `((upstream-name . "multirich")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=multirich")
    (synopsis "Calculate Multivariate Richness via UTC and sUTC")
    (description
     "This package provides functions to calculate Unique Trait Combinations (UTC) and
scaled Unique Trait Combinations (@code{sUTC}) as measures of multivariate
richness.  The package can also calculate beta-diversity for trait richness and
can partition this into nestedness-related and turnover components.  The code
will also calculate several measures of overlap.  See Keyel and Wiegand (2016)
<doi:10.1111/2041-210X.12558> for more details.")
    (license license:gpl2+)))

(define-public r-multirdpg
  (package
    (name "r-multirdpg")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiRDPG" version))
       (sha256
        (base32 "0r6n148pakixw4caj0zwib8zirgl98lm0mxqmhvmqjh389icskxc"))))
    (properties `((upstream-name . "multiRDPG")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=multiRDPG")
    (synopsis "Multiple Random Dot Product Graphs")
    (description
     "Fits the Multiple Random Dot Product Graph Model and performs a test for whether
two networks come from the same distribution.  Both methods are proposed in
Nielsen, A.M., Witten, D., (2018) \"The Multiple Random Dot Product Graph Model\",
@code{arXiv} preprint <@code{arXiv:1811.12172>} (Submitted to Journal of
Computational and Graphical Statistics).")
    (license license:gpl2)))

(define-public r-multipol
  (package
    (name "r-multipol")
    (version "1.0-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multipol" version))
       (sha256
        (base32 "0cn6l0f1mn3n2rzsfr6ncc37aqc7ij9a8pkxbxq74j11bib0bhsf"))))
    (properties `((upstream-name . "multipol")))
    (build-system r-build-system)
    (propagated-inputs (list r-abind))
    (home-page "https://cran.r-project.org/package=multipol")
    (synopsis "Multivariate Polynomials")
    (description
     "Various utilities to manipulate multivariate polynomials.  The package is almost
completely superceded by the spray and mvp packages, which are much more
efficient.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-multiplierdea
  (package
    (name "r-multiplierdea")
    (version "0.1.19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiplierDEA" version))
       (sha256
        (base32 "0ns74496grklacpij2cvbv116kc77wa8kv3jdy9fjzxk8yd6kv4d"))))
    (properties `((upstream-name . "MultiplierDEA")))
    (build-system r-build-system)
    (propagated-inputs (list r-roi-plugin-glpk
                             r-roi
                             r-ompr-roi
                             r-ompr
                             r-lpsolveapi
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=MultiplierDEA")
    (synopsis "Multiplier Data Envelopment Analysis and Cross Efficiency")
    (description
     "This package provides functions are provided for calculating efficiency using
multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision
making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross
efficiency using single and two-phase approach.  In addition, it includes some
datasets for calculating efficiency and cross efficiency.")
    (license license:lgpl2.0)))

(define-public r-multiplex
  (package
    (name "r-multiplex")
    (version "3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiplex" version))
       (sha256
        (base32 "0j941parx94iik14nf2kxixdnwang2clvj7g1b5x08sp96lh6wr9"))))
    (properties `((upstream-name . "multiplex")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mplex/multiplex/")
    (synopsis "Algebraic Tools for the Analysis of Multiple Social Networks")
    (description
     "Algebraic procedures for analyses of multiple social networks are delivered with
this package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>.
multiplex makes possible, among other things, to create and manipulate
multiplex, multimode, and multilevel network data with different formats.
Effective ways are available to treat multiple networks with routines that
combine algebraic systems like the partially ordered semigroup with
decomposition procedures or semiring structures with the relational bundles
occurring in different types of multivariate networks.  multiplex provides also
an algebraic approach for affiliation networks through Galois derivations
between families of the pairs of subsets in the two domains of the network with
visualization options.")
    (license license:gpl3)))

(define-public r-multiplestressr
  (package
    (name "r-multiplestressr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiplestressR" version))
       (sha256
        (base32 "167ch16bdlk4syjbgn1149q1np90w6299ph35p9npdh1zcynm95f"))))
    (properties `((upstream-name . "multiplestressR")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis r-patchwork r-ggplot2))
    (home-page "https://benjburgess.github.io/multiplestressR/")
    (synopsis
     "Additive and Multiplicative Null Models for Multiple Stressor Data")
    (description
     "An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>)
and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null
models for multiple stressor data (Burgess et al., 2021
<doi:10.1101/2021.07.21.453207>).  Effect sizes are able to be calculated for
either null model, and subsequently classified into one of four different
interaction classifications (e.g., antagonistic or synergistic interactions).
Analyses can be conducted on data for single experiments through to large
meta-analytical datasets.  Minimal input (or statistical knowledge) is required,
with any output easily understood.  Summary figures are also able to be easily
generated.")
    (license license:gpl3+)))

(define-public r-multipleregression
  (package
    (name "r-multipleregression")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultipleRegression" version))
       (sha256
        (base32 "1l9rn6zc60ih7lyydrsy1ih94ksim14yd7kiapiga58mr7abi45h"))))
    (properties `((upstream-name . "MultipleRegression")))
    (build-system r-build-system)
    (propagated-inputs (list r-crayon))
    (home-page "https://cran.r-project.org/package=MultipleRegression")
    (synopsis "Multiple Regression Analysis")
    (description
     "This package provides tools to analysis of experiments having two or more
quantitative explanatory variables and one quantitative dependent variable.
Experiments can be without repetitions or with a statistical design (Hair JF,
2016) <ISBN: 13: 978-0138132637>.  Pacote para uma analise de experimentos
havendo duas ou mais variaveis explicativas quantitativas e uma variavel
dependente quantitativa.  Os experimentos podem ser sem repeticoes ou com
delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.")
    (license license:gpl3)))

(define-public r-multipleoutcomes
  (package
    (name "r-multipleoutcomes")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multipleOutcomes" version))
       (sha256
        (base32 "1hdz7nsk8737pdnqf33ggddn23pm488i60w5cpq0qwynck5mk4rh"))))
    (properties `((upstream-name . "multipleOutcomes")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-stringr r-numderiv r-momentfit
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=multipleOutcomes")
    (synopsis
     "Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes")
    (description
     "Regression models can be fitted for multiple outcomes simultaneously.  This
package computes estimates of parameters across fitted models and returns the
matrix of asymptotic covariance.  Various applications of this package,
including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple
comparison adjustment, are illustrated.")
    (license license:expat)))

(define-public r-multiplencc
  (package
    (name "r-multiplencc")
    (version "1.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multipleNCC" version))
       (sha256
        (base32 "0vgldg31mb57l5lig9l4clv060fiwx842adv1qbdirfrpyrg295i"))))
    (properties `((upstream-name . "multipleNCC")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mgcv))
    (home-page "https://cran.r-project.org/package=multipleNCC")
    (synopsis "Weighted Cox-Regression for Nested Case-Control Data")
    (description
     "Fit Cox proportional hazard models with a weighted partial likelihood.  It
handles one or multiple endpoints, additional matching and makes it possible to
reuse controls for other endpoints Stoer NC and Samuelsen SO (2016)
<doi:10.32614/rj-2016-030>.")
    (license license:gpl2)))

(define-public r-multipledl
  (package
    (name "r-multipledl")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multipleDL" version))
       (sha256
        (base32 "0b8858w0g45xkhschg742kxicd1cp4xhfacn0q3yh17k2fcif7r3"))))
    (properties `((upstream-name . "multipleDL")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-sparsem
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-bh))
    (home-page "https://cran.r-project.org/package=multipleDL")
    (synopsis
     "Addressing Detection Limits by Cumulative Probability Models (CPMs)")
    (description
     "Build CPMs (cumulative probability models, also known as cumulative link models)
to account for detection limits (both single and multiple detection limits) in
response variables.  Conditional quantiles and conditional CDFs can be
calculated based on fitted models.  The package implements methods described in
Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E.
(2022). \"Addressing Detection Limits with Semiparametric Cumulative Probability
Models\". <@code{arXiv:2207.02815>}.")
    (license license:gpl2+)))

(define-public r-multiplebubbles
  (package
    (name "r-multiplebubbles")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultipleBubbles" version))
       (sha256
        (base32 "047a1pql3jf500gwlap6ws5z7mx0sqmdq6jbql24nl5iypi9jqpf"))))
    (properties `((upstream-name . "MultipleBubbles")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-foreach))
    (home-page "https://cran.r-project.org/package=MultipleBubbles")
    (synopsis "Test and Detection of Explosive Behaviors for Time Series")
    (description
     "This package provides the Augmented Dickey-Fuller test and its variations to
check the existence of bubbles (explosive behavior) for time series, based on
the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a)
<doi:10.1111/iere.12131>.  Some functions may take a while depending on the size
of the data used, or the number of Monte Carlo replications applied.")
    (license license:gpl2+)))

(define-public r-multiplebreakpoints
  (package
    (name "r-multiplebreakpoints")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultipleBreakpoints" version))
       (sha256
        (base32 "1vzz5dj2jml03pxdp3is3vg0gc6fwy08m1jx9qmiv6rhfzsdmfpk"))))
    (properties `((upstream-name . "MultipleBreakpoints")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://cran.r-project.org/package=MultipleBreakpoints")
    (synopsis
     "Estimating Multiple Breakpoints for a Sequence of Realizations of Bernoulli Variables")
    (description
     "The iterative procedure estimates structural changes in the success probability
of Bernoulli variables.  It estimates the number and location of the breakpoints
as well as the success probability of the different sequences between the
breakpoints.  In addition, it provides a graphical illustration of the result.")
    (license license:gpl3)))

(define-public r-multiphen
  (package
    (name "r-multiphen")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiPhen" version))
       (sha256
        (base32 "0razswg4wqsz4q0dvpxp3swmxk82ig8gkyi4vcmxh11nlhb258w3"))))
    (properties `((upstream-name . "MultiPhen")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-meta
                             r-mass
                             r-hardyweinberg
                             r-epitools
                             r-abind))
    (home-page "https://cran.r-project.org/package=MultiPhen")
    (synopsis "Package to Test for Multi-Trait Association")
    (description
     "This package performs genetic association tests between SNPs (one-at-a-time) and
multiple phenotypes (separately or in joint model).")
    (license license:gpl2)))

(define-public r-multipanelfigure
  (package
    (name "r-multipanelfigure")
    (version "2.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multipanelfigure" version))
       (sha256
        (base32 "0f35hfpd5sxjawpq33bwlabppfy8s7x53p6fmzpxlpdgnw61kg7v"))))
    (properties `((upstream-name . "multipanelfigure")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-magrittr
                             r-magick
                             r-gtable
                             r-gridgraphics
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=multipanelfigure")
    (synopsis "Infrastructure to Assemble Multi-Panel Figures (from Grobs)")
    (description
     "This package provides tools to create a layout for figures made of multiple
panels, and to fill the panels with base, lattice', ggplot2 and
@code{ComplexHeatmap} plots, grobs, as well as content from all image formats
supported by @code{ImageMagick} (accessed through magick').")
    (license license:gpl3+)))

(define-public r-multiord
  (package
    (name "r-multiord")
    (version "2.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiOrd" version))
       (sha256
        (base32 "1fmp8979ibxrx43vhi6dncljpbc1388y915d4g0gnl6a7z8g1dnh"))))
    (properties `((upstream-name . "MultiOrd")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-mvtnorm r-matrix r-corpcor))
    (home-page "https://cran.r-project.org/package=MultiOrd")
    (synopsis "Generation of Multivariate Ordinal Variates")
    (description
     "This package provides a method for multivariate ordinal data generation given
marginal distributions and correlation matrix based on the methodology proposed
by Demirtas (2006) <DOI:10.1080/10629360600569246>.")
    (license license:gpl2)))

(define-public r-multiocc
  (package
    (name "r-multiocc")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiocc" version))
       (sha256
        (base32 "1ndwky6rjyb7x0mg2xx5lsxr5yyhg7a9jrwmwryjg6jsw23965qx"))))
    (properties `((upstream-name . "multiocc")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-tmvtnorm r-mass r-interp r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=multiocc")
    (synopsis "Fits Multivariate Spatio-Temporal Occupancy Model")
    (description
     "Spatio-temporal multivariate occupancy models can handle multiple species in
occupancy models.  This method for fitting such models is described in Hepler
and Erhardt (2021) \"A spatiotemporal model for multivariate occupancy data\"
<https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2657>.")
    (license license:gpl2)))

(define-public r-multiobjmatch
  (package
    (name "r-multiobjmatch")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiObjMatch" version))
       (sha256
        (base32 "0pgmhwydxi741ys8j1ba00rp6gbssjzgamqrywhvpc0jddaqdmrc"))))
    (properties `((upstream-name . "MultiObjMatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlemon
                             r-rlang
                             r-rcurl
                             r-rcbalance
                             r-plyr
                             r-optmatch
                             r-matchmulti
                             r-mass
                             r-gtools
                             r-ggplot2
                             r-fields
                             r-dplyr
                             r-cobalt))
    (home-page "https://cran.r-project.org/package=MultiObjMatch")
    (synopsis "Multi-Objective Matching Algorithm")
    (description
     "Matching algorithm based on network-flow structure.  Users are able to modify
the emphasis on three different optimization goals: two different distance
measures and the number of treated units left unmatched.  The method is proposed
by Pimentel and Kelz (2019) <doi:10.1080/01621459.2020.1720693>.  The rrelaxiv
package, which provides an alternative solver for the underlying network flow
problems, carries an academic license and is not available on CRAN, but may be
downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.")
    (license license:expat)))

(define-public r-multinttestfunc
  (package
    (name "r-multinttestfunc")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multIntTestFunc" version))
       (sha256
        (base32 "02gv9fjjhm7d77g6jmkf52b7xchsx123zz4bkdxl0xn8s64z4jpk"))))
    (properties `((upstream-name . "multIntTestFunc")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/KlausHerrmann/multIntTestFunc")
    (synopsis "Provides Test Functions for Multivariate Integration")
    (description
     "This package provides implementations of functions that can be used to test
multivariate integration routines.  The package covers six different integration
domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative
real numbers and R^n).  For each domain several functions with different
properties (smooth, non-differentiable, ...) are available.  The functions are
available in all dimensions n >= 1.  For each function the exact value of the
integral is known and implemented to allow testing the accuracy of multivariate
integration routines.  Details on the available test functions can be found at
on the development website.")
    (license license:expat)))

(define-public r-multinomineq
  (package
    (name "r-multinomineq")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multinomineq" version))
       (sha256
        (base32 "0ip5ax1s7bk98hq9yqvhx0hiawxbac901zw41v0issq302dhvylk"))))
    (properties `((upstream-name . "multinomineq")))
    (build-system r-build-system)
    (propagated-inputs (list r-rglpk
                             r-rcppxptrutils
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-quadprog
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/danheck/multinomineq")
    (synopsis
     "Bayesian Inference for Multinomial Models with Inequality Constraints")
    (description
     "This package implements Gibbs sampling and Bayes factors for multinomial models
with linear inequality constraints on the vector of probability parameters.  As
special cases, the model class includes models that predict a linear order of
binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models
assuming that the parameter vector p must be inside the convex hull of a finite
number of predicted patterns (i.e., vertices).  A formal definition of
inequality-constrained multinomial models and the implemented computational
methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019).  Multinomial
models with linear inequality constraints: Overview and improvements of
computational methods for Bayesian inference.  Journal of Mathematical
Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>.  Inequality-constrained
multinomial models have applications in the area of judgment and decision making
to fit and test random utility models (Regenwetter, M., Dana, J., &
Davis-Stober, C.P. (2011).  Transitivity of preferences.  Psychological Review,
118, 42â56, <doi:10.1037/a0021150>) or to perform outcome-based strategy
classification to select the decision strategy that provides the best account
for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., &
Moshagen, M. (2017).  From information processing to decisions: Formalizing and
comparing probabilistic choice models.  Cognitive Psychology, 96, 26â40.
<doi:10.1016/j.cogpsych.2017.05.003>).")
    (license license:gpl3)))

(define-public r-multinomiallogitmix
  (package
    (name "r-multinomiallogitmix")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multinomialLogitMix" version))
       (sha256
        (base32 "11mv9xsixcjqv3cq5ay1id3736gzdk192cw2z9khlv1j9bf1x4xh"))))
    (properties `((upstream-name . "multinomialLogitMix")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-mvtnorm
                             r-matrixstats
                             r-mass
                             r-label-switching
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-coda))
    (home-page "https://cran.r-project.org/package=multinomialLogitMix")
    (synopsis
     "Clustering Multinomial Count Data under the Presence of Covariates")
    (description
     "This package provides methods for model-based clustering of multinomial counts
under the presence of covariates using mixtures of multinomial logit models, as
implemented in Papastamoulis (2023) <DOI:10.1007/s11634-023-00547-5>.  These
models are estimated under a frequentist as well as a Bayesian setup using the
Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC),
respectively.  The (unknown) number of clusters is selected according to the
Integrated Completed Likelihood criterion (for the frequentist model), and
estimating the number of non-empty components using overfitting mixture models
after imposing suitable sparse prior assumptions on the mixing proportions (in
the Bayesian case), see Rousseau and Mengersen (2011)
<DOI:10.1111/j.1467-9868.2011.00781.x>.  In the latter case, various MCMC chains
run in parallel and are allowed to switch states.  The final MCMC output is
suitably post-processed in order to undo label switching using the Equivalence
Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016)
<DOI:10.18637/jss.v069.c01>.")
    (license license:gpl2)))

(define-public r-multinomialci
  (package
    (name "r-multinomialci")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultinomialCI" version))
       (sha256
        (base32 "0cd6yxszbxdgpvfkfx5d3w3f0157lxljpkwlnijb99i911rbyflp"))))
    (properties `((upstream-name . "MultinomialCI")))
    (build-system r-build-system)
    (home-page "https://ccia.ugr.es/~pjvi/")
    (synopsis
     "Simultaneous Confidence Intervals for Multinomial Proportions According to the Method by Sison and Glaz")
    (description
     "An implementation of a method for building simultaneous confidence intervals for
the probabilities of a multinomial distribution given a set of observations,
proposed by Sison and Glaz in their paper: Sison, C.P and J. Glaz.  Simultaneous
confidence intervals and sample size determination for multinomial proportions.
Journal of the American Statistical Association, 90:366-369 (1995).  The method
is an R translation of the SAS code implemented by May and Johnson in their
paper: May, W.L. and W.D. Johnson.  Constructing two-sided simultaneous
confidence intervals for multinomial proportions for small counts in a large
number of cells.  Journal of Statistical Software 5(6) (2000).  Paper and code
available at <DOI:10.18637/jss.v005.i06>.")
    (license license:lgpl3+)))

(define-public r-multinma
  (package
    (name "r-multinma")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multinma" version))
       (sha256
        (base32 "1k4a0rgbxwnbkhdksjh25cp2m26qjl0rsqhbz3d6v7s1nms4aikz"))))
    (properties `((upstream-name . "multinma")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist
                             r-tidyr
                             r-tibble
                             r-survival
                             r-stringr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rdpack
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-randtoolbox
                             r-purrr
                             r-matrix
                             r-igraph
                             r-glue
                             r-ggraph
                             r-ggplot2
                             r-ggdist
                             r-forcats
                             r-dplyr
                             r-copula
                             r-bh
                             r-bayesplot))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://dmphillippo.github.io/multinma/")
    (synopsis
     "Bayesian Network Meta-Analysis of Individual and Aggregate Data")
    (description
     "Network meta-analysis and network meta-regression models for aggregate data,
individual patient data, and mixtures of both individual and aggregate data
using multilevel network meta-regression as described by Phillippo et al. (2020)
<doi:10.1111/rssa.12579>.  Models are estimated in a Bayesian framework using
Stan'.")
    (license license:gpl3)))

(define-public r-multinets
  (package
    (name "r-multinets")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multinets" version))
       (sha256
        (base32 "1i5jsq1xy0g0l5d1z3hz4gj6inj4rhd6lf2s5xzd5k8ccp5aba8c"))))
    (properties `((upstream-name . "multinets")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-igraph))
    (home-page "https://github.com/neylsoncrepalde/multinets")
    (synopsis "Multilevel Networks Analysis")
    (description
     "Analyze multilevel networks as described in Lazega et al (2008)
<doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders (2016,
ISBN:978-3-319-24520-1).  The package was developed essentially as an extension
to igraph'.")
    (license license:gpl3)))

(define-public r-multinet
  (package
    (name "r-multinet")
    (version "4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multinet" version))
       (sha256
        (base32 "1rrb03nl658r28sjn4d1g7hdwz5jsg8hgwhygy79xfsqf1pykmy8"))))
    (properties `((upstream-name . "multinet")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-rcolorbrewer r-igraph))
    (home-page "https://cran.r-project.org/package=multinet")
    (synopsis "Analysis and mining of multilayer social networks")
    (description
     "This package provides functions for the creation/generation and analysis of
multilayer social networks <doi:10.18637/jss.v098.i08>.")
    (license license:asl2.0)))

(define-public r-multiness
  (package
    (name "r-multiness")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiness" version))
       (sha256
        (base32 "08bgjs2mrmsdvha10dr74262yk5ghyflvs8pqbl7bc8aws7lv8f5"))))
    (properties `((upstream-name . "multiness")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-matrix r-glmnet))
    (home-page "https://github.com/peterwmacd/multiness/")
    (synopsis "MULTIplex NEtworks with Shared Structure")
    (description
     "Model fitting and simulation for Gaussian and logistic inner product
@code{MultiNeSS} models for multiplex networks.  The package implements a convex
fitting algorithm with fully adaptive parameter tuning, including options for
edge cross-validation.  For more details see @code{MacDonald} et al. (2020).")
    (license license:gpl3+)))

(define-public r-multimorbidity
  (package
    (name "r-multimorbidity")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multimorbidity" version))
       (sha256
        (base32 "07jy3pzws91qwg7i4fig1rkh3bkjqv6icm03lvw00g3f65mdr6y2"))))
    (properties `((upstream-name . "multimorbidity")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-sqldf
                             r-rlang
                             r-magrittr
                             r-lubridate
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/WYATTBENSKEN/multimorbidity")
    (synopsis
     "Harmonizing Various Comorbidity, Multimorbidity, and Frailty Measures")
    (description
     "Identifying comorbidities, frailty, and multimorbidity in claims and
administrative data is often a duplicative process.  The functions contained in
this package are meant to first prepare the data to a format acceptable by all
other packages, then provide a uniform and simple approach to generate
comorbidity and multimorbidity metrics based on these claims data.  The package
is ever evolving to include new metrics, and is always looking for new measures
to include.  The citations used in this package include the following
publications: Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M.
Coffey (1998) <doi:10.1097/00005650-199801000-00004>, Brian J Moore, Susan
White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>,
Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald @code{MacKenzie} (1987)
<doi:10.1016/0021-9681(87)90171-8>, Richard A. Deyo, Daniel C. Cherkin, Marcia
A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>, Hude Quan, Vijaya
Sundararajan, Patricia Halfon, et al. (2005)
<doi:10.1097/01.mlr.0000182534.19832.83>, Dae Hyun Kim, Sebastian Schneeweiss,
Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>, Melissa Y Wei, David
Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>, Kathryn Nicholson,
Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>,
Martin Fortin, JosÃ© Almirall, and Kathryn Nicholson
(2017)<doi:10.15256/joc.2017.7.122>.")
    (license license:expat)))

(define-public r-multimode
  (package
    (name "r-multimode")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multimode" version))
       (sha256
        (base32 "0qxb67qss2pmid0wiwblqng5qmihqchdhbkyjklgj20pvbjz1wjm"))))
    (properties `((upstream-name . "multimode")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-ks r-diptest))
    (native-inputs (list gfortran))
    (home-page "https://doi.org/10.18637/jss.v097.i09")
    (synopsis "Mode Testing and Exploring")
    (description
     "Different examples and methods for testing (including different proposals
described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and
exploring (including the mode tree, mode forest and @code{SiZer}) the number of
modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.")
    (license license:gpl3)))

(define-public r-multimix
  (package
    (name "r-multimix")
    (version "1.0-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multimix" version))
       (sha256
        (base32 "1dpy5pi0z5jfmkazxqn4jf462yi4n25jdqrzk4d3iv0hcrjmikk1"))))
    (properties `((upstream-name . "multimix")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://github.com/jmcurran/multimix")
    (synopsis
     "Fit Mixture Models Using the Expectation Maximisation (EM) Algorithm")
    (description
     "This package provides a set of functions which use the Expectation Maximisation
(EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977)
<doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from incomplete data
via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to
take a finite mixture model approach to clustering.  The package is designed to
cluster multivariate data that have categorical and continuous variables and
that possibly contain missing values.  The method is described in Hunt, L. and
Jorgensen, M. (1999) <doi:10.1111/1467-842X.00071> Australian & New Zealand
Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003)
<doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data with
missing information, Computational Statistics & Data Analysis, 41(3-4),
429--440.")
    (license license:gpl2+)))

(define-public r-multimedia
  (package
    (name "r-multimedia")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multimedia" version))
       (sha256
        (base32 "0rh7z5wn3ndg03hkd42kk43ydxcdyhrsfxbvlxb3bir6pvwa9s6m"))))
    (properties `((upstream-name . "multimedia")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tidygraph
                             r-summarizedexperiment
                             r-s4vectors
                             r-rlang
                             r-ranger
                             r-purrr
                             r-progress
                             r-phyloseq
                             r-patchwork
                             r-minilnm
                             r-mass
                             r-glue
                             r-glmnetutils
                             r-ggplot2
                             r-formula-tools
                             r-fansi
                             r-dplyr
                             r-cli
                             r-brms))
    (native-inputs (list r-knitr))
    (home-page "https://krisrs1128.github.io/multimedia/")
    (synopsis "Multimodal Mediation Analysis")
    (description
     "Multimodal mediation analysis is an emerging problem in microbiome data
analysis.  Multimedia make advanced mediation analysis techniques easy to use,
ensuring that all statistical components are transparent and adaptable to
specific problem contexts.  The package provides a uniform interface to direct
and indirect effect estimation, synthetic null hypothesis testing, bootstrap
confidence interval construction, and sensitivity analysis.  More details are
available in Jiang et al. (2024) \"multimedia: Multimodal Mediation Analysis of
Microbiome Data\" <doi:10.1101/2024.03.27.587024>.")
    (license license:cc0)))

(define-public r-multimarker
  (package
    (name "r-multimarker")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiMarker" version))
       (sha256
        (base32 "1kj29m1bzqmsj63drad4gvq9glwbnyca68df8sycpi7h4b8zirgq"))))
    (properties `((upstream-name . "multiMarker")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-ordinalnet))
    (home-page "https://cran.r-project.org/package=multiMarker")
    (synopsis
     "Latent Variable Model to Infer Food Intake from Multiple Biomarkers")
    (description
     "This package provides a latent variable model based on factor analytic and
mixture of experts models, designed to infer food intake from multiple
biomarkers data.  The model is framed within a Bayesian hierarchical framework,
which provides flexibility to adapt to different biomarker distributions and
facilitates inference on food intake from biomarker data alone, along with the
associated uncertainty.  Details are in D'Angelo, et al. (2020)
<@code{arXiv:2006.02995>}.")
    (license license:gpl2+)))

(define-public r-multimark
  (package
    (name "r-multimark")
    (version "2.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multimark" version))
       (sha256
        (base32 "14zgp3pqxmfjxf78rfr92qd3gjyzfrx5sbid3gcg14bsnpyhkns4"))))
    (properties `((upstream-name . "multimark")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod
                             r-sp
                             r-rmark
                             r-raster
                             r-prodlim
                             r-mvtnorm
                             r-matrix
                             r-coda
                             r-brobdingnag))
    (home-page "https://cran.r-project.org/package=multimark")
    (synopsis
     "Capture-Mark-Recapture Analysis using Multiple Non-Invasive Marks")
    (description
     "Traditional and spatial capture-mark-recapture analysis with multiple
non-invasive marks.  The models implemented in multimark combine encounter
history data arising from two different non-invasive \"marks\", such as images of
left-sided and right-sided pelage patterns of bilaterally asymmetrical species,
to estimate abundance and related demographic parameters while accounting for
imperfect detection.  Bayesian models are specified using simple formulae and
fitted using Markov chain Monte Carlo.  Addressing deficiencies in currently
available software, multimark also provides a user-friendly interface for
performing Bayesian multimodel inference using non-spatial or spatial
capture-recapture data consisting of a single conventional mark or multiple
non-invasive marks.  See @code{McClintock} (2015) <doi:10.1002/ece3.1676> and
Maronde et al. (2020) <doi:10.1002/ece3.6990>.")
    (license license:gpl2)))

(define-public r-multilink
  (package
    (name "r-multilink")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilink" version))
       (sha256
        (base32 "0bjsmr8ca8c3vhq8p8sadn5l4xns5isssmsvi47i3l96j51g7das"))))
    (properties `((upstream-name . "multilink")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-recordlinkage
                             r-rcpparmadillo
                             r-rcpp
                             r-mcclust
                             r-igraph
                             r-geosphere))
    (home-page "https://github.com/aleshing/multilink")
    (synopsis "Multifile Record Linkage and Duplicate Detection")
    (description
     "Implementation of the methodology of Aleshin-Guendel & Sadinle (2022)
<doi:10.1080/01621459.2021.2013242>.  It handles the general problem of
multifile record linkage and duplicate detection, where any number of files are
to be linked, and any of the files may have duplicates.")
    (license license:gpl3)))

(define-public r-multilinguer
  (package
    (name "r-multilinguer")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilinguer" version))
       (sha256
        (base32 "16r8ishisrh5xxjvdqaczqd16fva9fhw7h1sk076if452wwwk3zl"))))
    (properties `((upstream-name . "multilinguer")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis r-sys r-rappdirs r-askpass))
    (home-page "https://github.com/mrchypark/multilinguer")
    (synopsis "Gentle Language Installer for R User")
    (description
     "This package provides install functions of other languages such as java',
python'.")
    (license license:expat)))

(define-public r-multilevlca
  (package
    (name "r-multilevlca")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilevLCA" version))
       (sha256
        (base32 "0pmviwqsyb4lj8l4m359c2rynbb57k6ljsbvy3kg4nld9yyq9wa0"))))
    (properties `((upstream-name . "multilevLCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-magrittr
                             r-klar
                             r-foreach
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=multilevLCA")
    (synopsis
     "Estimates and Plots Single-Level and Multilevel Latent Class Models")
    (description
     "Efficiently estimates single- and multilevel latent class models with
covariates, allowing for output visualization in all specifications.  For more
technical details, see Lyrvall et al (2023)
<doi:10.48550/@code{arXiv.2305.07276>}.")
    (license license:gpl2+)))

(define-public r-multileveltools
  (package
    (name "r-multileveltools")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilevelTools" version))
       (sha256
        (base32 "01fzx7j0mkb4fr7axv34184w6w5nfsdcr51nscci1mi5b2805x1s"))))
    (properties `((upstream-name . "multilevelTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-nlme
                             r-lmertest
                             r-lme4
                             r-lavaan
                             r-jwileymisc
                             r-ggplot2
                             r-extraoperators
                             r-data-table
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "http://joshuawiley.com/multilevelTools")
    (synopsis
     "Multilevel and Mixed Effects Model Diagnostics and Effect Sizes")
    (description
     "Effect sizes, diagnostics and performance metrics for multilevel and mixed
effects models.  Includes marginal and conditional R2 estimates for linear mixed
effects models based on Johnson (2014) <doi:10.1111/2041-210X.12225>.")
    (license license:gpl3+)))

(define-public r-multilevelpsa
  (package
    (name "r-multilevelpsa")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilevelPSA" version))
       (sha256
        (base32 "0926jaicdxk846vyfphb2hyg3zan3wal44x1bwamws0zf4flc4hj"))))
    (properties `((upstream-name . "multilevelPSA")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-reshape
                             r-psych
                             r-psagraphics
                             r-plyr
                             r-party
                             r-mass
                             r-ggplot2))
    (home-page "http://github.com/jbryer/multilevelPSA")
    (synopsis "Multilevel Propensity Score Analysis")
    (description
     "Conducts and visualizes propensity score analysis for multilevel, or clustered
data.  Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.")
    (license license:gpl2+)))

(define-public r-multilevelmod
  (package
    (name "r-multilevelmod")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilevelmod" version))
       (sha256
        (base32 "0sq16w2a0wvxbvca5wknc77lzkdfvr8j71bc0d569hdfm59nrp2k"))))
    (properties `((upstream-name . "multilevelmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-parsnip
                             r-lme4
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tidymodels/multilevelmod")
    (synopsis "Model Wrappers for Multi-Level Models")
    (description
     "Bindings for hierarchical regression models for use with the parsnip package.
Models include longitudinal generalized linear models (Liang and Zeger, 1986)
<doi:10.1093/biomet/73.1.13>, and mixed-effect models (Pinheiro and Bates)
<doi:10.1007/978-1-4419-0318-1_1>.")
    (license license:expat)))

(define-public r-multilevelmediation
  (package
    (name "r-multilevelmediation")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilevelmediation" version))
       (sha256
        (base32 "0aq38qz4i05sn3qa1f0lwpf57r4aya30i028gfdj66w0nh766pg0"))))
    (properties `((upstream-name . "multilevelmediation")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-posterior
                             r-parallelly
                             r-nlme
                             r-mcmcpack
                             r-matrixcalc
                             r-future
                             r-furrr
                             r-brms))
    (home-page "https://cran.r-project.org/package=multilevelmediation")
    (synopsis "Utility Functions for Multilevel Mediation Analysis")
    (description
     "The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel
mediation, the option of a moderating variable for either the a, b, or both
paths, and covariates.  Currently the 1-1-1 model is supported and several
options of random effects; the initial code for bootstrapping was evaluated in
simulations by Falk, Vogel, Hammami, and MioÄeviÄ (2024)
<doi:10.3758/s13428-023-02079-4>.  Support for Bayesian estimation using brms
comprises ongoing work.  Currently only continuous mediators and outcomes are
supported.  Factors for any predictors must be numerically represented.")
    (license license:gpl3)))

(define-public r-multilevelcoda
  (package
    (name "r-multilevelcoda")
    (version "1.3.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilevelcoda" version))
       (sha256
        (base32 "0dd31bs1wfwfmi65aj8ycmipyszpl79cik5z2w7rkii4pl1zc6ja"))))
    (properties `((upstream-name . "multilevelcoda")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinystan
                             r-shiny
                             r-plotly
                             r-loo
                             r-insight
                             r-hrbrthemes
                             r-ggplot2
                             r-future
                             r-foreach
                             r-extraoperators
                             r-emmeans
                             r-dt
                             r-dofuture
                             r-data-table
                             r-compositions
                             r-bslib
                             r-brms
                             r-bayestestr
                             r-bayesplot
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://florale.github.io/multilevelcoda/")
    (synopsis "Estimate Bayesian Multilevel Models for Compositional Data")
    (description
     "Implement Bayesian Multilevel Modelling for compositional data in a multilevel
framework.  Compute multilevel compositional data and Isometric log ratio (ILR)
at between and within-person levels, fit Bayesian multilevel models for
compositional predictors and outcomes, and run post-hoc analyses such as
isotemporal substitution models.")
    (license license:gpl3+)))

(define-public r-multilevel
  (package
    (name "r-multilevel")
    (version "2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilevel" version))
       (sha256
        (base32 "0ds7q3q2ncldb4iik24xisyxyyv1yv8l1yz7zd7cy036bna1yp5i"))))
    (properties `((upstream-name . "multilevel")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass))
    (home-page "https://www.r-project.org")
    (synopsis "Multilevel Functions")
    (description
     "This package provides tools used by organizational researchers for the analysis
of multilevel data.  Includes four broad sets of tools.  First, functions for
estimating within-group agreement and reliability indices.  Second, functions
for manipulating multilevel and longitudinal (panel) data.  Third, simulations
for estimating power and generating multilevel data.  Fourth, miscellaneous
functions for estimating reliability and performing simple calculations and data
transformations.")
    (license license:gpl2+)))

(define-public r-multilcirt
  (package
    (name "r-multilcirt")
    (version "2.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiLCIRT" version))
       (sha256
        (base32 "1qls0qp5fz377h50lvpzq3vkw49i3nvizli98gss50nqci8ssqm4"))))
    (properties `((upstream-name . "MultiLCIRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-limsolve))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=MultiLCIRT")
    (synopsis "Multidimensional Latent Class Item Response Theory Models")
    (description
     "Framework for the Item Response Theory analysis of dichotomous and ordinal
polytomous outcomes under the assumption of multidimensionality and discreteness
of the latent traits.  The fitting algorithms allow for missing responses and
for different item parameterizations and are based on the
Expectation-Maximization paradigm.  Individual covariates affecting the class
weights may be included in the new version (since 2.1).")
    (license license:gpl2+)))

(define-public r-multilaterals
  (package
    (name "r-multilaterals")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multilaterals" version))
       (sha256
        (base32 "11zfdhn6qzqz0n31g23bn40wnfzkjndb753iiqvihirphzb74v5v"))))
    (properties `((upstream-name . "multilaterals")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-ape))
    (home-page "https://cran.r-project.org/package=multilaterals")
    (synopsis "Transitive Index Numbers for Cross-Sections and Panel Data")
    (description
     "Computing transitive (and non-transitive) index numbers (Coelli et al., 2005
<doi:10.1007/b136381>) for cross-sections and panel data.  For the calculation
of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao
et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill,
2004 <doi:10.1257/0002828043052178>) methods are implemented.  Traditional
fixed-base and chained indexes, and their growth rates, can also be derived
using the Paasche, Laspeyres, Fisher and Tornqvist formulas.")
    (license license:gpl3)))

(define-public r-multikink
  (package
    (name "r-multikink")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiKink" version))
       (sha256
        (base32 "14npzvidqiy25ldg2g4vyj6bbrmi9vbpswbm9ah9hc4sdjdq344h"))))
    (properties `((upstream-name . "MultiKink")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg r-pracma r-matrix r-gam))
    (home-page "https://cran.r-project.org/package=MultiKink")
    (synopsis "Estimation and Inference for Multi-Kink Quantile Regression")
    (description
     "Estimation and inference for multiple kink quantile regression for longitudinal
data and the i.i.d data.  A bootstrap restarting iterative segmented quantile
algorithm is proposed to estimate the multiple kink quantile regression model
conditional on a given number of change points.  The number of kinks is also
allowed to be unknown.  In such case, the backward elimination algorithm and the
bootstrap restarting iterative segmented quantile algorithm are combined to
select the number of change points based on a quantile BIC. For longitudinal
data, we also develop the GEE estimator to incorporate the within-subject
correlations.  A score-type based test statistic is also developed for testing
the existence of kink effect.  The package is based on the paper, ``Wei Zhong,
Chuang Wan and Wenyang Zhang (2022).  Estimation and inference for multikink
quantile regression, JBES and ``Chuang Wan, Wei Zhong, Wenyang Zhang and
Changliang Zou (2022).  Multi-kink quantile regression for longitudinal data
with application to progesterone data analysis, Biometrics\".")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-multijoin
  (package
    (name "r-multijoin")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiJoin" version))
       (sha256
        (base32 "0cfk6yc5jagj9y8xybw6yg0yph4zah2vzaxv0384ad6r0r3x3nm8"))))
    (properties `((upstream-name . "MultiJoin")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MultiJoin")
    (synopsis
     "Enables Efficient Joining of Data File on Common Fields using the Unix Utility Join")
    (description
     "Wrapper around the Unix join facility which is more efficient than the built-in
R routine merge().  The package enables the joining of multiple files on disk at
once.  The files can be compressed and various filters can be deployed before
joining.  Compiles only under Unix.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-multigroupsequential
  (package
    (name "r-multigroupsequential")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiGroupSequential" version))
       (sha256
        (base32 "0mx0a10260ks77njxf7yhxwhw66b7rjlqkjxmidvxbl6r0jfgr5b"))))
    (properties `((upstream-name . "MultiGroupSequential")))
    (build-system r-build-system)
    (propagated-inputs (list r-openmx r-hommel))
    (home-page "https://cran.r-project.org/package=MultiGroupSequential")
    (synopsis "Group-Sequential Procedures with Multiple Hypotheses")
    (description
     "It is often challenging to strongly control the family-wise type-1 error rate in
the group-sequential trials with multiple endpoints (hypotheses).  The inflation
of type-1 error rate comes from two sources (S1) repeated testing individual
hypothesis and (S2) simultaneous testing multiple hypotheses.  The
@code{MultiGroupSequential} package is intended to help researchers to tackle
this challenge.  The procedures provided include the sequential procedures
described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the
graphical procedure proposed by Maurer and Bretz (2013)
<doi:10.1080/19466315.2013.807748>.  Luo and Quan (2013) describes three
procedures, and the functions to implement these procedures are (1) seqgspgx()
implements a sequential graphical procedure based on the group-sequential
p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based
on the group-sequential p-values; and (3) seqqvalhh() implements a sequential
Hochberg/Hommel procedure based on the q-values.  In addition, seqmbgx()
implements the sequential graphical procedure described in Maurer and Bretz
(2013).")
    (license license:gpl2+)))

(define-public r-multigroupo
  (package
    (name "r-multigroupo")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiGroupO" version))
       (sha256
        (base32 "0yiia0zf54hwr9gd2jisasmg996w1ajnak1i1ls5dl2bpfic31gn"))))
    (properties `((upstream-name . "MultiGroupO")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist
                             r-qgraph
                             r-plsgenomics
                             r-mvtnorm
                             r-mgm
                             r-lemon
                             r-gridextra
                             r-gplots
                             r-ggrepel
                             r-ggplot2
                             r-expm
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultiGroupO")
    (synopsis "MultiGroup Method and Simulation Data Analysis")
    (description
     "Two method new of multigroup and simulation of data.  The first technique called
multigroup PCA (@code{mgPCA}) this multivariate exploration approach that has
the idea of considering the structure of groups and / or different types of
variables.  On the other hand, the second multivariate technique called
Multigroup Dimensionality Reduction (MDR) it is another multivariate exploration
method that is based on projections.  In addition, a method called Single
Dimension Exploration (SDE) was incorporated for to analyze the exploration of
the data.  It could help us in a better way to observe the behavior of the
multigroup data with certain variables of interest.")
    (license license:gpl3)))

(define-public r-multigraphr
  (package
    (name "r-multigraphr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multigraphr" version))
       (sha256
        (base32 "13ac16zghccdapr0kmkcrj11b0ipgaajdpps867sgk6yqd7pyizx"))))
    (properties `((upstream-name . "multigraphr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/termehs/multigraphr")
    (synopsis
     "Probability Models and Statistical Analysis of Random Multigraphs")
    (description
     "This package provides methods and models for analysing multigraphs as introduced
by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local
and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit
tests.")
    (license license:expat)))

(define-public r-multigraph
  (package
    (name "r-multigraph")
    (version "0.99-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multigraph" version))
       (sha256
        (base32 "0q634jsr1m9nzn4g3bcy5dxl26bdzdg6imcym2jifaa29b7m1790"))))
    (properties `((upstream-name . "multigraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-multiplex))
    (home-page "https://github.com/mplex/multigraph/")
    (synopsis "Plot and Manipulate Multigraphs")
    (description
     "This package provides functions to plot and manipulate multigraphs, signed and
valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with
various layout options.")
    (license license:gpl3)))

(define-public r-multiglarmavarsel
  (package
    (name "r-multiglarmavarsel")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiGlarmaVarSel" version))
       (sha256
        (base32 "1cpxy716ca7xa6y7sjrrssx6r4wc1fr2p0i5im5s745xlc66qi06"))))
    (properties `((upstream-name . "MultiGlarmaVarSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-glmnet r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultiGlarmaVarSel")
    (synopsis "Variable Selection in Sparse Multivariate GLARMA Models")
    (description
     "This package performs variable selection in high-dimensional sparse GLARMA
models.  For further details we refer the reader to the paper Gomtsyan et al.
(2022), <@code{arXiv:2208.14721>}.")
    (license license:gpl2)))

(define-public r-multifwf
  (package
    (name "r-multifwf")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multifwf" version))
       (sha256
        (base32 "1l6z3pzz6g6w1spp1f918jh6w0jm93qyc882rj8jhn1198d2s8nd"))))
    (properties `((upstream-name . "multifwf")))
    (build-system r-build-system)
    (home-page "https://github.com/prontog/multifwf")
    (synopsis
     "Read Fixed Width Format Files Containing Lines of Different Type")
    (description
     "Read a table of fixed width formatted data of different types into a data.frame
for each type.")
    (license license:gpl2+)))

(define-public r-multifunc
  (package
    (name "r-multifunc")
    (version "0.9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multifunc" version))
       (sha256
        (base32 "0gxgxfh8vmczc7dc4awfmmmwds98kk9d5c9rk1lz1himp19mqsj4"))))
    (properties `((upstream-name . "multifunc")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-mass r-magrittr r-dplyr r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://jebyrnes.github.io/multifunc/")
    (synopsis "Analysis of Ecological Drivers on Ecosystem Multifunctionality")
    (description
     "This package provides methods for the analysis of how ecological drivers affect
the multifunctionality of an ecosystem based on methods of Byrnes et al.  2016
<doi:10.1111/2041-210X.12143> and Byrnes et al.  2022
<doi:10.1101/2022.03.17.484802>.  Most standard methods in the literature are
implemented (see vignettes) in a tidy format.")
    (license license:expat)))

(define-public r-multifit
  (package
    (name "r-multifit")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiFit" version))
       (sha256
        (base32 "086bnqp61mfanrhi7dqaq5j2ddg3rfiyms0f5wj34svcjal9wjvc"))))
    (properties `((upstream-name . "MultiFit")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultiFit")
    (synopsis
     "Multiscale Fisher's Independence Test for Multivariate Dependence")
    (description
     "Test for independence of two random vectors, learn and report the dependency
structure.  For more information, see Gorsky, Shai and Li Ma, Multiscale
Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted,
January 2022.")
    (license license:cc0)))

(define-public r-multifear
  (package
    (name "r-multifear")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multifear" version))
       (sha256
        (base32 "0240dk3cqdm9x9y139p5i0gj374f58qxnqw32cii1abhv852ijfm"))))
    (properties `((upstream-name . "multifear")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-reshape2
                             r-purrr
                             r-nlme
                             r-ggplot2
                             r-forestplot
                             r-ez
                             r-esc
                             r-effsize
                             r-effectsize
                             r-dplyr
                             r-broom
                             r-bootstrap
                             r-bayestestr
                             r-bayesfactor))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AngelosPsy/multifear")
    (synopsis "Multiverse Analyses for Conditioning Data")
    (description
     "This package provides a suite of functions for performing analyses, based on a
multiverse approach, for conditioning data.  Specifically, given the appropriate
data, the functions are able to perform t-tests, analyses of variance, and mixed
models for the provided data and return summary statistics and plots.  The
function is also able to return for all those tests p-values, confidence
intervals, and Bayes factors.  The methods are described in Lonsdorf, Gerlicher,
Klingelhofer-Jens, & Krypotos (2022) <doi:10.1016/j.brat.2022.104072>.")
    (license license:gpl3)))

(define-public r-multifanova
  (package
    (name "r-multifanova")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiFANOVA" version))
       (sha256
        (base32 "03dx08rpp2h6mwbdcsgsmyjmbslva3578yif298zc9r7v5j6d0rr"))))
    (properties `((upstream-name . "multiFANOVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix
                             r-mass
                             r-gfdmcv
                             r-foreach
                             r-fda
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=multiFANOVA")
    (synopsis "Multiple Contrast Tests for Functional Data")
    (description
     "The provided package implements multiple contrast tests for functional data
(Munko et al., 2023, <@code{arXiv:2306.15259>}).  These procedures enable us to
evaluate the overall hypothesis regarding equality, as well as specific
hypotheses defined by contrasts.  In particular, we can perform post hoc tests
to examine particular comparisons of interest.  Different experimental designs
are supported, e.g., one-way and multi-way analysis of variance for functional
data.")
    (license (list license:lgpl2.0 license:lgpl3 license:gpl2 license:gpl3))))

(define-public r-multifamm
  (package
    (name "r-multifamm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multifamm" version))
       (sha256
        (base32 "09s6hnyi5x48yp97499zai7b959ylkk17zbbc6d8a71ynvfas8ir"))))
    (properties `((upstream-name . "multifamm")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-sparseflmm
                             r-mgcv
                             r-mfpca
                             r-fundata
                             r-data-table))
    (home-page "https://cran.r-project.org/package=multifamm")
    (synopsis "Multivariate Functional Additive Mixed Models")
    (description
     "An implementation for multivariate functional additive mixed models
(@code{multiFAMM}), see Volkmann et al. (2021, <@code{arXiv:2103.06606>}).  It
builds on developed methods for univariate sparse functional regression models
and multivariate functional principal component analysis.  This package contains
the function to run a @code{multiFAMM} and some convenience functions useful
when working with large models.  An additional package on @code{GitHub} contains
more convenience functions to reproduce the analyses of the corresponding paper
(<https://github.com/alexvolkmann/@code{multifammPaper>}).")
    (license license:gpl2+)))

(define-public r-multidimbio
  (package
    (name "r-multidimbio")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiDimBio" version))
       (sha256
        (base32 "0xadpvnyghpqh2q1gp1fqwv591sm3icmx60x4g8gn1vybi51k5rx"))))
    (properties `((upstream-name . "multiDimBio")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-pcamethods
                             r-misc3d
                             r-mass
                             r-lme4
                             r-gridgraphics
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=multiDimBio")
    (synopsis "Multivariate Analysis and Visualization for Biological Data")
    (description
     "Code to support a systems biology research program from inception through
publication.  The methods focus on dimension reduction approaches to detect
patterns in complex, multivariate experimental data and places an emphasis on
informative visualizations.  The goal for this project is to create a package
that will evolve over time, thereby remaining relevant and reflective of current
methods and techniques.  As a result, we encourage suggested additions to the
package, both methodological and graphical.")
    (license license:gpl3+)))

(define-public r-multid
  (package
    (name "r-multid")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multid" version))
       (sha256
        (base32 "0nx8gfsypg00v1hvqzjsbka86j8d1m5nl1y34ir425byzr26gx9v"))))
    (properties `((upstream-name . "multid")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg
                             r-proc
                             r-lmertest
                             r-lme4
                             r-lavaan
                             r-glmnet
                             r-ggpubr
                             r-ggplot2
                             r-emmeans
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=multid")
    (synopsis "Multivariate Difference Between Two Groups")
    (description
     "Estimation of multivariate differences between two groups (e.g., multivariate
sex differences) with regularized regression methods and predictive approach.
See LÃ¶nnqvist & Ilmarinen (2021) <doi:10.1007/s11109-021-09681-2> and Ilmarinen
et al. (2023) <doi:10.1177/08902070221088155>.  Includes tools that help in
understanding difference score reliability, predictions of difference score
variables, conditional intra-class correlations, and heterogeneity of variance
estimates.  Package development was supported by the Academy of Finland research
grant 338891.")
    (license license:gpl3)))

(define-public r-multicoll
  (package
    (name "r-multicoll")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiColl" version))
       (sha256
        (base32 "1dq1s1l1q720hkymdwxh9gizg9qjbinq762jvq9f3p5sqhbx2fhf"))))
    (properties `((upstream-name . "multiColl")))
    (build-system r-build-system)
    (home-page "http://colldetreat.r-forge.r-project.org/")
    (synopsis "Collinearity Detection in a Multiple Linear Regression Model")
    (description
     "The detection of worrying approximate collinearity in a multiple linear
regression model is a problem addressed in all existing statistical packages.
However, we have detected deficits regarding to the incorrect treatment of
qualitative independent variables and the role of the intercept of the model.
The objective of this package is to correct these deficits.  In this package
will be available detection and treatment techniques traditionally used as the
recently developed.")
    (license license:gpl2+)))

(define-public r-multicoap
  (package
    (name "r-multicoap")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiCOAP" version))
       (sha256
        (base32 "1209pv52lgshfk67nfkp4fq4fm7k9g5ly2yilrzwihywsym8yxmp"))))
    (properties `((upstream-name . "MultiCOAP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-irlba))
    (home-page "https://github.com/feiyoung/MultiCOAP")
    (synopsis
     "High-Dimensional Covariate-Augmented Overdispersed Multi-Study Poisson Factor Model")
    (description
     "We introduce factor models designed to jointly analyze high-dimensional count
data from multiple studies by extracting study-shared and specified factors.
Our factor models account for heterogeneous noises and overdispersion among
counts with augmented covariates.  We propose an efficient and speedy
variational estimation procedure for estimating model parameters, along with a
novel criterion for selecting the optimal number of factors and the rank of
regression coefficient matrix.  More details can be referred to Liu et al.
(2024) <doi:10.48550/@code{arXiv.2402.15071>}.")
    (license license:gpl3)))

(define-public r-multicmp
  (package
    (name "r-multicmp")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multicmp" version))
       (sha256
        (base32 "0mz7ksc5h6dy95l3kbsamm191372blhlrj573krc57bj93lmp1a5"))))
    (properties `((upstream-name . "multicmp")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv))
    (home-page "http://dx.doi.org/10.1016/j.jmva.2016.04.007")
    (synopsis
     "Flexible Modeling of Multivariate Count Data via the Multivariate Conway-Maxwell-Poisson Distribution")
    (description
     "This package provides a toolkit containing statistical analysis models motivated
by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution
for flexible modeling of multivariate count data, especially in the presence of
data dispersion.  Currently the package only supports bivariate data, via the
bivariate COM-Poisson distribution described in Sellers et al. (2016)
<doi:10.1016/j.jmva.2016.04.007>.  Future development will extend the package to
higher-dimensional data.")
    (license license:gpl3)))

(define-public r-multiclassroc
  (package
    (name "r-multiclassroc")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiClassROC" version))
       (sha256
        (base32 "06ns1yl16yxaksiv1mjwzkixc03mzz4rlidkzan0nxi9fqfp7nvc"))))
    (properties `((upstream-name . "MultiClassROC")))
    (build-system r-build-system)
    (propagated-inputs (list r-proc r-ggplot2))
    (home-page "https://cran.r-project.org/package=MultiClassROC")
    (synopsis "ROC Curves for Multi-Class Analysis")
    (description
     "Function multiroc() can be used for computing and visualizing Receiver Operating
Characteristics (ROC) and Area Under the Curve (AUC) for multi-class
classification problems.  It supports both One-vs-One approach by M.Bishop, C.
(2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012,
ISBN:9780262018029).")
    (license license:gpl3)))

(define-public r-multiclasspairs
  (package
    (name "r-multiclasspairs")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiclassPairs" version))
       (sha256
        (base32 "0mz15dl3fiznbnfjjmnhr9z6qh43mwafnkisd7svc3cb0lrbbjci"))))
    (properties `((upstream-name . "multiclassPairs")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdist
                             r-ranger
                             r-e1071
                             r-dunn-test
                             r-caret
                             r-boruta))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/NourMarzouka/multiclassPairs")
    (synopsis "Build MultiClass Pair-Based Classifiers using TSPs or RF")
    (description
     "This package provides a toolbox to train a single sample classifier that uses
in-sample feature relationships.  The relationships are represented as feature1
< feature2 (e.g. gene1 < gene2).  We provide two options to go with.  First is
based on @code{switchBox} package which uses Top-score pairs algorithm.  Second
is a novel implementation based on random forest algorithm.  For simple problems
we recommend to use one-vs-rest using TSP option due to its simplicity and for
being easy to interpret.  For complex problems RF performs better.  Both lines
filter the features first then combine the filtered features to make the list of
all the possible rules (i.e.  rule1: feature1 < feature2, rule2: feature1 <
feature3, etc...).  Then the list of rules will be filtered and the most
important and informative rules will be kept.  The informative rules will be
assembled in an one-vs-rest model or in an RF model.  We provide a detailed
description with each function in this package to explain the filtration and
training methodology in each line.  Reference: Marzouka & Eriksson (2021)
<doi:10.1093/bioinformatics/btab088>.")
    (license license:gpl2+)))

(define-public r-multichull
  (package
    (name "r-multichull")
    (version "3.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multichull" version))
       (sha256
        (base32 "17c2hwgnlw67s2209ran6gz0mdg66kzxzp6d484dp53gggnpdw0l"))))
    (properties `((upstream-name . "multichull")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes r-shiny r-plotly r-igraph r-dt))
    (home-page "https://cran.r-project.org/package=multichull")
    (synopsis "Generic Convex-Hull-Based Model Selection Method")
    (description
     "Given a set of models for which a measure of model (mis)fit and model complexity
is provided, CHull(), developed by Ceulemans and Kiers (2006)
<doi:10.1348/000711005X64817>, determines the models that are located on the
boundary of the convex hull and selects an optimal model by means of the scree
test values.")
    (license license:gpl2+)))

(define-public r-multicastr
  (package
    (name "r-multicastr")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multicastR" version))
       (sha256
        (base32 "0zxv3kxk8gh2fgllzzg7k625n3p4q5pr465ja0mhvs48m4n262l9"))))
    (properties `((upstream-name . "multicastR")))
    (build-system r-build-system)
    (home-page "https://multicast.aspra.uni-bamberg.de/")
    (synopsis "Companion to the Multi-CAST Collection")
    (description
     "This package provides a basic interface for accessing annotation data from the
Multi-CAST collection, a database of spoken natural language texts edited by
Geoffrey Haig and Stefan Schnell.  The collection draws from a diverse set of
languages and has been annotated across multiple levels.  Annotation data is
downloaded on request from the servers of the University of Bamberg.  See the
Multi-CAST website <https://multicast.aspra.uni-bamberg.de/> for more
information and a list of related publications.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-multica
  (package
    (name "r-multica")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiCA" version))
       (sha256
        (base32 "1vgb13cfq10g8hxykgi3is3mrlbm76vh40cznapl7xxmw226ccrl"))))
    (properties `((upstream-name . "multiCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-multcomp r-bitops))
    (home-page "https://cran.r-project.org/package=multiCA")
    (synopsis "Multinomial Cochran-Armitage Trend Test")
    (description
     "This package implements a generalization of the Cochran-Armitage trend test to
multinomial data.  In addition to an overall test, multiple testing adjusted
p-values for trend in individual outcomes and power calculation is available.")
    (license license:gpl2+)))

(define-public r-multibridge
  (package
    (name "r-multibridge")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multibridge" version))
       (sha256
        (base32 "0akl9i3fhgd0pqsvgp7cv2l2l84cqc3cc4lambnjmwdyh7x4qcvd"))))
    (properties `((upstream-name . "multibridge")))
    (build-system r-build-system)
    (inputs (list mpfr gmp))
    (propagated-inputs (list r-stringr
                             r-rdpack
                             r-rcpp
                             r-purrr
                             r-progress
                             r-mvtnorm
                             r-magrittr
                             r-coda
                             r-brobdingnag))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://github.com/asarafoglou/multibridge/")
    (synopsis "Evaluating Multinomial Order Restrictions with Bridge Sampling")
    (description
     "Evaluate hypotheses concerning the distribution of multinomial proportions using
bridge sampling.  The bridge sampling routine is able to compute Bayes factors
for hypotheses that entail inequality constraints, equality constraints, free
parameters, and mixtures of all three.  These hypotheses are tested against the
encompassing hypothesis, that all parameters vary freely or against the null
hypothesis that all category proportions are equal.  For more information see
Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.")
    (license license:gpl2)))

(define-public r-multibreaker
  (package
    (name "r-multibreaker")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multibreakeR" version))
       (sha256
        (base32 "1rg5k2ldaf5ygnrjpfk28gqqs7d8cfjq9cm5zli47yavqm43mq78"))))
    (properties `((upstream-name . "multibreakeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rlang r-reshape2 r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/loicym/multibreakeR")
    (synopsis "Tests for a Structural Change in Multivariate Time Series")
    (description
     "Flexible implementation of a structural change point detection algorithm for
multivariate time series.  It authorizes inclusion of trends, exogenous
variables, and break test on the intercept or on the full vector autoregression
system.  Bai, Lumsdaine, and Stock (1998) <doi:10.1111/1467-937X.00051>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-multiblock
  (package
    (name "r-multiblock")
    (version "0.8.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiblock" version))
       (sha256
        (base32 "05z6zf9vh94as4m2si5kam101swhyfyv2vm0k9js1pxakwziavkq"))))
    (properties `((upstream-name . "multiblock")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssbtools
                             r-rspectra
                             r-rcppeigen
                             r-rcpp
                             r-progress
                             r-pracma
                             r-plsvarsel
                             r-pls
                             r-plotrix
                             r-mixlm
                             r-mass
                             r-lme4
                             r-car
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://khliland.github.io/multiblock/")
    (synopsis "Multiblock Data Fusion in Statistics and Machine Learning")
    (description
     "This package provides functions and datasets to support Smilde, NÃ¦s and Liland
(2021, ISBN: 978-1-119-60096-1) \"Multiblock Data Fusion in Statistics and
Machine Learning - Applications in the Natural and Life Sciences\".  This
implements and imports a large collection of methods for multiblock data
analysis with common interfaces, result- and plotting functions, several real
data sets and six vignettes covering a range different applications.")
    (license license:gpl2+)))

(define-public r-multibiplotgui
  (package
    (name "r-multibiplotgui")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multibiplotGUI" version))
       (sha256
        (base32 "08vmdhrn6qqq57q818cqnmsw7qaa4gnw3m5myzp4dc85njgzzi02"))))
    (properties `((upstream-name . "multibiplotGUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-tkrplot
                             r-tcltk2
                             r-shapes
                             r-rgl
                             r-plotrix
                             r-matrix
                             r-mass
                             r-dendroextras
                             r-cluster))
    (home-page "https://cran.r-project.org/package=multibiplotGUI")
    (synopsis "Multibiplot Analysis in R")
    (description
     "This package provides a GUI with which users can construct and interact with
Multibiplot Analysis.")
    (license license:gpl2+)))

(define-public r-multibiasmeta
  (package
    (name "r-multibiasmeta")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multibiasmeta" version))
       (sha256
        (base32 "04qnb3l2y86xy8f1j754g8fy6hc1a26rwdznaka789k3dkkckk93"))))
    (properties `((upstream-name . "multibiasmeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-robumeta
                             r-rlang
                             r-rdpack
                             r-purrr
                             r-metafor
                             r-metabias
                             r-evalue
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mathurlabstanford/multibiasmeta")
    (synopsis "Sensitivity Analysis for Multiple Biases in Meta-Analyses")
    (description
     "Meta-analyses can be compromised by studies internal biases (e.g., confounding
in nonrandomized studies) as well as by publication bias.  This package conducts
sensitivity analyses for the joint effects of these biases (per Mathur (2022)
<doi:10.31219/osf.io/u7vcb>).  These sensitivity analyses address two questions:
(1) For a given severity of internal bias across studies and of publication
bias, how much could the results change?; and (2) For a given severity of
publication bias, how severe would internal bias have to be, hypothetically, to
attenuate the results to the null or by a given amount?")
    (license license:expat)))

(define-public r-multibias
  (package
    (name "r-multibias")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multibias" version))
       (sha256
        (base32 "0hqcb3adzl3m6cyvj2khqdpvj5akkwr4vsbas8v88476mp48q1wn"))))
    (properties `((upstream-name . "multibias")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-lifecycle r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pcbrendel/multibias")
    (synopsis "Simultaneous Multi-Bias Adjustment")
    (description
     "Quantify the causal effect of a binary exposure on a binary outcome with
adjustment for multiple biases.  The functions can simultaneously adjust for any
combination of uncontrolled confounding, exposure/outcome misclassification, and
selection bias.  The underlying method generalizes the concept of combining
inverse probability of selection weighting with predictive value weighting.
Simultaneous multi-bias analysis can be used to enhance the validity and
transparency of real-world evidence obtained from observational, longitudinal
studies.  Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi
Arah (2023) <doi:10.1093/ije/dyad001>.")
    (license license:expat)))

(define-public r-multibd
  (package
    (name "r-multibd")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiBD" version))
       (sha256
        (base32 "1qjfr8hx8sr5m8370yzi09r8jnh3i6907wzj3v4h83vmjh81db71"))))
    (properties `((upstream-name . "MultiBD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultiBD")
    (synopsis "Multivariate Birth-Death Processes")
    (description
     "Computationally efficient functions to provide direct likelihood-based inference
for partially-observed multivariate birth-death processes.  Such processes range
from a simple Yule model to the complex susceptible-infectious-removed model in
disease dynamics.  Efficient likelihood evaluation facilitates maximum
likelihood estimation and Bayesian inference.")
    (license license:asl2.0)))

(define-public r-multiatsm
  (package
    (name "r-multiatsm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultiATSM" version))
       (sha256
        (base32 "0a52vkyydlvimqc9x6plx71k6fk639am57l54xbxdnzd5l899zql"))))
    (properties `((upstream-name . "MultiATSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-wrapr r-pracma r-hablar r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MultiATSM")
    (synopsis "Multicountry Term Structure of Interest Rates Models")
    (description
     "Estimation routines for several classes of affine term structure of interest
rates models.  All the models are based on the single-country unspanned
macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF)
<doi:10.1111/jofi.12131>.  Multicountry extensions such as the ones of
Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>,
Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon
and Moura (Forthcoming, JFEC) <doi:10.1093/jjfinec/nbae008> are also available.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-multiassetoptions
  (package
    (name "r-multiassetoptions")
    (version "0.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiAssetOptions" version))
       (sha256
        (base32 "0ci17bjl7k2id7grlynisvjfmmgrmq5f5lf8kh5xbq70gmvxh7v9"))))
    (properties `((upstream-name . "multiAssetOptions")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://cran.r-project.org/package=multiAssetOptions")
    (synopsis "Finite Difference Method for Multi-Asset Option Valuation")
    (description
     "Efficient finite difference method for valuing European and American multi-asset
options.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-multiapply
  (package
    (name "r-multiapply")
    (version "2.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiApply" version))
       (sha256
        (base32 "0yc63phci4xxb3ja3mi03ckdw9za3dwq2llc681wism1yfm26wlh"))))
    (properties `((upstream-name . "multiApply")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-foreach r-doparallel))
    (home-page "https://earth.bsc.es/gitlab/ces/multiApply")
    (synopsis "Apply Functions to Multiple Multidimensional Arrays or Vectors")
    (description
     "The base apply function and its variants, as well as the related functions in
the plyr package, typically apply user-defined functions to a single argument
(or a list of vectorized arguments in the case of mapply).  The
@code{multiApply} package extends this paradigm with its only function, Apply,
which efficiently applies functions taking one or a list of multiple
unidimensional or multidimensional arrays (or combinations thereof) as input.
The input arrays can have different numbers of dimensions as well as different
dimension lengths, and the applied function can return one or a list of
unidimensional or multidimensional arrays as output.  This saves development
time by preventing the R user from writing often error-prone and
memory-inefficient loops dealing with multiple complex arrays.  Also, a
remarkable feature of Apply is the transparent use of multi-core through its
parameter ncores'.  In contrast to the base apply function, this package
suggests the use of target dimensions as opposite to the margins for specifying
the dimensions relevant to the function to be applied.")
    (license license:gpl3)))

(define-public r-multiactionbutton
  (package
    (name "r-multiactionbutton")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multiActionButton" version))
       (sha256
        (base32 "1yc111l0ah6ldh5fh1nd3fy9qxyzzszj02w26nqm49c3q0058f15"))))
    (properties `((upstream-name . "multiActionButton")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (home-page "https://github.com/stla/multiActionButton")
    (synopsis "Multi Action Button for 'Shiny' Applications")
    (description
     "This package provides a multi action button for usage in shiny applications.")
    (license license:gpl3)))

(define-public r-multgee
  (package
    (name "r-multgee")
    (version "1.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multgee" version))
       (sha256
        (base32 "0hjlnmnn18cf2blcr23y8vc2l6zaqfi8223nfwm2bx3arslj41xn"))))
    (properties `((upstream-name . "multgee")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-rcpparmadillo r-rcpp r-gnm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AnestisTouloumis/multgee")
    (synopsis
     "GEE Solver for Correlated Nominal or Ordinal Multinomial Responses")
    (description
     "GEE solver for correlated nominal or ordinal multinomial responses using a local
odds ratios parameterization.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-multfisher
  (package
    (name "r-multfisher")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multfisher" version))
       (sha256
        (base32 "0vzvq7v2xz35fx8pg25c9xqkic09k6fcg9zh81j7pgqmzi2wwrp8"))))
    (properties `((upstream-name . "multfisher")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=multfisher")
    (synopsis "Optimal Exact Tests for Multiple Binary Endpoints")
    (description
     "Calculates exact hypothesis tests to compare a treatment and a reference group
with respect to multiple binary endpoints.  The tested null hypothesis is an
identical multidimensional distribution of successes and failures in both
groups.  The alternative hypothesis is a larger success proportion in the
treatment group in at least one endpoint.  The tests are based on the
multivariate permutation distribution of subjects between the two groups.  For
this permutation distribution, rejection regions are calculated that satisfy one
of different possible optimization criteria.  In particular, regions with
maximal exhaustion of the nominal significance level, maximal power under a
specified alternative or maximal number of elements can be found.  Optimization
is achieved by a branch-and-bound algorithm.  By application of the closed
testing principle, the global hypothesis tests are extended to multiple testing
procedures.")
    (license license:gpl3)))

(define-public r-multeq
  (package
    (name "r-multeq")
    (version "2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultEq" version))
       (sha256
        (base32 "0x0mfmrfcjdv7v5nlakmjh2ilmgn15xgzc4qgm6djkif6565v46i"))))
    (properties `((upstream-name . "MultEq")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MultEq")
    (synopsis
     "Multiple Equivalence Tests and Simultaneous Confidence Intervals")
    (description
     "Equivalence tests and related confidence intervals for the comparison of two
treatments, simultaneously for one or many normally distributed, primary
response variables (endpoints).  The step-up procedure of Quan et al. (2001) is
both applied for differences and extended to ratios of means.  A related
single-step procedure is also available.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-multe
  (package
    (name "r-multe")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multe" version))
       (sha256
        (base32 "1827787d3prv94asyqw4h91z7w9k5yzp0d79afxrnp3b9q9vp5v3"))))
    (properties `((upstream-name . "multe")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kolesarm/multe")
    (synopsis "Multiple Treatment Effects Regression")
    (description
     "This package implements contamination bias diagnostics and alternative
estimators for regressions with multiple treatments.  The implementation is
based on Goldsmith-Pinkham, Hull, and KolesÃ¡r (2024)
<doi:10.48550/@code{arXiv.2106.05024>}.")
    (license license:expat)))

(define-public r-multdm
  (package
    (name "r-multdm")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multDM" version))
       (sha256
        (base32 "132kqbrg1ism2byhal50j0hr0qqlnd30db9z6vvjc977d03hf41l"))))
    (properties `((upstream-name . "multDM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mts))
    (home-page "https://CRAN.R-project.org/package=multDM")
    (synopsis "Multivariate Version of the Diebold-Mariano Test")
    (description
     "Allows to perform the multivariate version of the Diebold-Mariano test for equal
predictive ability of multiple forecast comparison.  Main reference: Mariano,
R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.")
    (license license:gpl3)))

(define-public r-multbxxc
  (package
    (name "r-multbxxc")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "multbxxc" version))
       (sha256
        (base32 "0pb8pf66jribf1riyzsv9snmhvf3vivzklkmfhzadgrs72sxlgw4"))))
    (properties `((upstream-name . "multbxxc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmumps r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/sgsokol/influx/")
    (synopsis "Auxiliary Routines for Influx Software")
    (description
     "This package contains auxiliary routines for influx software.  This packages is
not intended to be used directly.  Influx was published here: Sokol et al.
(2012) <doi:10.1093/bioinformatics/btr716>.")
    (license license:gpl2+)))

(define-public r-multbiplotr
  (package
    (name "r-multbiplotr")
    (version "23.11.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultBiplotR" version))
       (sha256
        (base32 "0qcf97r9f5l572dckfvi9pjl2gvc6zw36z34pbbfwxha8bnqiyqc"))))
    (properties `((upstream-name . "MultBiplotR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-threeway
                             r-scales
                             r-psych
                             r-polycor
                             r-mvtnorm
                             r-mirt
                             r-mass
                             r-lattice
                             r-knitr
                             r-hmisc
                             r-gplots
                             r-gparotation
                             r-geometry
                             r-dunn-test
                             r-deldir
                             r-dae
                             r-car))
    (home-page "https://cran.r-project.org/package=MultBiplotR")
    (synopsis "Multivariate Analysis Using Biplots in R")
    (description
     "Several multivariate techniques from a biplot perspective.  It is the
translation (with many improvements) into R of the previous package developed in
Matlab'.  The package contains some of the main developments of my team during
the last 30 years together with some more standard techniques.  Package
includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots,
Correspondence Analysis, Canonical Correspondence Analysis, Canonical
STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional
Unfolding, External Biplots for Principal Coordinates Analysis or
Multidimensional Scaling, among many others.  References can be found in the
help of each procedure.")
    (license license:gpl2+)))

(define-public r-multalloc
  (package
    (name "r-multalloc")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MultAlloc" version))
       (sha256
        (base32 "0c3sqfaa08s8mk4yz77kh6q6v9ic5xp52g9prfw1k2kv4nw1k2qd"))))
    (properties `((upstream-name . "MultAlloc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rglpk))
    (home-page "https://cran.r-project.org/package=MultAlloc")
    (synopsis "Optimal Allocation in Stratified Sampling")
    (description
     "Integer Programming Formulations Applied to Univariate and Multivariate
Allocation Problems.")
    (license license:gpl2)))

(define-public r-mult-latent-reg
  (package
    (name "r-mult-latent-reg")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mult.latent.reg" version))
       (sha256
        (base32 "0cppmlk7dfbs4h1z83vkmp6y1yr078ky2hb4lbpi8xlycjaydc4k"))))
    (properties `((upstream-name . "mult.latent.reg")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrixstats r-lme4))
    (home-page "https://cran.r-project.org/package=mult.latent.reg")
    (synopsis "Regression and Clustering in Multivariate Response Scenarios")
    (description
     "Fitting multivariate response models with random effects on one or two levels;
whereby the (one-dimensional) random effect represents a latent variable
approximating the multivariate space of outcomes, after possible adjustment for
covariates.  The method is particularly useful for multivariate, highly
correlated outcome variables with unobserved heterogeneities.  Applications
include regression with multivariate responses, as well as multivariate
clustering or ranking problems.  See Zhang and Einbeck (2024)
<doi:10.1007/s42519-023-00357-0>.")
    (license license:gpl3)))

(define-public r-mulset
  (package
    (name "r-mulset")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mulset" version))
       (sha256
        (base32 "0pigiy6hfiqh6x4zx6zqn3ykk36s3abzzyb67jdlf4jzc5zhrl9g"))))
    (properties `((upstream-name . "mulset")))
    (build-system r-build-system)
    (propagated-inputs (list r-gtools r-digest))
    (home-page "https://github.com/LogIN-/mulset")
    (synopsis "Multiset Intersection Generator")
    (description
     "Computes efficient data distributions from highly inconsistent datasets with
many missing values using multi-set intersections.  Based upon hash functions,
mulset can quickly identify intersections from very large matrices of input
vectors across columns and rows and thus provides scalable solution for dealing
with missing values.  Tomic et al. (2019) <doi:10.1101/545186>.")
    (license (license:fsdg-compatible "EUPL (>= 1.2)"))))

(define-public r-mulsem
  (package
    (name "r-mulsem")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mulSEM" version))
       (sha256
        (base32 "00lx8w3xxm7zdwv07pc3z5vsl4l1rj95s9wwil9y55bpf2ih4yp2"))))
    (properties `((upstream-name . "mulSEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-openmx))
    (home-page "https://github.com/mikewlcheung/mulsem")
    (synopsis "Some Multivariate Analyses using Structural Equation Modeling")
    (description
     "This package provides a set of functions for some multivariate analyses
utilizing a structural equation modeling (SEM) approach through the
@code{OpenMx} package.  These analyses include canonical correlation analysis
(CANCORR), redundancy analysis (RDA), and multivariate principal component
regression (MPCR).  It implements procedures discussed in Gu and Cheung (2023)
<doi:10.1111/bmsp.12301>, Gu, Yung, and Cheung (2019)
<doi:10.1080/00273171.2018.1512847>, and Gu et al. (2023)
<doi:10.1080/00273171.2022.2141675>.")
    (license license:gpl2+)))

(define-public r-mully
  (package
    (name "r-mully")
    (version "2.1.38")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mully" version))
       (sha256
        (base32 "1lvyj1kyi9ad2byhc1amrjasc2brv7g4d2i08rf7149vp35cwrsg"))))
    (properties `((upstream-name . "mully")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-randomcolor r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/frankkramer-lab/mully")
    (synopsis "Create, Modify and Visualize Multi-Layered Networks")
    (description
     "Allows the user to create graphs with multiple layers.  The user can also modify
the layers, the nodes, and the edges.  The graph can also be visualized.  Zaynab
Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>.  More about
multilayered graphs and their usage can be found in our review paper: Zaynab
Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.")
    (license license:gpl2+)))

(define-public r-mullerplot
  (package
    (name "r-mullerplot")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MullerPlot" version))
       (sha256
        (base32 "17n3w76a1fqqirshd754pi0p302pi1bld3iv4zi0g7q9ml2q42iw"))))
    (properties `((upstream-name . "MullerPlot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MullerPlot")
    (synopsis
     "Generates Muller Plot from Population/Abundance/Frequency Dynamics Data")
    (description
     "Generates Muller plot from parental/genealogy/phylogeny information and
population/abundance/frequency dynamics data.  Muller plots are plots which
combine information about succession of different OTUs (genotypes, phenotypes,
species, ...) and information about dynamics of their abundances (populations or
frequencies) over time.  They are powerful and fascinating tools to visualize
evolutionary dynamics.  They may be employed also in study of diversity and its
dynamics, i.e.  how diversity emerges and how changes over time.  They are
called Muller plots in honor of Hermann Joseph Muller which used them to explain
his idea of Muller's ratchet (Muller, 1932, American Naturalist).  A big
difference between Muller plots and normal box plots of abundances is that a
Muller plot depicts not only the relative abundances but also succession of OTUs
based on their genealogy/phylogeny/parental relation.  In a Muller plot,
horizontal axis is time/generations and vertical axis represents relative
abundances of OTUs at the corresponding times/generations.  Different OTUs are
usually shown with polygons with different colors and each OTU originates
somewhere in the middle of its parent area in order to illustrate their
succession in evolutionary process.  To generate a Muller plot one needs the
genealogy/phylogeny/parental relation of OTUs and their abundances over time.
@code{MullerPlot} package has the tools to generate Muller plots which clearly
depict the origin of successors of OTUs.")
    (license license:gpl3)))

(define-public r-mulgar
  (package
    (name "r-mulgar")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mulgar" version))
       (sha256
        (base32 "00yaxi14x7g0pzdj65hgs2xm9m7w1w6xjhparbhcjz1jd34ga30y"))))
    (properties `((upstream-name . "mulgar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-purrr
                             r-ggplot2
                             r-geozoo
                             r-dplyr))
    (home-page "https://dicook.github.io/mulgar/")
    (synopsis
     "Functions for Pre-Processing Data for Multivariate Data Visualisation using Tours")
    (description
     "This is a companion to the book Cook, D. and Laa, U. (2023)
<https://dicook.github.io/mulgar_book/> \"Interactively exploring
high-dimensional data and models in R\".  by Cook and Laa.  It contains useful
functions for processing data in preparation for visualising with a tour.  There
are also several sample data sets.")
    (license license:expat)))

(define-public r-mulea
  (package
    (name "r-mulea")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mulea" version))
       (sha256
        (base32 "012g9pjfhzwfcswkjs5wmxdhv72zkxhigsggp95h93vsj4p26xw1"))))
    (properties `((upstream-name . "mulea")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tidygraph
                             r-tibble
                             r-stringi
                             r-scales
                             r-rlang
                             r-readr
                             r-rcpp
                             r-plyr
                             r-magrittr
                             r-ggraph
                             r-ggplot2
                             r-fgsea
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ELTEbioinformatics/mulea")
    (synopsis
     "Enrichment Analysis using Multiple Ontologies and False Discovery Rate")
    (description
     "Background - Traditional gene set enrichment analyses are typically limited to a
few ontologies and do not account for the interdependence of gene sets or terms,
resulting in overcorrected p-values.  To address these challenges, we introduce
mulea, an R package offering comprehensive overrepresentation and functional
enrichment analysis.  Results - mulea employs a progressive empirical false
discovery rate (@code{eFDR}) method, specifically designed for interconnected
biological data, to accurately identify significant terms within diverse
ontologies.  mulea expands beyond traditional tools by incorporating a wide
range of ontologies, encompassing Gene Ontology, pathways, regulatory elements,
genomic locations, and protein domains.  This flexibility enables researchers to
tailor enrichment analysis to their specific questions, such as identifying
enriched transcriptional regulators in gene expression data or overrepresented
protein domains in protein sets.  To facilitate seamless analysis, mulea
provides gene sets (in standardised GMT format) for 27 model organisms, covering
22 ontology types from 16 databases and various identifiers resulting in almost
900 files.  Additionally, the @code{muleaData} @code{ExperimentData}
Bioconductor package simplifies access to these pre-defined ontologies.
Finally, mulea's architecture allows for easy integration of user-defined
ontologies, or GMT files from external sources (e.g., M@code{SigDB} or Enrichr),
expanding its applicability across diverse research areas.  Conclusions - mulea
is distributed as a CRAN R package.  It offers researchers a powerful and
flexible toolkit for functional enrichment analysis, addressing limitations of
traditional tools with its progressive @code{eFDR} and by supporting a variety
of ontologies.  Overall, mulea fosters the exploration of diverse biological
questions across various model organisms.")
    (license license:gpl2)))

(define-public r-muir
  (package
    (name "r-muir")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "muir" version))
       (sha256
        (base32 "0h3qaqf549v40ms7c851sspaxzidmdpcj89ycdmfp94b2q3bmz98"))))
    (properties `((upstream-name . "muir")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-dplyr r-diagrammer))
    (home-page "https://github.com/alforj/muir")
    (synopsis "Exploring Data with Tree Data Structures")
    (description
     "This package provides a simple tool allowing users to easily and dynamically
explore or document a data set using a tree structure.")
    (license license:gpl2+)))

(define-public r-muerelativerisk
  (package
    (name "r-muerelativerisk")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mueRelativeRisk" version))
       (sha256
        (base32 "16yclfmgxc32pv00vyb9fjdh4syax8ynizr8a29haiq22q5fqclh"))))
    (properties `((upstream-name . "mueRelativeRisk")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mueRelativeRisk")
    (synopsis "Relative Risk Based on the Ratio of Median Unbiased Estimates")
    (description
     "This package implements an estimator for relative risk based on the median
unbiased estimator.  The relative risk estimator is well defined and performs
satisfactorily for a wide range of data configurations.  The details of the
method are available in Carter et al (2010)
<doi:10.1111/j.1467-9876.2010.00711.x>.")
    (license license:gpl3)))

(define-public r-mudfold
  (package
    (name "r-mudfold")
    (version "1.1.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mudfold" version))
       (sha256
        (base32 "0bz0k490pda2a6lx9mmnpvnp6vrlyjd89bn05dsvqgbdfnyfs272"))))
    (properties `((upstream-name . "mudfold")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-reshape2
                             r-mgcv
                             r-glmnet
                             r-ggplot2
                             r-dplyr
                             r-broom
                             r-boot))
    (home-page "https://github.com/cran/mudfold")
    (synopsis "Multiple UniDimensional unFOLDing")
    (description
     "Nonparametric unfolding item response theory (IRT) model for dichotomous data
(see W.H. Van Schuur (1984).  Structure in Political Beliefs: A New Model for
Stochastic Unfolding with Application to European Party Activists, and W.J.Post
(1992).  Nonparametric Unfolding Models: A Latent Structure Approach).  The
package implements MUDFOLD (Multiple @code{UniDimensional} @code{unFOLDing}), an
iterative item selection algorithm that constructs unfolding scales from
dichotomous preferential-choice data without explicitly assuming a parametric
form of the item response functions.  Scale diagnostics from Post(1992) and
estimates for the person locations proposed by Johnson(2006) and Van
Schuur(1984) are also available.  This model can be seen as the unfolding
variant of Mokken(1971) scaling method.")
    (license license:gpl2+)))

(define-public r-muckrock
  (package
    (name "r-muckrock")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "muckrock" version))
       (sha256
        (base32 "16lm1iiaaws7clby7qgblqdiznw6abjjgvsxlfpza7l2xdvplxpg"))))
    (properties `((upstream-name . "muckrock")))
    (build-system r-build-system)
    (home-page "https://github.com/Ironholds/muckrock/")
    (synopsis "Data on Freedom of Information Act Requests")
    (description
     "This package provides a data package containing public domain information on
requests made by the @code{MuckRock} (https://www.muckrock.com/) project under
the United States Freedom of Information Act.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-muchpoint
  (package
    (name "r-muchpoint")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MuChPoint" version))
       (sha256
        (base32 "0mixy2rcr23rk823r1r3s2bhr22dhsx56xvkk0djvfdb60pq0kfh"))))
    (properties `((upstream-name . "MuChPoint")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rcpp r-matrix r-capushe))
    (home-page "https://github.com/Lionning/MuChPoint")
    (synopsis "Multiple Change Point")
    (description
     "Nonparametric approach to estimate the location of block boundaries
(change-points) of non-overlapping blocks in a random symmetric matrix which
consists of random variables whose distribution changes from block to block.
BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017)
<doi:10.1016/j.jmva.2017.12.005>.")
    (license license:gpl2)))

(define-public r-muacz
  (package
    (name "r-muacz")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MUACz" version))
       (sha256
        (base32 "1d771vb2aimr87kyqj7zkmwarmxcxzbh7mvr396pf2zr3h8wi30c"))))
    (properties `((upstream-name . "MUACz")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-epidisplay r-dplyr))
    (home-page "https://cran.r-project.org/package=MUACz")
    (synopsis
     "Generate MUAC and BMI z-Scores and Percentiles for Children and Adolescents")
    (description
     "Generates mid upper arm circumference (MUAC) and body mass index (BMI) for age
z-scores and percentiles based on LMS method for children and adolescents up to
19 years that can be used to assess nutritional and health status and define
risk of adverse health events.")
    (license license:gpl3)))

(define-public r-mtvc
  (package
    (name "r-mtvc")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtvc" version))
       (sha256
        (base32 "0d4rwgp36q94maq1yyms3lcf1m3r17s6rpdgq8ifhdapbz2d5sv6"))))
    (properties `((upstream-name . "mtvc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-dplyr))
    (home-page "https://github.com/egonzato/mtvc")
    (synopsis "Multiple Counting Process Structure for Survival Analysis")
    (description
     "Counting process structure is fundamental to model time varying covariates.
This package restructures dataframes in the counting process format for one or
more variables.  F. W. Dekker, et al. (2008) <doi:10.1038/ki.2008.328>.")
    (license license:expat)))

(define-public r-mtsys
  (package
    (name "r-mtsys")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTSYS" version))
       (sha256
        (base32 "04dkan3hxl3difflskp7d1lw1bvxhg4s28ssy2v4m7z7wbpj3v1m"))))
    (properties `((upstream-name . "MTSYS")))
    (build-system r-build-system)
    (home-page "https://github.com/okayaa/MTSYS")
    (synopsis "Methods in Mahalanobis-Taguchi (MT) System")
    (description
     "Mahalanobis-Taguchi (MT) system is a collection of multivariate analysis methods
developed for the field of quality engineering.  MT system consists of two
families depending on their purpose.  One is a family of Mahalanobis-Taguchi
(MT) methods (in the broad sense) for diagnosis (see Woodall, W. H., Koudelik,
R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and Carvounis, C. P. (2003)
<doi:10.1198/004017002188618626>) and the other is a family of Taguchi (T)
methods for forecasting (see Kawada, H., and Nagata, Y. (2015)
<doi:10.17929/tqs.1.12>).  The MT package contains three basic methods for the
family of MT methods and one basic method for the family of T methods.  The MT
method (in the narrow sense), the Mahalanobis-Taguchi Adjoint (MTA) methods, and
the Recognition-Taguchi (RT) method are for the MT method and the two-sided
Taguchi (T1) method is for the family of T methods.  In addition, the Ta and Tb
methods, which are the improved versions of the T1 method, are included.")
    (license license:expat)))

(define-public r-mtsta
  (package
    (name "r-mtsta")
    (version "0.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtsta" version))
       (sha256
        (base32 "1k6q2lwvz39lvznqr48x03c7rdcqyxskkikv4wbvm1aiannmmqlj"))))
    (properties `((upstream-name . "mtsta")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PaulESantos/mtsta")
    (synopsis
     "Accessing the Red List of Montane Tree Species of the Tropical Andes")
    (description
     "Access the Red List of Montane Tree Species of the Tropical Andes Tejedor
Garavito et al.(2014, ISBN:978-1-905164-60-8).  This package allows users to
search for globally threatened tree species within the andean montane forests,
including cloud forests and seasonal (wet) forests above 1500 m a.s.l.")
    (license license:expat)))

(define-public r-mtsdi
  (package
    (name "r-mtsdi")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtsdi" version))
       (sha256
        (base32 "0j4hl690n8x7zfpygw5qv0m0jyl8dnz1d3r4314w06h7c578n2kp"))))
    (properties `((upstream-name . "mtsdi")))
    (build-system r-build-system)
    (propagated-inputs (list r-gam))
    (home-page "https://cran.r-project.org/package=mtsdi")
    (synopsis "Multivariate Time Series Data Imputation")
    (description
     "This is an EM algorithm based method for imputation of missing values in
multivariate normal time series.  The imputation algorithm accounts for both
spatial and temporal correlation structures.  Temporal patterns can be modeled
using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric
cubic spline or generalized additive models with exogenous covariates.  This
algorithm is specially tailored for climate data with missing measurements from
several monitors along a given region.")
    (license license:gpl2+)))

(define-public r-mts
  (package
    (name "r-mts")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTS" version))
       (sha256
        (base32 "0nc1g5dbg42j0h5xg9psg5kqldp5w87in8ggzliixpsz3cilvry3"))))
    (properties `((upstream-name . "MTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-mvtnorm r-fgarch r-fbasics))
    (home-page "https://cran.r-project.org/package=MTS")
    (synopsis
     "All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models")
    (description
     "Multivariate Time Series (MTS) is a general package for analyzing multivariate
linear time series and estimating multivariate volatility models.  It also
handles factor models, constrained factor models, asymptotic principal component
analysis commonly used in finance and econometrics, and principal volatility
component analysis. (a) For the multivariate linear time series analysis, the
package performs model specification, estimation, model checking, and prediction
for many widely used models, including vector AR models, vector MA models,
vector ARMA models, seasonal vector ARMA models, VAR models with exogenous
variables, multivariate regression models with time series errors, augmented VAR
models, and Error-correction VAR models for co-integrated time series.  For
model specification, the package performs structural specification to overcome
the difficulties of identifiability of VARMA models.  The methods used for
structural specification include Kronecker indices and Scalar Component Models.
(b) For multivariate volatility modeling, the MTS package handles several
commonly used models, including multivariate exponentially weighted
moving-average volatility, Cholesky decomposition volatility models, dynamic
conditional correlation (DCC) models, copula-based volatility models, and
low-dimensional BEKK models.  The package also considers multiple tests for
conditional heteroscedasticity, including rank-based statistics. (c) Finally,
the MTS package also performs forecasting using diffusion index , transfer
function analysis, Bayesian estimation of VAR models, and multivariate time
series analysis with missing values.Users can also use the package to simulate
VARMA models, to compute impulse response functions of a fitted VARMA model, and
to calculate theoretical cross-covariance matrices of a given VARMA model.")
    (license (license:fsdg-compatible "Artistic License 2.0"))))

(define-public r-mtreering
  (package
    (name "r-mtreering")
    (version "1.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MtreeRing" version))
       (sha256
        (base32 "1i6ljz950n9fpgci9b5777dg3b88ihhz8jvyw621hg218imswhxz"))))
    (properties `((upstream-name . "MtreeRing")))
    (build-system r-build-system)
    (propagated-inputs (list r-tiff
                             r-spatstat-geom
                             r-shinywidgets
                             r-shinydashboard
                             r-shiny
                             r-png
                             r-measuring
                             r-magrittr
                             r-magick
                             r-jpeg
                             r-imager
                             r-dplyr
                             r-dplr
                             r-bmp))
    (home-page "https://docs.ropensci.org/MtreeRing")
    (synopsis
     "Shiny Application for Automatic Measurements of Tree-Ring Widths on Digital Images")
    (description
     "Use morphological image processing and edge detection algorithms to
automatically measure tree ring widths on digital images.  Users can also
manually mark tree rings on species with complex anatomical structures.  The
arcs of inner-rings and angles of successive inclined ring boundaries are used
to correct ring-width series.  The package provides a Shiny-based application,
allowing R beginners to easily analyze tree ring images and export ring-width
series in standard file formats.")
    (license license:gpl3)))

(define-public r-mtps
  (package
    (name "r-mtps")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTPS" version))
       (sha256
        (base32 "0bv81vwswhrs0p3mvsiv5njyq1j5p16y14m3sysnyg8rr98gqy38"))))
    (properties `((upstream-name . "MTPS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-mass r-glmnet r-e1071 r-class))
    (native-inputs (list r-knitr))
    (home-page "https://doi.org/10.1093/bioinformatics/btz531")
    (synopsis "Multi-Task Prediction using Stacking Algorithms")
    (description
     "Simultaneous multiple outcomes prediction based on revised stacking algorithms,
which enables the integration of information from predictions of individual
models.  An implementation of methodologies proposed in our paper: Li Xing, Mary
L Lesperance, Xuekui Zhang. (2019) Bioinformatics, \"Simultaneous prediction of
multiple outcomes using revised stacking algorithms\"
<doi:10.1093/bioinformatics/btz531>.")
    (license license:gpl2+)))

(define-public r-mtlr
  (package
    (name "r-mtlr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTLR" version))
       (sha256
        (base32 "070121qn7vmxmxgvzv7ymhk19j9kxks4xa7qpd8vkc2rp3ara4cp"))))
    (properties `((upstream-name . "MTLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/haiderstats/MTLR")
    (synopsis "Survival Prediction with Multi-Task Logistic Regression")
    (description
     "An implementation of Multi-Task Logistic Regression (MTLR) for R. This package
is based on the method proposed by Yu et al. (2011) which utilized MTLR for
generating individual survival curves by learning feature weights which vary
across time.  This model was further extended to account for left and interval
censored data.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mtlgmm
  (package
    (name "r-mtlgmm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtlgmm" version))
       (sha256
        (base32 "0q2nl9sk03w0x8khsgwycp1c66nbgrf88hdp6gys7fmvgmaz3yj9"))))
    (properties `((upstream-name . "mtlgmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mclust r-foreach r-doparallel r-caret))
    (home-page "https://cran.r-project.org/package=mtlgmm")
    (synopsis
     "Unsupervised Multi-Task and Transfer Learning on Gaussian Mixture Models")
    (description
     "Unsupervised learning has been widely used in many real-world applications.  One
of the simplest and most important unsupervised learning models is the Gaussian
mixture model (GMM).  In this work, we study the multi-task learning problem on
GMMs, which aims to leverage potentially similar GMM parameter structures among
tasks to obtain improved learning performance compared to single-task learning.
We propose a multi-task GMM learning procedure based on the
Expectation-Maximization (EM) algorithm that not only can effectively utilize
unknown similarity between related tasks but is also robust against a fraction
of outlier tasks from arbitrary sources.  The proposed procedure is shown to
achieve minimax optimal rate of convergence for both parameter estimation error
and the excess mis-clustering error, in a wide range of regimes.  Moreover, we
generalize our approach to tackle the problem of transfer learning for GMMs,
where similar theoretical results are derived.  Finally, we demonstrate the
effectiveness of our methods through simulations and a real data analysis.  To
the best of our knowledge, this is the first work studying multi-task and
transfer learning on GMMs with theoretical guarantees.  This package implements
the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022)
<@code{arXiv:2209.15224>}.")
    (license license:gpl2)))

(define-public r-mthapower
  (package
    (name "r-mthapower")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mthapower" version))
       (sha256
        (base32 "0g6dn0qn7z6qc1gbzspy1n7b803427bc9fv5yqk7j8i1is1p8h94"))))
    (properties `((upstream-name . "mthapower")))
    (build-system r-build-system)
    (home-page "https://github.com/aurora-mareviv/mthapower")
    (synopsis
     "Sample Size and Power for Association Studies Involving Mitochondrial DNA Haplogroups")
    (description
     "Calculate Sample Size and Power for Association Studies Involving Mitochondrial
DNA Haplogroups.  Based on formulae by Samuels et al.  AJHG, 2006.
78(4):713-720. <DOI:10.1086/502682>.")
    (license license:gpl3)))

(define-public r-mtest
  (package
    (name "r-mtest")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTest" version))
       (sha256
        (base32 "19sz6s5hbrvm4jv54hv8g3d2ixf9pk72ch5j9418skal4dawh1yn"))))
    (properties `((upstream-name . "MTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly r-ggplot2 r-car))
    (home-page "https://github.com/vmoprojs/MTest")
    (synopsis "Procedure for Multicollinearity Testing using Bootstrap")
    (description
     "This package provides functions for detecting multicollinearity.  This test
gives statistical support to two of the most famous methods for detecting
multicollinearity in applied work: Kleinâs rule and Variance Inflation Factor
(VIF).  See the URL for the papers associated with this package, as for
instance, Morales-OÃ±ate and Morales-OÃ±ate (2015) <doi:10.33333/rp.vol51n2.05>.")
    (license license:gpl3+)))

(define-public r-mte
  (package
    (name "r-mte")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTE" version))
       (sha256
        (base32 "0xh9q302gklx0djkjvmp79n7y8d0sc06mm7lvmva5i6s69jq243f"))))
    (properties `((upstream-name . "MTE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rqpen r-quantreg r-glmnet))
    (home-page "https://cran.r-project.org/package=MTE")
    (synopsis
     "Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection")
    (description
     "Several robust estimators for linear regression and variable selection are
provided.  Included are Maximum tangent likelihood estimator by Qin, et al.,
(2017) <arxiv:1708.05439>, least absolute deviance estimator and Huber
regression.  The penalized version of each of these estimator incorporates L1
penalty function, i.e., LASSO and Adaptive Lasso.  They are able to produce
consistent estimates for both fixed and high-dimensional settings.")
    (license license:gpl3)))

(define-public r-mtdrh
  (package
    (name "r-mtdrh")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTDrh" version))
       (sha256
        (base32 "0ass3b90nr4i0y7v30hk8xhq3hj9bxaxcsr4f8wfi0lnvzfv6aaa"))))
    (properties `((upstream-name . "MTDrh")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MTDrh")
    (synopsis "Mass Transportation Distance Rank Histogram")
    (description
     "The Mass Transportation Distance rank histogram was developed to assess the
reliability of scenarios with equal or different probabilities of occurrence
<doi:10.1002/we.1872>.")
    (license license:gpl2)))

(define-public r-mtdesign
  (package
    (name "r-mtdesign")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtdesign" version))
       (sha256
        (base32 "0fnyw6zcls12b6mmr9pxj3b9vs8pv1ik3h671kmxfgv1k6dwn6b9"))))
    (properties `((upstream-name . "mtdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-rcpp
                             r-magrittr
                             r-logger
                             r-ggplot2
                             r-dplyr
                             r-bh))
    (home-page "https://github.com/openpharma/mtdesign")
    (synopsis "Mander and Thompson Designs")
    (description
     "This package implements Mander & Thompson's (2010)
<doi:10.1016/j.cct.2010.07.008> methods for two-stage designs optimal under the
alternative hypothesis for phase II [cancer] trials.  Also provides an
implementation of Simon's (1989) <doi:10.1016/0197-2456(89)90015-9> original
methodology and allows exploration of the operating characteristics of
sub-optimal designs.")
    (license license:gpl3+)))

(define-public r-mtb
  (package
    (name "r-mtb")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtb" version))
       (sha256
        (base32 "1xlm3s34y9g4g6nqzm7zwl08j6587qnzmr5xx0fwgiv5mr45jdz4"))))
    (properties `((upstream-name . "mtb")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-labeling r-htmltools r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yh202109/mtb")
    (synopsis "My Toolbox for Assisting Document Editing and Data Presenting")
    (description
     "The purpose of this package is to share a collection of functions the author
wrote during weekends for managing kitchen and garden tasks, e.g. making plant
growth charts or Thanksgiving kitchen schedule charts, etc.  Functions might
include but not limited to: (1) aiding summarizing time related data; (2)
generating axis transformation from data; and (3) aiding Markdown (with html
output) and Shiny file editing.")
    (license license:agpl3+)))

(define-public r-mtarm
  (package
    (name "r-mtarm")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtarm" version))
       (sha256
        (base32 "19830spz9r1bj520vkafiq3inb45ydzwx6fjaa2vadr6mxr7b2l0"))))
    (properties `((upstream-name . "mtarm")))
    (build-system r-build-system)
    (propagated-inputs (list r-gigrvg r-formula r-coda))
    (home-page "https://cran.r-project.org/package=mtarm")
    (synopsis
     "Bayesian Estimation of Multivariate Threshold Autoregressive Models")
    (description
     "Estimation, inference and forecasting using the Bayesian approach for
multivariate threshold autoregressive (TAR) models in which the distribution
used to describe the noise process belongs to the class of Gaussian variance
mixtures.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mtar
  (package
    (name "r-mtar")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTAR" version))
       (sha256
        (base32 "17r2da1hdvs8qvgar87zdl51jy8w30vczcsi6jrq0rdyi1lld641"))))
    (properties `((upstream-name . "MTAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MTAR")
    (synopsis "Multi-Trait Analysis of Rare-Variant Association Study")
    (description
     "Perform multi-trait rare-variant association tests using the summary statistics
and adjust for possible sample overlap.  Package is based on \"Multi-Trait
Analysis of Rare-Variant Association Summary Statistics using MTAR\" by Luo, L.,
Shen, J., Zhang, H., Chhibber, A. Mehrotra, D.V., Tang, Z., 2019 (submitted).")
    (license license:gpl2+)))

(define-public r-mtanan
  (package
    (name "r-mtanan")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mtanan" version))
       (sha256
        (base32 "10j1p5za9f9g9ygzpljfbnmhp32w6bmx9xwnjl84q32nkvagrmpx"))))
    (properties `((upstream-name . "mtanan")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mtanan")
    (synopsis
     "Single Valued Neutrosophic Kruskal-Wallis and Mann Whitney Tests")
    (description
     "Dealing with neutrosophic data in single valued form using score, accuracy and
certainty functions to calculate ranks of Single Valued Neutrosophic Set (SVNS),
also to calculate the Mann-Whitney test, and making a post-hoc test after
rejecting the null hypothesis using the Neutrosophic Statistics Kruskal-Wallis
test.  For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina,
Mohamed Bisher(2022)
<https://digitalrepository.unm.edu/nss_journal/vol51/iss1/60/>.")
    (license license:gpl3)))

(define-public r-mtaft
  (package
    (name "r-mtaft")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTAFT" version))
       (sha256
        (base32 "0fpvfl19dmp4b6mn55xn42n1v5qml18bxfisa7a1xb5nxdflpxr4"))))
    (properties `((upstream-name . "MTAFT")))
    (build-system r-build-system)
    (propagated-inputs (list r-grpreg))
    (home-page "https://cran.r-project.org/package=MTAFT")
    (synopsis
     "Data-Driven Estimation for Multi-Threshold Accelerate Failure Time Model")
    (description
     "Developed a data-driven estimation framework for the multi-threshold accelerate
failure time (MTAFT) model.  The MTAFT model features different linear forms in
different subdomains, and one of the major challenges is determining the number
of threshold effects.  The package introduces a data-driven approach that
utilizes a Schwarz information criterion, which demonstrates consistency under
mild conditions.  Additionally, a cross-validation (CV) criterion with an
order-preserved sample-splitting scheme is proposed to achieve consistent
estimation, without the need for additional parameters.  The package establishes
the asymptotic properties of the parameter estimates and includes an efficient
score-type test to examine the existence of threshold effects.  The
methodologies are supported by numerical experiments and theoretical results,
showcasing their reliable performance in finite-sample cases.")
    (license license:gpl3)))

(define-public r-mta
  (package
    (name "r-mta")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MTA" version))
       (sha256
        (base32 "0fsn3plw2hh4hbdrzgpjbb7hnfxfxcjz9wgzpn9jy3w910cxv5jj"))))
    (properties `((upstream-name . "MTA")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/riatelab/MTA/")
    (synopsis "Multiscalar Territorial Analysis")
    (description
     "Build multiscalar territorial analysis based on various contexts.")
    (license license:gpl3)))

(define-public r-mt
  (package
    (name "r-mt")
    (version "2.0-1.20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mt" version))
       (sha256
        (base32 "12r7hs83ijn7vw1qqgfrmd5ff7x6p3hxmaxnr3bjr5h2gd6sj42l"))))
    (properties `((upstream-name . "mt")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest
                             r-pls
                             r-mass
                             r-latticeextra
                             r-lattice
                             r-ellipse
                             r-e1071
                             r-class))
    (home-page "https://github.com/wanchanglin/mt")
    (synopsis "Metabolomics Data Analysis Toolbox")
    (description
     "This package provides functions for metabolomics data analysis: data
preprocessing, orthogonal signal correction, PCA analysis, PCA-DA analysis,
PLS-DA analysis, classification, feature selection, correlation analysis, data
visualisation and re-sampling strategies.")
    (license license:gpl2+)))

(define-public r-mswm
  (package
    (name "r-mswm")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSwM" version))
       (sha256
        (base32 "0ny5apvww1537qhiwb3f8x173rh7mypvgpw0i46dkvn897i0dq36"))))
    (properties `((upstream-name . "MSwM")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme))
    (home-page "https://cran.r-project.org/package=MSwM")
    (synopsis "Fitting Markov Switching Models")
    (description
     "Estimation, inference and diagnostics for Univariate Autoregressive Markov
Switching Models for Linear and Generalized Models.  Distributions for the
series include gaussian, Poisson, binomial and gamma cases.  The EM algorithm is
used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).")
    (license license:gpl2+)))

(define-public r-msu
  (package
    (name "r-msu")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msu" version))
       (sha256
        (base32 "1vhh9725dbywmzihnmsq1jircpn91r8227j2f76fvma9rwss90p7"))))
    (properties `((upstream-name . "msu")))
    (build-system r-build-system)
    (propagated-inputs (list r-entropy))
    (home-page "https://cran.r-project.org/package=msu")
    (synopsis "Multivariate Symmetric Uncertainty and Other Measurements")
    (description
     "Estimators for multivariate symmetrical uncertainty based on the work of Gustavo
Sosa et al. (2016) <@code{arXiv:1709.08730>}, total correlation, information
gain and symmetrical uncertainty of categorical variables.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mstudentd
  (package
    (name "r-mstudentd")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mstudentd" version))
       (sha256
        (base32 "0s5kidcz6sv9gaa0mdx2lawn79qd1y5j3vpr76bpv91fibfpzpa6"))))
    (properties `((upstream-name . "mstudentd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-mass r-data-table))
    (home-page "https://forgemia.inra.fr/imhorphen/mstudentd")
    (synopsis "Multivariate t Distribution")
    (description
     "Distance between multivariate t distributions, as presented by N. Bouhlel and D.
Rousseau (2023) <doi:10.1109/LSP.2023.3324594>.")
    (license license:gpl3+)))

(define-public r-mstrio
  (package
    (name "r-mstrio")
    (version "11.3.5.101")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mstrio" version))
       (sha256
        (base32 "103xpkcahp3rzw01yb1qmymavl40xzydmn0z6ya2mcxihzfdf2k3"))))
    (properties `((upstream-name . "mstrio")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-shinyjs
                             r-shiny
                             r-rstudioapi
                             r-r6
                             r-openssl
                             r-markdown
                             r-jsonlite
                             r-httr
                             r-data-table
                             r-crul))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mstrio")
    (synopsis "Interface for 'MicroStrategy' REST API")
    (description
     "Interface for creating data sets and extracting data through the
@code{MicroStrategy} REST API. Access the demo API at
<https://demo.microstrategy.com/@code{MicroStrategyLibrary/api-docs/index.html>}.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mstr
  (package
    (name "r-mstr")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mstR" version))
       (sha256
        (base32 "0v8cv9pswkvw0lva6jx5vavsb20dawgq83gn4rgydyhvigcl5szd"))))
    (properties `((upstream-name . "mstR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mstR")
    (synopsis "Procedures to Generate Patterns under Multistage Testing")
    (description
     "Generation of response patterns under dichotomous and polytomous computerized
multistage testing (MST) framework.  It holds various item response theory (IRT)
and score-based methods to select the next module and estimate ability levels
(Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).")
    (license license:gpl2+)))

(define-public r-mstknnclust
  (package
    (name "r-mstknnclust")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mstknnclust" version))
       (sha256
        (base32 "1b07a2whgr27ph0i1gdszahpbiriklf8jafikg02x6qb63lnry7r"))))
    (properties `((upstream-name . "mstknnclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mstknnclust")
    (synopsis "MST-kNN Clustering Algorithm")
    (description
     "This package implements the MST-@code{kNN} clustering algorithm which was
proposed by Inostroza-Ponta, M. (2008)
<https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.")
    (license license:gpl2)))

(define-public r-mstherm
  (package
    (name "r-mstherm")
    (version "0.4.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mstherm" version))
       (sha256
        (base32 "04jrp0w17svwmrvx356jmh04npbwhk9nvfy3r39vqr82yrvn6jip"))))
    (properties `((upstream-name . "mstherm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-plotrix r-nls2 r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mstherm")
    (synopsis "Analyze MS/MS Protein Melting Data")
    (description
     "Software to aid in modeling and analyzing mass-spectrometry-based proteome
melting data.  Quantitative data is imported and normalized and thermal behavior
is modeled at the protein level.  Methods exist for normalization, modeling,
visualization, and export of results.  For a general introduction to MS-based
thermal profiling, see Savitski et al. (2014) <doi:10.1126/science.1255784>.")
    (license license:gpl3)))

(define-public r-mstest
  (package
    (name "r-mstest")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSTest" version))
       (sha256
        (base32 "0ad28aj9pmy9vn6z9fhzvap4cbxafbk09amhgp178hvw9rdp48zf"))))
    (properties `((upstream-name . "MSTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-pso
                             r-pracma
                             r-numderiv
                             r-nloptr
                             r-gensa
                             r-ga
                             r-foreach))
    (home-page "https://github.com/roga11/MSTest")
    (synopsis "Hypothesis Testing for Markov Switching Models")
    (description
     "Implementation of hypothesis testing procedures described in Hansen (1992)
<doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014)
<doi:10.3982/ECTA8609>, Dufour & Luger (2017)
<doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2022)
<https://grodriguezrondon.com/files/@code{RodriguezRondon_Dufour_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels.pdf>}
that can be used to identify the number of regimes in Markov switching models.")
    (license license:gpl2+)))

(define-public r-mstem
  (package
    (name "r-mstem")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mSTEM" version))
       (sha256
        (base32 "0mh09mqqpq9k8jrxs9d39009idld3b4y98ik75czdy580jmdg7mm"))))
    (properties `((upstream-name . "mSTEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-latex2exp r-foreach r-doparallel))
    (home-page "https://arxiv.org/abs/1504.06384")
    (synopsis
     "Multiple Testing of Local Extrema for Detection of Change Points")
    (description
     "This package provides a new approach to detect change points based on smoothing
and multiple testing, which is for long data sequence modeled as piecewise
constant functions plus stationary Gaussian noise, see Dan Cheng and Armin
Schwartzman (2015) <@code{arXiv:1504.06384>}.")
    (license license:gpl3)))

(define-public r-mstdif
  (package
    (name "r-mstdif")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mstDIF" version))
       (sha256
        (base32 "010v2rbwv7fl4w3jln6isjx8nm8lv4p6iimhrwwkp5vnbvxs3jkq"))))
    (properties `((upstream-name . "mstDIF")))
    (build-system r-build-system)
    (propagated-inputs (list r-scdiftest
                             r-pp
                             r-mirt
                             r-matrix
                             r-expm
                             r-erm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mstDIF")
    (synopsis "Collection of DIF Tests for Multistage Tests")
    (description
     "This package provides a collection of statistical tests for the detection of
differential item functioning (DIF) in multistage tests.  Methods entail
logistic regression, an adaptation of the simultaneous item bias test (SIBTEST),
and various score-based tests.  The presented tests provide itemwise test for
DIF along categorical, ordinal or metric covariates.  Methods for uniform and
non-uniform DIF effects are available depending on which method is used.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mstclustering
  (package
    (name "r-mstclustering")
    (version "1.0.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mstclustering" version))
       (sha256
        (base32 "1m2hmjia3r5ph5zl2314kq8dmjggzvw3ad8cixjjd70hi8570fpw"))))
    (properties `((upstream-name . "mstclustering")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-data-table))
    (home-page "https://cran.r-project.org/package=mstclustering")
    (synopsis "\"MST-Based Clustering\"")
    (description
     "This package implements a minimum-spanning-tree-based heuristic for k-means
clustering using a union-find disjoint set and the algorithm in Kruskal (1956)
<doi:10.1090/S0002-9939-1956-0078686-7>.")
    (license license:agpl3+)))

(define-public r-mstats
  (package
    (name "r-mstats")
    (version "3.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mStats" version))
       (sha256
        (base32 "196fjnld26dmirga4mghlvsrj4fgnw08wdg77masxm15kpvm1x1d"))))
    (properties `((upstream-name . "mStats")))
    (build-system r-build-system)
    (home-page "https://myominnoo.github.io/")
    (synopsis "Epidemiological Data Analysis")
    (description
     "This is a tool for epidemiologist, medical data analyst, medical or public
health professionals.  It contains three domains of functions: 1) data
management, 2) statistical analysis and 3) calculating epidemiological measures.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mst
  (package
    (name "r-mst")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MST" version))
       (sha256
        (base32 "0p833nyq9p0r0hy02f4sy6gv1wm9flj3dxkv73ihy8503r89bbzz"))))
    (properties `((upstream-name . "MST")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-partykit r-mass r-formula))
    (home-page "https://cran.r-project.org/package=MST")
    (synopsis "Multivariate Survival Trees")
    (description
     "Constructs trees for multivariate survival data using marginal and frailty
models.  Grows, prunes, and selects the best-sized tree.")
    (license license:gpl2)))

(define-public r-mssql
  (package
    (name "r-mssql")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSSQL" version))
       (sha256
        (base32 "15glgn2dr1yygxps0mx68435zqc2hi4hvgr4rvgrwyxdnlm5g35y"))))
    (properties `((upstream-name . "MSSQL")))
    (build-system r-build-system)
    (propagated-inputs (list r-rodbc))
    (home-page "https://cran.r-project.org/package=MSSQL")
    (synopsis "Tools to Work with Microsoft SQL Server Databases via 'RODBC'")
    (description
     "This package provides tools that extend the functionality of the RODBC package
to work with Microsoft SQL Server databases.  Makes it easier to browse the
database and examine individual tables and views.")
    (license license:gpl3)))

(define-public r-msspchelpr
  (package
    (name "r-msspchelpr")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msSPChelpR" version))
       (sha256
        (base32 "0m2cijzdgxq3f0ncqvk8klb2hvndm5rzl4swwh62wck12n7z250g"))))
    (properties `((upstream-name . "msSPChelpR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidytable
                             r-tidyselect
                             r-tidyr
                             r-stringr
                             r-sjlabelled
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://marianschmidt.github.io/msSPChelpR/")
    (synopsis "Helper Functions for Second Primary Cancer Analyses")
    (description
     "This package provides a collection of helper functions for analyzing Second
Primary Cancer data, including functions to reshape data, to calculate patient
states and analyze cancer incidence.")
    (license license:gpl3)))

(define-public r-mssearchr
  (package
    (name "r-mssearchr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mssearchr" version))
       (sha256
        (base32 "0w5i2vcnargw8z80sgmh0qrb5sdv5s3fi3b05qvyc0mx0k6b77lh"))))
    (properties `((upstream-name . "mssearchr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://mass-spec.ru/projects/gcmsdata/mssearchr/eng/")
    (synopsis
     "Library Search Against Electron Ionization Mass Spectral Databases")
    (description
     "Perform library searches against electron ionization mass spectral databases
using either the API provided by MS Search software
(<https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nistlibs>) or custom
implementations of the Identity and Similarity algorithms.")
    (license license:expat)))

(define-public r-msrdt
  (package
    (name "r-msrdt")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSRDT" version))
       (sha256
        (base32 "1i35zsip6p177k9pnbmvkjdc1hpr6nclhbx2sfah95ls5v6xnzz8"))))
    (properties `((upstream-name . "MSRDT")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-gtools r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ericchen12377/MSRDT")
    (synopsis "Multi-State Reliability Demonstration Tests (MSRDT)")
    (description
     "This is a implementation of design methods for multi-state reliability
demonstration tests (MSRDT) with failure count data, which is associated with
the work from the published paper \"Multi-state Reliability Demonstration Tests\"
by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>.  It implements
two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM).  For
MP, two different scenarios with criteria on cumulative periods (Cum) or
separate periods (Sep) are implemented respectively.  It also provides the
implementation of conventional design method, namely binomial tests for failure
count data.")
    (license license:gpl3)))

(define-public r-msprt
  (package
    (name "r-msprt")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSPRT" version))
       (sha256
        (base32 "0fvikvnf3fa4ski9idg8llxx33bgkrfj5sgkjn0d1dz30kgcx2w2"))))
    (properties `((upstream-name . "MSPRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv
                             r-iterators
                             r-ggpubr
                             r-ggplot2
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=MSPRT")
    (synopsis "Modified Sequential Probability Ratio Test (MSPRT)")
    (description
     "Given the maximum available sample size (N) for an experiment, and the target
levels of Type I and II error probabilities, this package designs a modified
SPRT (MSPRT).  For any designed MSPRT the package can also obtain its operating
characteristics and implement the test for a given sequentially observed data.
The MSPRT is defined in a manner very similar to Wald's initial proposal.  The
proposed test has shown evidence of reducing the average sample size required to
perform statistical hypothesis tests at specified levels of significance and
power.  Currently, the package implements one-sample proportion tests, one and
two-sample z tests, and one and two-sample t tests.  A brief user guidance for
this package is provided below.  One can also refer to the supplemental
information for the same.")
    (license license:gpl2+)))

(define-public r-msoutcomes
  (package
    (name "r-msoutcomes")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSoutcomes" version))
       (sha256
        (base32 "037kpsi24anid9brf9s4yrknyxi5c9xyxnckvh2f5m1wxbl7bmlj"))))
    (properties `((upstream-name . "MSoutcomes")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://cran.r-project.org/package=MSoutcomes")
    (synopsis "CORe Multiple Sclerosis Outcomes Toolkit")
    (description
     "Enable operationalized evaluation of disease outcomes in multiple sclerosis.
âMSoutcomesâ requires longitudinally recorded clinical data structured in
long format.  The package is based on the research developed at Clinical
Outcomes Research unit (CORe), University of Melbourne and Neuroimmunology
Centre, Royal Melbourne Hospital.  Kalincik et al. (2015)
<doi:10.1093/brain/awv258>.  Lorscheider et al. (2016)
<doi:10.1093/brain/aww173>.  Sharmin et al. (2022) <doi:10.1111/ene.15406>.
Dzau et al. (2023) <doi:10.1136/jnnp-2023-331748>.")
    (license license:expat)))

(define-public r-msos
  (package
    (name "r-msos")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msos" version))
       (sha256
        (base32 "1fg9q6269cckn4g6isv22k22mzglxhjxjg5npqsikmisribmr9kb"))))
    (properties `((upstream-name . "msos")))
    (build-system r-build-system)
    (propagated-inputs (list r-tree r-mclust))
    (home-page "https://github.com/coatless/msos")
    (synopsis
     "Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden")
    (description
     "Multivariate Analysis methods and data sets used in John Marden's book
Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.  This also
serves as a companion package for the STAT 571: Multivariate Analysis course
offered by the Department of Statistics at the University of Illinois at
Urbana-Champaign ('UIUC').")
    (license license:expat)))

(define-public r-msmwra
  (package
    (name "r-msmwra")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSMwRA" version))
       (sha256
        (base32 "062d37h47p3sx65k5jh8n900dfzm1mvi18ikral3ajdhy7x2cif9"))))
    (properties `((upstream-name . "MSMwRA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MSMwRA")
    (synopsis "Multivariate Statistical Methods with R Applications")
    (description
     "Data sets in the book entitled \"Multivariate Statistical Methods with R
Applications\", H.Bulut (2018).  The book was published in Turkish and the
original name of this book will be \"R Uygulamalari ile Cok Degiskenli
Istatistiksel Yontemler\".")
    (license license:gpl3)))

(define-public r-msml
  (package
    (name "r-msml")
    (version "1.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSML" version))
       (sha256
        (base32 "0nzn3zys6cbqvzjncdgvlxw00zvg7c2wcnf59yky66chz42c31wd"))))
    (properties `((upstream-name . "MSML")))
    (build-system r-build-system)
    (propagated-inputs (list r-r2roc r-r2redux))
    (home-page "https://github.com/mommy003/MSML")
    (synopsis "Model Selection Based on Machine Learning (ML)")
    (description
     "Model evaluation based on a modified version of the recursive feature
elimination algorithm.  This package is designed to determine the optimal
model(s) by leveraging all available features.")
    (license license:gpl3+)))

(define-public r-msmix
  (package
    (name "r-msmix")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSmix" version))
       (sha256
        (base32 "10bdzgx21y8xqggj98lv17mb2dv39ay6k0l5mz3dsn44967mv7rm"))))
    (properties `((upstream-name . "MSmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-spsutil
                             r-scales
                             r-rlang
                             r-reshape
                             r-rcpp
                             r-rcolorbrewer
                             r-rankcluster
                             r-magrittr
                             r-gridextra
                             r-gmp
                             r-ggplot2
                             r-ggbump
                             r-foreach
                             r-fields
                             r-factoextra
                             r-dplyr
                             r-data-table
                             r-bmixture
                             r-bayesmallows))
    (home-page "https://cran.r-project.org/package=MSmix")
    (synopsis
     "Finite Mixtures of Mallows Models with Spearman Distance for Full and Partial Rankings")
    (description
     "Fit and analysis of finite Mixtures of Mallows models with Spearman Distance for
full and partial rankings with arbitrary missing positions.  Inference is
conducted within the maximum likelihood framework via Expectation-Maximization
algorithms.  Estimation uncertainty is tackled via diverse versions of
bootstrapping as well as via Hessian-based standard errors calculations.  The
most relevant reference of the methods is Crispino, Mollica, Astuti and Tardella
(2023) <doi:10.1007/s11222-023-10266-8>.")
    (license license:gpl3+)))

(define-public r-msme
  (package
    (name "r-msme")
    (version "0.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msme" version))
       (sha256
        (base32 "0mq57zdas1s87nblnvbif9lisgahfhvmabglvp9imr1mvpwybpbh"))))
    (properties `((upstream-name . "msme")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lattice))
    (home-page "https://cran.r-project.org/package=msme")
    (synopsis
     "Functions and Datasets for \"Methods of Statistical Model Estimation\"")
    (description
     "This package provides functions and datasets from Hilbe, J.M., and Robinson,
A.P. 2013.  Methods of Statistical Model Estimation.  Chapman & Hall / CRC.")
    (license license:gpl3)))

(define-public r-msma
  (package
    (name "r-msma")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msma" version))
       (sha256
        (base32 "173gi1j313z5kakvh24432j3f2mg8c9bvvi3rj5w3n4d6qk7s5cj"))))
    (properties `((upstream-name . "msma")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=msma")
    (synopsis "Multiblock Sparse Multivariable Analysis")
    (description
     "Several functions can be used to analyze multiblock multivariable data.  If the
input is a single matrix, then principal components analysis (PCA) is
implemented.  If the input is a list of matrices, then multiblock PCA is
implemented.  If the input is two matrices, for exploratory and objective
variables, then partial least squares (PLS) analysis is implemented.  If the
input is two lists of matrices, for exploratory and objective variables, then
multiblock PLS analysis is implemented.  Additionally, if an extra outcome
variable is specified, then a supervised version of the methods above is
implemented.  For each method, sparse modeling is also incorporated.  Functions
for selecting the number of components and regularized parameters are also
provided.")
    (license license:gpl2+)))

(define-public r-msltrend
  (package
    (name "r-msltrend")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msltrend" version))
       (sha256
        (base32 "1rwy77ijf3hzq2zp47cijwvqcq34rdlfxwhrd9l56bvmlmzr1dqx"))))
    (properties `((upstream-name . "msltrend")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tseries
                             r-rssa
                             r-plyr
                             r-forecast
                             r-changepoint))
    (home-page "https://cran.r-project.org/package=msltrend")
    (synopsis
     "Improved Techniques to Estimate Trend, Velocity and Acceleration from Sea Level Records")
    (description
     "Analysis of annual average ocean water level time series from long (minimum
length 80 years) individual records, providing improved estimates of trend (mean
sea level) and associated real-time velocities and accelerations.  Improved
trend estimates are based on Singular Spectrum Analysis methods.  Various
gap-filling options are included to accommodate incomplete time series records.
The package also contains a forecasting module to consider the implication of
user defined quantum of sea level rise between the end of the available
historical record and the year 2100.  A wide range of screen and pdf plotting
options are available in the package.")
    (license license:gpl3+)))

(define-public r-mskcc-oncotree
  (package
    (name "r-mskcc-oncotree")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mskcc.oncotree" version))
       (sha256
        (base32 "1n79k3rp84r8j6paqshvingykszsw995fm7yxf1wl1jfp4hiyl4n"))))
    (properties `((upstream-name . "mskcc.oncotree")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tidyjson
                             r-tibble
                             r-rlang
                             r-readr
                             r-purrr
                             r-memoise
                             r-magrittr
                             r-httr2
                             r-glue
                             r-dplyr))
    (home-page "https://maialab.org/mskcc.oncotree/")
    (synopsis "Interface to the 'OncoTree' API")
    (description
     "Programmatic access to @code{OncoTree} API <http://oncotree.mskcc.org/>.  Get
access to tumor main types, identifiers and utility routines to map across to
other tumor classification systems.")
    (license license:expat)))

(define-public r-msip
  (package
    (name "r-msip")
    (version "1.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSiP" version))
       (sha256
        (base32 "1n8yl5icd3qkpmx2knxsp4g6jvkbg6hl82jxg9apxkx4nmsvkawz"))))
    (properties `((upstream-name . "MSiP")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-ranger
                             r-prroc
                             r-proc
                             r-plyr
                             r-mice
                             r-magrittr
                             r-e1071
                             r-dplyr
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MSiP")
    (synopsis "'MassSpectrometry' Interaction Prediction")
    (description
     "The M@code{SiP} is a computational approach to predict protein-protein
interactions from large-scale affinity purification mass spectrometry (AP-MS)
data.  This approach includes both spoke and matrix models for interpreting
AP-MS data in a network context.  The \"spoke\" model considers only bait-prey
interactions, whereas the \"matrix\" model assumes that each of the identified
proteins (baits and prey) in a given AP-MS experiment interacts with each of the
others.  The spoke model has a high false-negative rate, whereas the matrix
model has a high false-positive rate.  Although, both statistical models have
merits, a combination of both models has shown to increase the performance of
machine learning classifiers in terms of their capabilities in discrimination
between true and false positive interactions.")
    (license license:gpl3)))

(define-public r-msinference
  (package
    (name "r-msinference")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSinference" version))
       (sha256
        (base32 "0xazpyycpc4p2lhxfszy5ga8synwdvwilhzqhp7dv4900zsysbk7"))))
    (properties `((upstream-name . "MSinference")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpp r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MSinference")
    (synopsis "Multiscale Inference for Nonparametric Time Trend(s)")
    (description
     "This package performs a multiscale analysis of a nonparametric regression or
nonparametric regressions with time series errors.  In case of one regression,
with the help of this package it is possible to detect the regions where the
trend function is increasing or decreasing.  In case of multiple regressions,
the test identifies regions where the trend functions are different from each
other.  See Khismatullina and Vogt (2020) <doi:10.1111/rssb.12347>,
Khismatullina and Vogt (2022) <doi:10.48550/@code{arXiv.2209.10841>} and
Khismatullina and Vogt (2023) <doi:10.1016/j.jeconom.2021.04.010> for more
details on theory and applications.")
    (license license:gpl2+)))

(define-public r-msimst
  (package
    (name "r-msimst")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSIMST" version))
       (sha256
        (base32 "1n8jx4narzxyghn5xdmn1vm5b4nyd2n24dkrb34p29bwlnqyhdf9"))))
    (properties `((upstream-name . "MSIMST")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-mass
                             r-fields))
    (home-page "https://github.com/rh8liuqy/MSIMST")
    (synopsis
     "Bayesian Monotonic Single-Index Regression Model with the Skew-T Likelihood")
    (description
     "Incorporates a Bayesian monotonic single-index mixed-effect model with a
multivariate skew-t likelihood, specifically designed to handle survey weights
adjustments.  Features include a simulation program and an associated Gibbs
sampler for model estimation.  The single-index function is constrained to be
monotonic increasing, utilizing a customized Gaussian process prior for precise
estimation.  The model assumes random effects follow a canonical skew-t
distribution, while residuals are represented by a multivariate Student-t
distribution.  Offers robust Bayesian adjustments to integrate survey weight
information effectively.")
    (license license:gpl3+)))

(define-public r-msimcc
  (package
    (name "r-msimcc")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mSimCC" version))
       (sha256
        (base32 "1dnyrc4hxdimqkk8rm57z74ihmlzmg8plx30ch7rf1z86i33hnjy"))))
    (properties `((upstream-name . "mSimCC")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=mSimCC")
    (synopsis "Micro Simulation Model for Cervical Cancer Prevention")
    (description
     "Micro simulation model to reproduce natural history of cervical cancer and
cost-effectiveness evaluation of prevention strategies.  See Georgalis L, de
Sanjose S, Esnaola M, Bosch F X, Diaz M (2016)
<doi:10.1097/CEJ.0000000000000202> for more details.")
    (license license:gpl2+)))

(define-public r-msigtools
  (package
    (name "r-msigtools")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mSigTools" version))
       (sha256
        (base32 "11xjrfgifh491bzrzh1vw7sawdrlj1yb1yfvj09gjggszn9wn40r"))))
    (properties `((upstream-name . "mSigTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-sets r-quadprog r-philentropy r-clue))
    (home-page "https://github.com/Rozen-Lab/mSigTools")
    (synopsis "Mutational Signature Analysis Tools")
    (description
     "Utility functions for mutational signature analysis as described in Alexandrov,
L. B. (2020) <doi:10.1038/s41586-020-1943-3>.  This package provides two groups
of functions.  One is for dealing with mutational signature \"exposures\" (i.e.
the counts of mutations in a sample that are due to each mutational signature).
The other group of functions is for matching or comparing sets of mutational
signatures. @code{mSigTools} stands for mutational Signature analysis Tools.")
    (license license:gpl3)))

(define-public r-msigseg
  (package
    (name "r-msigseg")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSigSeg" version))
       (sha256
        (base32 "0675nkv5lqw3y19i6bcqhc1jc9qjmyf0x01224d098kfwl74lxiw"))))
    (properties `((upstream-name . "MSigSeg")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-ggpubr r-ggplot2))
    (home-page "https://cran.r-project.org/package=MSigSeg")
    (synopsis "Multiple SIGnal SEGmentation")
    (description
     "Traditional methods typically detect breakpoints from individual signals, which
means that when applied separately to multiple signals, the breakpoints are not
aligned.  However, this package implements a common breakpoint detection
approach for multiple piecewise constant signals, resulting in increased
detection sensitivity and specificity.  By employing various techniques, optimal
performance is ensured, and computation is accelerated.  We hope that this
package will be beneficial for researchers in signal processing, bioinformatics,
economy, and other related fields.  The segmentation(), lambda_estimator()
functions are the main functions of this package.")
    (license license:gpl3)))

(define-public r-msig
  (package
    (name "r-msig")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msig" version))
       (sha256
        (base32 "0gqd0kqlzhvp9widgjxbi5rz75bx0rkbnwbagjmzfv49f8g7yh46"))))
    (properties `((upstream-name . "msig")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tmcn
                             r-stringr
                             r-sqldf
                             r-set
                             r-rvest
                             r-plyr
                             r-kableextra
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-do
                             r-crayon))
    (home-page "https://cran.r-project.org/package=msig")
    (synopsis "An R Package for Exploring Molecular Signatures Database")
    (description
     "The Molecular Signatures Database ('M@code{SigDB}') is one of the most widely
used and comprehensive databases of gene sets for performing gene set enrichment
analysis <doi:10.1016/j.cels.2015.12.004>.  The msig package provides you with
powerful, easy-to-use and flexible query functions for the @code{MsigDB}
database.  There are 2 query modes in the msig package: online query and local
query.  Both queries contain 2 steps: gene set name and gene.  The online search
is divided into 2 modes: registered search and non-registered browse.  For
registered search, email that you registered should be provided.  Local queries
can be made from local database, which can be updated by msig_update() function.")
    (license license:gpl2)))

(define-public r-mshap
  (package
    (name "r-mshap")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mshap" version))
       (sha256
        (base32 "1q01q1w5wsiynabsvd5vl5jffiyxrmyj4ad6b07zhja0ff6b2n9j"))))
    (properties `((upstream-name . "mshap")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-ggbeeswarm
                             r-forcats
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mshap")
    (synopsis "Multiplicative SHAP Values for Two-Part Models")
    (description
     "Allows for the computation of @code{mSHAP} values on two-part models as proposed
by Matthews, S. and Hartman, B. (2021) <@code{arXiv:2106.08990>}.  Also contains
functions for simple plotting of the results (or any SHAP values).  For
information about the @code{TreeSHAP} algorithm that @code{mSHAP} builds on, see
Lundberg, S.M., Erion, G., Chen, H., @code{DeGrave}, A., Prutkin, J.M., Nair,
B., Katz, R., Himmelfarb, J., Bansal, N., Lee, S.I. (2020)
<doi:10.1038/s42256-019-0138-9>.")
    (license license:expat)))

(define-public r-msgr
  (package
    (name "r-msgr")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msgr" version))
       (sha256
        (base32 "15s3d77xqczjydgcfx4jlsq408hgsm8yzqk5248v58vm6zy36mh0"))))
    (properties `((upstream-name . "msgr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-purrr))
    (home-page "https://github.com/ChadGoymer/msgr")
    (synopsis
     "Extends Messages, Warnings and Errors by Adding Levels and Log Files")
    (description
     "This package provides new functions info(), warn() and error(), similar to
message(), warning() and stop() respectively.  However, the new functions can
have a level associated with them, so that when executed the global level option
determines whether they are shown or not.  This allows debug modes, outputting
more information.  The can also output all messages to a log file.")
    (license license:expat)))

(define-public r-msgps
  (package
    (name "r-msgps")
    (version "1.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msgps" version))
       (sha256
        (base32 "1gbkfp1dnvkrxd6rapl6mrzpr8g1acl3xmifvr2aaixfalnh1nif"))))
    (properties `((upstream-name . "msgps")))
    (build-system r-build-system)
    (home-page "https://keihirose.com/")
    (synopsis
     "Degrees of Freedom of Elastic Net, Adaptive Lasso and Generalized Elastic Net")
    (description
     "Computes the degrees of freedom of the lasso, elastic net, generalized elastic
net and adaptive lasso based on the generalized path seeking algorithm.  The
optimal model can be selected by model selection criteria including Mallows Cp,
bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC.")
    (license license:gpl2+)))

(define-public r-msgpackr
  (package
    (name "r-msgpackr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msgpackR" version))
       (sha256
        (base32 "0a6vm4q1zfy8wlvhl9wfy09ig1iag9fvjasz5w9bll7idky4ldx5"))))
    (properties `((upstream-name . "msgpackR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=msgpackR")
    (synopsis "library to serialize or unserialize data in MessagePack format")
    (description
     "This is the library that can serialize or unserialize @code{MessagePack} format
data.")
    (license license:bsd-2)))

(define-public r-msgarchelm
  (package
    (name "r-msgarchelm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSGARCHelm" version))
       (sha256
        (base32 "172xpvs98b9ay44ffb8bp9k4zsjzhy92pp7bv3d1j186pqhl13ib"))))
    (properties `((upstream-name . "MSGARCHelm")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnfor r-msgarch r-forecast))
    (home-page "https://cran.r-project.org/package=MSGARCHelm")
    (synopsis "Hybridization of MS-GARCH and ELM Model")
    (description
     "This package implements the three parallel forecast combinations of Markov
Switching GARCH and extreme learning machine model along with the selection of
appropriate model for volatility forecasting.  For method details see Hsiao C,
Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007).
<doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A
(2013). <doi:10.1016/j.jeconom.2013.04.017>.")
    (license license:gpl3)))

(define-public r-msgarch
  (package
    (name "r-msgarch")
    (version "2.51")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSGARCH" version))
       (sha256
        (base32 "1n8v9017p8iw9mai3yqh294v7kyj458a6vj9ma20gzd94nhyhlxh"))))
    (properties `((upstream-name . "MSGARCH")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-mass
                             r-fanplot
                             r-expm
                             r-coda))
    (home-page "https://github.com/keblu/MSGARCH")
    (synopsis "Markov-Switching GARCH Models")
    (description
     "Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various
Markov-Switching GARCH models as described in Ardia et al. (2019)
<doi:10.18637/jss.v091.i04>.")
    (license license:gpl2+)))

(define-public r-msg
  (package
    (name "r-msg")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSG" version))
       (sha256
        (base32 "178209p30fmh3c03w8iakz306h2xswcj4ffxg1s9s8md156rbj7s"))))
    (properties `((upstream-name . "MSG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer))
    (home-page "https://github.com/yihui/MSG")
    (synopsis "Data and Functions for the Book Modern Statistical Graphics")
    (description
     "This package provides a companion to the Chinese book ``Modern Statistical
Graphics''.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-msetool
  (package
    (name "r-msetool")
    (version "3.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSEtool" version))
       (sha256
        (base32 "1k3df2xpzm41dsqxpvdki5hmar1azz24zfhkl9nlrr2jbb8p8pjr"))))
    (properties `((upstream-name . "MSEtool")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall
                             r-rcpparmadillo
                             r-rcpp
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-abind))
    (home-page "https://msetool.openmse.com/")
    (synopsis "Management Strategy Evaluation Toolkit")
    (description
     "Development, simulation testing, and implementation of management procedures for
fisheries (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).")
    (license license:gpl3)))

(define-public r-msentropy
  (package
    (name "r-msentropy")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msentropy" version))
       (sha256
        (base32 "1wn71xqpzs06rnp27ci0lw86blyb25k9xkpkqwdv6656shf21iqp"))))
    (properties `((upstream-name . "msentropy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/YuanyueLi/MSEntropy")
    (synopsis "Spectral Entropy for Mass Spectrometry Data")
    (description
     "Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy
similarity, and entropy similarity for mass spectrometry data.  The entropy
similarity is a novel similarity measure for MS/MS spectra which outperform the
widely used dot product similarity in compound identification.  For more
details, please refer to the paper: Yuanyue Li et al. (2021) \"Spectral entropy
outperforms MS/MS dot product similarity for small-molecule compound
identification\" <doi:10.1038/s41592-021-01331-z>.")
    (license license:asl2.0)))

(define-public r-mseapca
  (package
    (name "r-mseapca")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mseapca" version))
       (sha256
        (base32 "0kmli8dmkv7dacb52dz84plhr4ncagch2kzh61amhga4chwqhwzq"))))
    (properties `((upstream-name . "mseapca")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-loadings))
    (home-page "https://cran.r-project.org/package=mseapca")
    (synopsis "Metabolite Set Enrichment Analysis for Loadings")
    (description
     "Computing metabolite set enrichment analysis (MSEA) (Yamamoto, H. et al. (2014)
<doi:10.1186/1471-2105-15-51>) and single sample enrichment analysis (SSEA)
(Yamamoto, H. (2023) <doi:10.51094/jxiv.262>).")
    (license license:lgpl3)))

(define-public r-msdrought
  (package
    (name "r-msdrought")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msdrought" version))
       (sha256
        (base32 "1h8g8i1f8hynr32c7xiz72ykqd0l4fnh8vl3dhm8agxa8l0ksjpm"))))
    (properties `((upstream-name . "msdrought")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts r-signal r-quantmod r-lubridate r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Turner-SCU/msdrought")
    (synopsis "Seasonal Mid-Summer Drought Characteristics")
    (description
     "Characterization of a mid-summer drought (MSD) with precipitation based
statistics.  The MSD is a phenomenon of decreased rainfall during a typical
rainy season.  It is a feature of rainfall in much of Central America and is
also found in other locations, typically those with a Mediterranean climate.
Details on the metrics are in Maurer et al. (2022)
<doi:10.5194/hess-26-1425-2022>.")
    (license license:expat)))

(define-public r-msde
  (package
    (name "r-msde")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msde" version))
       (sha256
        (base32 "0q2jy5f8r5gfj2w8r0kmbgal3wjk37h62ahhjzbphrd8ghdf7za8"))))
    (properties `((upstream-name . "msde")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker r-rcppprogress r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=msde")
    (synopsis
     "Bayesian Inference for Multivariate Stochastic Differential Equations")
    (description
     "This package implements an MCMC sampler for the posterior distribution of
arbitrary time-homogeneous multivariate stochastic differential equation (SDE)
models with possibly latent components.  The package provides a simple entry
point to integrate user-defined models directly with the sampler's C++ code, and
parallelizes large portions of the calculations when compiled with
@code{OpenMP}'.")
    (license license:gpl3)))

(define-public r-msd
  (package
    (name "r-msd")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msd" version))
       (sha256
        (base32 "0n3zg2mwbmazbkk41jv94v28glj38d7f08d3w3xrlnlr9gsqh4sd"))))
    (properties `((upstream-name . "msd")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=msd")
    (synopsis "Method of Successive Dichotomizations")
    (description
     "This package implements the method of successive dichotomizations by Bradley and
Massof (2018) <doi:10.1371/journal.pone.0206106>, which estimates item measures,
person measures and ordered rating category thresholds given ordinal rating
scale data.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mscsweblm4r
  (package
    (name "r-mscsweblm4r")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mscsweblm4r" version))
       (sha256
        (base32 "031s00wpr9zfjpii56m67q1phn05vqlhb8cfzhyf6fbrxvpb8k7n"))))
    (properties `((upstream-name . "mscsweblm4r")))
    (build-system r-build-system)
    (propagated-inputs (list r-pander r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/philferriere/mscsweblm4r")
    (synopsis
     "R Client for the Microsoft Cognitive Services Web Language Model REST API")
    (description
     "R Client for the Microsoft Cognitive Services Web Language Model REST API,
including Break Into Words, Calculate Conditional Probability, Calculate Joint
Probability, Generate Next Words, and List Available Models.  A valid account
MUST be registered at the Microsoft Cognitive Services website
<https://www.microsoft.com/cognitive-services/> in order to obtain a (free) API
key.  Without an API key, this package will not work properly.")
    (license license:expat)))

(define-public r-mscstts
  (package
    (name "r-mscstts")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mscstts" version))
       (sha256
        (base32 "1yqb9p7404yh9bjjjpy83k7yssvvpn3v9vddy47pgwjv4hbpbq19"))))
    (properties `((upstream-name . "mscstts")))
    (build-system r-build-system)
    (propagated-inputs (list r-tuner r-jsonlite r-httr))
    (home-page "https://github.com/muschellij2/mscstts")
    (synopsis
     "R Client for the Microsoft Cognitive Services 'Text-to-Speech' REST API")
    (description
     "R Client for the Microsoft Cognitive Services Text-to-Speech REST API, including
voice synthesis.  A valid account must be registered at the Microsoft Cognitive
Services website <https://azure.microsoft.com/services/cognitive-services/> in
order to obtain a (free) API key.  Without an API key, this package will not
work properly.")
    (license license:gpl3)))

(define-public r-mscstexta4r
  (package
    (name "r-mscstexta4r")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mscstexta4r" version))
       (sha256
        (base32 "1hjcasmn33xav2mw085lcndv0432l6sz327aikf63491wj1sj7mw"))))
    (properties `((upstream-name . "mscstexta4r")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi r-pander r-jsonlite r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/philferriere/mscstexta4r")
    (synopsis
     "R Client for the Microsoft Cognitive Services Text Analytics REST API")
    (description
     "R Client for the Microsoft Cognitive Services Text Analytics REST API, including
Sentiment Analysis, Topic Detection, Language Detection, and Key Phrase
Extraction.  An account MUST be registered at the Microsoft Cognitive Services
website <https://www.microsoft.com/cognitive-services/> in order to obtain a
(free) API key.  Without an API key, this package will not work properly.")
    (license license:expat)))

(define-public r-mscsimtester
  (package
    (name "r-mscsimtester")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSCsimtester" version))
       (sha256
        (base32 "0k04fdprwa56vm6n372lipybgq1plhmjbxvp58wqmpgzpnhcxkvd"))))
    (properties `((upstream-name . "MSCsimtester")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-ksamples r-ape))
    (home-page "https://cran.r-project.org/package=MSCsimtester")
    (synopsis "Tests of Multispecies Coalescent Gene Tree Simulator Output")
    (description
     "Statistical tests for validating multispecies coalescent gene tree simulators,
using pairwise distances and rooted triple counts.  Background is given by
Allman, Banos, and Rhodes (2019) <@code{arXiv:1908.01424>}.")
    (license license:expat)))

(define-public r-mscquartets
  (package
    (name "r-mscquartets")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSCquartets" version))
       (sha256
        (base32 "1d1bgvwz88q2sipqzhiybwflixb45nc1fw9isf7xbhq18msr9vmh"))))
    (properties `((upstream-name . "MSCquartets")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr
                             r-rdpack
                             r-rcppprogress
                             r-rcpp
                             r-phangorn
                             r-igraph
                             r-foreach
                             r-doparallel
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MSCquartets")
    (synopsis
     "Analyzing Gene Tree Quartets under the Multi-Species Coalescent")
    (description
     "This package provides methods for analyzing and using quartets displayed on a
collection of gene trees, primarily to make inferences about the species tree or
network under the multi-species coalescent model.  These include quartet
hypothesis tests for the model, as developed by Mitchell et al. (2019)
<doi:10.1214/19-EJS1576>, simplex plots of quartet concordance factors as
presented by Allman et al. (2020) <doi:10.1101/2020.02.13.948083>, species tree
inference methods based on quartet distances of Rhodes (2019)
<doi:10.1109/TCBB.2019.2917204> and Yourdkhani and Rhodes (2019)
<doi:10.1007/s11538-020-00773-4>, the NANUQ algorithm for inference of level-1
species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>, and
the TINNIK algorithm for inference of the tree of blobs of an arbitrary network
of Allman et al.(2022) <doi:10.1007/s00285-022-01838-9>.  Software announcement
by Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.")
    (license license:expat)))

(define-public r-mscp
  (package
    (name "r-mscp")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mscp" version))
       (sha256
        (base32 "18cy7nnvhpjl874bawsh8j1gry9wx134q6gk6j03s6546bzy8lgc"))))
    (properties `((upstream-name . "mscp")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mscp")
    (synopsis
     "Multiscale Change Point Detection via Gradual Bandwidth Adjustment in Moving Sum Processes")
    (description
     "Multiscale moving sum procedure for the detection of changes in expectation in
univariate sequences.  References - Multiscale change point detection via
gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic
and Michael Messer.")
    (license license:gpl3)))

(define-public r-mscombine
  (package
    (name "r-mscombine")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MScombine" version))
       (sha256
        (base32 "0s3gbf3s5mf00d5mkbqhd9wwnyps7qb4r3zbal8j8nsaspql9nd3"))))
    (properties `((upstream-name . "MScombine")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr))
    (home-page "https://cran.r-project.org/package=MScombine")
    (synopsis
     "Combine Data from Positive and Negative Ionization Mode Finding Common Entities")
    (description
     "Find common entities detected in both positive and negative ionization mode,
delete this entity in the less sensible mode and combine both matrices.")
    (license license:gpl2)))

(define-public r-mscmt
  (package
    (name "r-mscmt")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSCMT" version))
       (sha256
        (base32 "1mjm85pxxww6m7mfk8xfwhx65c64k4v6pjpvbasv5jhy49kfjgw4"))))
    (properties `((upstream-name . "MSCMT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rglpk r-rdpack r-lpsolveapi r-lpsolve r-ggplot2))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=MSCMT")
    (synopsis "Multivariate Synthetic Control Method Using Time Series")
    (description
     "Three generalizations of the synthetic control method (which has already an
implementation in package Synth') are implemented: first, MSCMT allows for using
multiple outcome variables, second, time series can be supplied as economic
predictors, and third, a well-defined cross-validation approach can be used.
Much effort has been taken to make the implementation as stable as possible
(including edge cases) without losing computational efficiency.  A detailed
description of the main algorithms is given in Becker and KlÃ¶Ãner (2018)
<doi:10.1016/j.ecosta.2017.08.002>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-msclust
  (package
    (name "r-msclust")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSclust" version))
       (sha256
        (base32 "1ra9y9njcz21s2p63gisi3vgjjqzdnn9vvp0qfszkvpm0kkl0487"))))
    (properties `((upstream-name . "MSclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych
                             r-mvtnorm
                             r-mnormt
                             r-mclust
                             r-matrix
                             r-gtools
                             r-ggplot2
                             r-ggally
                             r-cluster))
    (home-page "https://cran.r-project.org/package=MSclust")
    (synopsis "Multiple-Scaled Clustering")
    (description
     "Model based clustering using the multivariate multiple Scaled t (MST) and
multivariate multiple scaled contaminated normal (MSCN) distributions.  The MST
is an extension of the multivariate Student-t distribution to include flexible
tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>.
The MSCN represents a heavy-tailed generalization of the multivariate normal
(MN) distribution to model elliptical contoured scatters in the presence of mild
outliers (also referred to as \"bad\" points) and automatically detect bad points,
Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.")
    (license license:gpl2+)))

(define-public r-msclassifr
  (package
    (name "r-msclassifr")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSclassifR" version))
       (sha256
        (base32 "1qn79awr6yiywiz9k1wx6bqml1ssjijg1k2rm4blvlbd26yk5lbr"))))
    (properties `((upstream-name . "MSclassifR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-vsurf
                             r-vita
                             r-ubl
                             r-statmod
                             r-reshape2
                             r-randomforest
                             r-performanceestimation
                             r-nnet
                             r-mltools
                             r-mixomics
                             r-metap
                             r-mclust
                             r-maldirppa
                             r-maldiquantforeign
                             r-maldiquant
                             r-limma
                             r-glmnet
                             r-ggplot2
                             r-fuzzyjoin
                             r-e1071
                             r-dplyr
                             r-cp4p
                             r-caret
                             r-car))
    (home-page "https://github.com/agodmer/MSclassifR_examples")
    (synopsis "Automated Classification of Mass Spectra")
    (description
     "This package provides functions to classify mass spectra in known categories,
and to determine discriminant mass-over-charge values.  It includes easy-to-use
functions for pre-processing mass spectra, functions to determine discriminant
mass-over-charge values (m/z) from a library of mass spectra corresponding to
different categories, and functions to predict the category (species,
phenotypes, etc.) associated to a mass spectrum from a list of selected
mass-over-charge values.  Three vignettes illustrating how to use the functions
of this package from real data sets are also available online to help users:
<https://agodmer.github.io/M@code{SclassifR_examples/Vignettes/Vignettemsclassifr_Ecrobiav3.html>},
<https://agodmer.github.io/M@code{SclassifR_examples/Vignettes/Vignettemsclassifr_Klebsiellav3.html>}
and
<https://agodmer.github.io/M@code{SclassifR_examples/Vignettes/Vignettemsclassifr_DAv3.html>}.")
    (license license:gpl3+)))

(define-public r-mschart
  (package
    (name "r-mschart")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mschart" version))
       (sha256
        (base32 "0znylmkzqsk9qw640bhqnn4np748s9q1zcwgyavb3b808nfcv6ri"))))
    (properties `((upstream-name . "mschart")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-writexl
                             r-officer
                             r-htmltools
                             r-data-table
                             r-cellranger))
    (home-page "https://ardata-fr.github.io/officeverse/")
    (synopsis
     "Chart Generation for 'Microsoft Word' and 'Microsoft PowerPoint' Documents")
    (description
     "Create native charts for Microsoft @code{PowerPoint} and Microsoft Word
documents.  These can then be edited and annotated.  Functions are provided to
let users create charts, modify and format their content.  The chart's
underlying data is automatically saved within the Word document or
@code{PowerPoint} presentation.  It extends package officer that does not
contain any feature for Microsoft native charts production.")
    (license license:expat)))

(define-public r-msce
  (package
    (name "r-msce")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msce" version))
       (sha256
        (base32 "1x9ivc2bjp07pdn15p3qds00m1m00amcg2dfcdjsgbkx9x1kkfab"))))
    (properties `((upstream-name . "msce")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=msce")
    (synopsis "Hazard of Multi-Stage Clonal Expansion Models")
    (description
     "This package provides functions to calculate hazard and survival function of
Multi-Stage Clonal Expansion Models used in cancer epidemiology.  For the
Two-Stage Clonal Expansion Model an exact solution is implemented assuming
piecewise constant parameters.  Numerical solutions are provided for its
extensions.")
    (license license:gpl2+)))

(define-public r-msbstatsdata
  (package
    (name "r-msbstatsdata")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSBStatsData" version))
       (sha256
        (base32 "06k7fy327d0jrzjcc35lwzl0iax40wm2sfrqwpgw0rh88yaxjzvv"))))
    (properties `((upstream-name . "MSBStatsData")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://cran.r-project.org/package=MSBStatsData")
    (synopsis "Data Sets for Courses at the MÃ¼nster School of Business")
    (description
     "This package provides sample data sets that are used in statistics and data
science courses at the MÃ¼nster School of Business.  The datasets refer to
different business topics but also other domains, e.g. sports, traffic, etc.")
    (license license:gpl3+)))

(define-public r-msbp
  (package
    (name "r-msbp")
    (version "1.4-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msBP" version))
       (sha256
        (base32 "1n833ghqh9n5m2qx8njsq5njbcap67y1v71a0vm1p2igh0f8b54i"))))
    (properties `((upstream-name . "msBP")))
    (build-system r-build-system)
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=msBP")
    (synopsis "Multiscale Bernstein Polynomials for Densities")
    (description
     "This package performs Bayesian nonparametric multiscale density estimation and
multiscale testing of group differences with multiscale Bernstein polynomials
(@code{msBP}) mixtures as in Canale and Dunson (2016).")
    (license license:gpl2)))

(define-public r-msbox
  (package
    (name "r-msbox")
    (version "1.4.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MSbox" version))
       (sha256
        (base32 "1cppzy3kri2zmhyk34scffix28v1bjb0fhk51kb33amg6vz5mnj3"))))
    (properties `((upstream-name . "MSbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-stringr r-crayon))
    (home-page "https://github.com/YonghuiDong/MSbox")
    (synopsis "Mass Spectrometry Tools")
    (description
     "Common mass spectrometry tools described in John Roboz (2013)
<doi:10.1201/b15436>.  It allows checking element isotopes, calculating (isotope
labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting
possible contaminant mass peaks, examining possible adducts in electrospray
ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) ion
sources.")
    (license license:gpl2)))

(define-public r-msar
  (package
    (name "r-msar")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msaR" version))
       (sha256
        (base32 "12d6vql85d1mzcy889z7pbmldcwwmzijimcr9frm12ahmka81byx"))))
    (properties `((upstream-name . "msaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmlwidgets r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=msaR")
    (synopsis "Multiple Sequence Alignment for R Shiny")
    (description
     "Visualizes multiple sequence alignments dynamically within the Shiny web
application framework.")
    (license (license:fsdg-compatible "BSL-1.0"))))

(define-public r-msamp
  (package
    (name "r-msamp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msamp" version))
       (sha256
        (base32 "0r5m50x8l5dijy7nzcbxl3ipfnkgvaa9r580b6b71ny3madsc8rh"))))
    (properties `((upstream-name . "msamp")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=msamp")
    (synopsis
     "Estimate Sample Size to Detect Bacterial Contamination in a Product Lot")
    (description
     "Estimates the sample size needed to detect microbial contamination in a lot with
a user-specified detection probability and user-specified analytical
sensitivity.  Various patterns of microbial contamination are accounted for:
homogeneous (Poisson), heterogeneous (Poisson-Gamma) or localized(Zero-inflated
Poisson).  Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004>
\"Impact of microbial distributions on food safety\".  Leroy Simon (1963)
<doi:10.1017/S0515036100001975> \"Casualty Actuarial Society - The Negative
Binomial and Poisson Distributions Compared\".")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-msaface
  (package
    (name "r-msaface")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msaFACE" version))
       (sha256
        (base32 "1lh8crz507nlddm946lmn73xbnsh4cs0kg238ysnsvxs726c83fs"))))
    (properties `((upstream-name . "msaFACE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=msaFACE")
    (synopsis "Moving Subset Analysis FACE")
    (description
     "The new methodology \"moving subset analysis\" provides functions to investigate
the effect of environmental conditions on the CO2 fertilization effect within
longterm free air carbon enrichment (FACE) experiments.  In general, the
functionality is applicable to derive the influence of a third variable (forcing
experiment-support variable) on the relation between a dependent and an
independent variable.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-msaenet
  (package
    (name "r-msaenet")
    (version "3.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msaenet" version))
       (sha256
        (base32 "1gs5mcwdkc3wwaa49liywsrl8454801i6jiylfksyw1rlqlcnz9p"))))
    (properties `((upstream-name . "msaenet")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-ncvreg
                             r-mvtnorm
                             r-matrix
                             r-glmnet
                             r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://nanx.me/msaenet/")
    (synopsis "Multi-Step Adaptive Estimation Methods for Sparse Regressions")
    (description
     "Multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in
high-dimensional regressions proposed in Xiao and Xu (2015)
<DOI:10.1080/00949655.2015.1016944>, with support for multi-step adaptive
MCP-net (MSAMNet) and multi-step adaptive SCAD-net (MSASNet) methods.")
    (license license:gpl3+)))

(define-public r-msaehb
  (package
    (name "r-msaehb")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msaeHB" version))
       (sha256
        (base32 "1grmkr146csyim1lpgingjnp4km3sqlpzihln9cpkqaqhvl8rycj"))))
    (properties `((upstream-name . "msaeHB")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-coda))
    (home-page "https://cran.r-project.org/package=msaeHB")
    (synopsis
     "Multivariate Small Area Estimation using Hierarchical Bayesian Method")
    (description
     "This package implements area level of multivariate small area estimation using
Hierarchical Bayesian method under Normal and T distribution.  The rjags package
is employed to obtain parameter estimates.  For the reference, see Rao and
Molina (2015) <doi:10.1002/9781118735855>.")
    (license license:gpl3)))

(define-public r-msae
  (package
    (name "r-msae")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "msae" version))
       (sha256
        (base32 "0ra4qj0yvyi4nl492hqj7l6cpyxwm1hq7mm15sip4rk2fm7h0kjf"))))
    (properties `((upstream-name . "msae")))
    (build-system r-build-system)
    (propagated-inputs (list r-magic))
    (home-page "https://cran.r-project.org/package=msae")
    (synopsis "Multivariate Fay Herriot Models for Small Area Estimation")
    (description
     "This package implements multivariate Fay-Herriot models for small area
estimation.  It uses empirical best linear unbiased prediction (EBLUP)
estimator.  Multivariate models consider the correlation of several target
variables and borrow strength from auxiliary variables to improve the
effectiveness of a domain sample size.  Models which accommodated by this
package are univariate model with several target variables (model 0),
multivariate model (model 1), autoregressive multivariate model (model 2), and
heteroscedastic autoregressive multivariate model (model 3).  Functions provide
EBLUP estimators and mean squared error (MSE) estimator for each model.  These
models were developed by Roberto Benavent and Domingo Morales (2015)
<doi:10.1016/j.csda.2015.07.013>.")
    (license license:gpl2)))

(define-public r-mrzero
  (package
    (name "r-mrzero")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRZero" version))
       (sha256
        (base32 "0bjxy7rcgcsy2d0kg5v230l5z0hd72jq3z7692km9sbhh3bq51bf"))))
    (properties `((upstream-name . "MRZero")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase
                             r-rmarkdown
                             r-quantreg
                             r-plotly
                             r-knitr
                             r-glmnet
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=MRZero")
    (synopsis "Diet Mendelian Randomization")
    (description
     "Encodes several methods for performing Mendelian randomization analyses with
summarized data.  Similar to the @code{MendelianRandomization} package, but with
fewer bells and whistles, and less frequent updates.  As described in Yavorska
(2017) <doi:10.1093/ije/dyx034> and Broadbent (2020)
<doi:10.12688/wellcomeopenres.16374.2>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mrtsamplesizebinary
  (package
    (name "r-mrtsamplesizebinary")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRTSampleSizeBinary" version))
       (sha256
        (base32 "1kb88sdhb8pp4mmvz8fy08drslzxj58fh1ycqaa3da6j7s9x65bn"))))
    (properties `((upstream-name . "MRTSampleSizeBinary")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MRTSampleSizeBinary")
    (synopsis "Sample Size Calculator for MRT with Binary Outcomes")
    (description
     "This package provides a sample size calculator for micro-randomized trials
(MRTs) with binary outcomes based on Cohn et al. (2023) <doi:10.1002/sim.9748>.
Also provides a power calculator when the sample size is input by the user.")
    (license license:gpl3)))

(define-public r-mrtsamplesize
  (package
    (name "r-mrtsamplesize")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRTSampleSize" version))
       (sha256
        (base32 "03c6jkanff241rcv1yixvmlnya3sd8hqpawf9dx9mpsri4pp6mdk"))))
    (properties `((upstream-name . "MRTSampleSize")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MRTSampleSize")
    (synopsis "Sample Size Calculator for Micro-Randomized Trials")
    (description
     "Provide a sample size calculator for micro-randomized trials (MRTs) based on
methodology developed in Sample Size Calculations for Micro-randomized Trials in
@code{mHealth} by Liao et al. (2016) <DOI:10.1002/sim.6847>.")
    (license license:gpl2+)))

(define-public r-mrtanalysis
  (package
    (name "r-mrtanalysis")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRTAnalysis" version))
       (sha256
        (base32 "19s30k2crlnracwld0c1dhkvnwfyb32bhr06w7mbxm748lwhkwdh"))))
    (properties `((upstream-name . "MRTAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-rootsolve r-geepack))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MRTAnalysis")
    (synopsis "Primary and Secondary Analyses for Micro-Randomized Trials")
    (description
     "Estimates marginal causal excursion effects and moderated causal excursion
effects for micro-randomized trial (MRT).  Applicable to MRT with binary
treatment options and continuous or binary outcomes.  The method for MRT with
continuous outcomes is the weighted centered least squares (WCLS) by Boruvka et
al. (2018) <doi:10.1080/01621459.2017.1305274>.  The method for MRT with binary
outcomes is the estimator for marginal excursion effect (EMEE) by Qian et al.
(2021) <doi:10.1093/biomet/asaa070>.")
    (license license:gpl3)))

(define-public r-mrs
  (package
    (name "r-mrs")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRS" version))
       (sha256
        (base32 "0rw3gmjn35118f1v1i8wa56zmz0cz2qbjahic87ylbyamnfsxz3l"))))
    (properties `((upstream-name . "MRS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-igraph))
    (home-page "https://cran.r-project.org/package=MRS")
    (synopsis "Multi-Resolution Scanning for Cross-Sample Differences")
    (description
     "An implementation of the MRS algorithm for comparison across distributions, as
described in Jacopo Soriano, Li Ma (2017) <doi:10.1111/rssb.12180>.  The model
is based on a nonparametric process taking the form of a Markov model that
transitions between a \"null\" and an \"alternative\" state on a multi-resolution
partition tree of the sample space.  MRS effectively detects and characterizes a
variety of underlying differences.  These differences can be visualized using
several plotting functions.")
    (license license:gpl3+)))

(define-public r-mrregression
  (package
    (name "r-mrregression")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrregression" version))
       (sha256
        (base32 "1yy2sjs0b67bqyggzypp2g7x4fdmi86jx2nbcp2n7hcf9cdacysi"))))
    (properties `((upstream-name . "mrregression")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-data-table))
    (home-page "https://cran.r-project.org/package=mrregression")
    (synopsis
     "Regression Analysis for Very Large Data Sets via Merge and Reduce")
    (description
     "Frequentist and Bayesian linear regression for large data sets.  Useful when the
data does not fit into memory (for both frequentist and Bayesian regression), to
make running time manageable (mainly for Bayesian regression), and to reduce the
total running time because of reduced or less severe memory-spillover into the
virtual memory.  This is an implementation of Merge & Reduce for linear
regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler,
C. (2020).  Streaming statistical models via Merge & Reduce'.  International
Journal of Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mrreg
  (package
    (name "r-mrreg")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRReg" version))
       (sha256
        (base32 "0705hc85hhk7nd4ygvkswz7drdgia86v6svk85vqa5p899bsam14"))))
    (properties `((upstream-name . "MRReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/DarkEyes/MRReg")
    (synopsis "MDL Multiresolution Linear Regression Framework")
    (description
     "We provide the framework to analyze multiresolution partitions (e.g. country,
provinces, subdistrict) where each individual data point belongs to only one
partition in each layer (e.g. i belongs to subdistrict A, province P, and
country Q).  We assume that a partition in a higher layer subsumes lower-layer
partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd
layer).  Given N individuals that have a pair of real values (x,y) that
generated from independent variable X and dependent variable Y. Each individual
i belongs to one partition per layer.  Our goal is to find which partitions at
which highest level that all individuals in the these partitions share the same
linear model Y=f(X) where f is a linear function.  The framework deploys the
Minimum Description Length principle (MDL) to infer solutions.  The publication
of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon
Plangprasopchok, and Suttipong Thajchayapong (2021) <doi:10.1145/3424670>.")
    (license license:expat)))

(define-public r-mrqol
  (package
    (name "r-mrqol")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRQoL" version))
       (sha256
        (base32 "0isn4g3jpz7wm99ymrshl6zgkb7iancdzdxl2w98n8fbxsh5z6sw"))))
    (properties `((upstream-name . "MRQoL")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MRQoL")
    (synopsis
     "Minimal Clinically Important Difference and Response Shift Effect for Health-Related Quality of Life")
    (description
     "We can calculate directly used this package the Minimal Clinically Important
Difference by applying the Anchor-based method and the Response shift effect by
applying the Then-Test method.")
    (license license:gpl2+)))

(define-public r-mrpostman
  (package
    (name "r-mrpostman")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mRpostman" version))
       (sha256
        (base32 "0jg1mgcqcsms0ggirxxwphjhmwbwsjsb05frzwlnrri3iz4sdym0"))))
    (properties `((upstream-name . "mRpostman")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stringr
                             r-stringi
                             r-rvest
                             r-r6
                             r-magrittr
                             r-curl
                             r-base64enc
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://allanvc.github.io/mRpostman/")
    (synopsis "An IMAP Client for R")
    (description
     "An easy-to-use IMAP client that provides tools for message searching, selective
fetching of message attributes, mailbox management, attachment extraction, and
several other IMAP features, paving the way for e-mail data analysis in R.")
    (license license:gpl3)))

(define-public r-mrpc
  (package
    (name "r-mrpc")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRPC" version))
       (sha256
        (base32 "1hsrcqp4yygz2psxj9m2n1z7pivdf82b3rnqc34ripyql5apmikn"))))
    (properties `((upstream-name . "MRPC")))
    (build-system r-build-system)
    (propagated-inputs (list r-wgcna
                             r-sna
                             r-rgraphviz
                             r-psych
                             r-plyr
                             r-pcalg
                             r-network
                             r-mice
                             r-hmisc
                             r-gtools
                             r-graph
                             r-ggally
                             r-fastcluster
                             r-dynamictreecut
                             r-compositions
                             r-bnlearn))
    (home-page "https://cran.r-project.org/package=MRPC")
    (synopsis "PC Algorithm with the Principle of Mendelian Randomization")
    (description
     "This package provides a PC Algorithm with the Principle of Mendelian
Randomization.  This package implements the MRPC (PC with the principle of
Mendelian randomization) algorithm to infer causal graphs.  It also contains
functions to simulate data under a certain topology, to visualize a graph in
different ways, and to compare graphs and quantify the differences.  See Badsha
and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021)
<doi:10.3389/fgene.2021.651812>.")
    (license license:gpl2+)))

(define-public r-mro
  (package
    (name "r-mro")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mro" version))
       (sha256
        (base32 "00gc2hd8q5hb2xrswclcqqw1gxl83zmpma6bhsggg3kppsw1dpjv"))))
    (properties `((upstream-name . "mro")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-mass))
    (home-page "https://cran.r-project.org/package=mro")
    (synopsis "Multiple Correlation")
    (description
     "Computes multiple correlation coefficient when the data matrix is given and
tests its significance.")
    (license license:gpl2)))

(define-public r-mrmre
  (package
    (name "r-mrmre")
    (version "2.1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mRMRe" version))
       (sha256
        (base32 "1nbz2rq6fzzy01vhizpszw47c8yha1rvks6hf5dh0dx4h8p3jg6m"))))
    (properties `((upstream-name . "mRMRe")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-igraph))
    (home-page "https://www.pmgenomics.ca/bhklab/")
    (synopsis "Parallelized Minimum Redundancy, Maximum Relevance (mRMR)")
    (description
     "Computes mutual information matrices from continuous, categorical and survival
variables, as well as feature selection with minimum redundancy, maximum
relevance (@code{mRMR}) and a new ensemble @code{mRMR} technique.  Published in
De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.")
    (license license:artistic2.0)))

(define-public r-mrmlm-gui
  (package
    (name "r-mrmlm-gui")
    (version "4.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrMLM.GUI" version))
       (sha256
        (base32 "0ywxaw164czfgbnqfkh5zw06006clisbc780w2h4z71wfvj56dsi"))))
    (properties `((upstream-name . "mrMLM.GUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs
                             r-shiny
                             r-sbl
                             r-sampling
                             r-rcppeigen
                             r-rcpp
                             r-ncvreg
                             r-mrmlm
                             r-lars
                             r-foreach
                             r-doparallel
                             r-data-table
                             r-coin
                             r-bigmemory))
    (home-page "https://cran.r-project.org/package=mrMLM.GUI")
    (synopsis
     "Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for Genome-Wide Association Study with Graphical User Interface")
    (description
     "Conduct multi-locus genome-wide association study under the framework of
multi-locus random-SNP-effect mixed linear model (@code{mrMLM}).  First, each
marker on the genome is scanned.  Bonferroni correction is replaced by a less
stringent selection criterion for significant test.  Then, all the markers that
are potentially associated with the trait are included in a multi-locus genetic
model, their effects are estimated by empirical Bayes and all the nonzero
effects were further identified by likelihood ratio test for true QTL. Wen YJ,
Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R
(2018) <doi:10.1093/bib/bbw145>.")
    (license license:gpl2+)))

(define-public r-mrmlm
  (package
    (name "r-mrmlm")
    (version "5.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrMLM" version))
       (sha256
        (base32 "0z2vwvpc7yrxza3jy3x3mj7d3n10bfbq72rcsnrjrz2jkcbkwb8g"))))
    (properties `((upstream-name . "mrMLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-sbl
                             r-sampling
                             r-rcppeigen
                             r-rcpp
                             r-ncvreg
                             r-lars
                             r-foreach
                             r-doparallel
                             r-data-table
                             r-coin
                             r-bedmatrix))
    (home-page "https://cran.r-project.org/package=mrMLM")
    (synopsis
     "Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for GWAS")
    (description
     "Conduct multi-locus genome-wide association study under the framework of
multi-locus random-SNP-effect mixed linear model (@code{mrMLM}).  First, each
marker on the genome is scanned.  Bonferroni correction is replaced by a less
stringent selection criterion for significant test.  Then, all the markers that
are potentially associated with the trait are included in a multi-locus genetic
model, their effects are estimated by empirical Bayes, and all the nonzero
effects were further identified by likelihood ratio test for significant QTL.
The program may run on a desktop or laptop computers.  If marker genotypes in
association mapping population are almost homozygous, these methods in this
software are very effective.  If there are many heterozygous marker genotypes,
the III@code{VmrMLM} software is recommended.  Wen YJ, Zhang H, Ni YL, Huang B,
Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018,
<doi:10.1093/bib/bbw145>), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou
YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, <doi:10.1016/j.molp.2022.02.012>).")
    (license license:gpl2+)))

(define-public r-mrmediation
  (package
    (name "r-mrmediation")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRmediation" version))
       (sha256
        (base32 "0v0kn6x4fwj35imj1fb2f2024ifb7081z4cwlhjmdxhabcy6xbx7"))))
    (properties `((upstream-name . "MRmediation")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-fda))
    (home-page "https://cran.r-project.org/package=MRmediation")
    (synopsis
     "Causal Mediation Method with Methylated Region (MR) as the Mediator")
    (description
     "This package provides a causal mediation approach under the counterfactual
framework to test the significance of total, direct and indirect effects.  In
this approach, a group of methylated sites from a predefined region are utilized
as the mediator, and the functional transformation is used to reduce the
possible high dimension in the region-based methylated sites and account for
their location information.")
    (license license:gpl2+)))

(define-public r-mrmcsamplesize
  (package
    (name "r-mrmcsamplesize")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRMCsamplesize" version))
       (sha256
        (base32 "1wpgc0gfp5sv18l3kkw9kw1vrijnjjz163vsk8q02g64zcm7k78q"))))
    (properties `((upstream-name . "MRMCsamplesize")))
    (build-system r-build-system)
    (propagated-inputs (list r-fpow))
    (home-page "https://github.com/technOslerphile/MRMCsamplesize")
    (synopsis
     "Sample Size Estimations for Planning Multi-Reader Multi-Case (MRMC) Studies Without Pilot Data")
    (description
     "Sample size estimations for MRMC studies based on the Obuchowski-Rockette (OR)
methodology is implemented.  The function can calculate sample sizes where the
endpoint of interest in the study is either ROC AUC
(Area-Under-the-Receiver-Operating-Characteristics-Curve) or sensitivity.  The
package can also return sample sizes for studies expected to have clustering
effect (e.g.- multiple pulmonary nodules per patient).  All calculations assume
that the study design is fully crossed (paired-reader, paired-case) where each
reader reads/interprets each case and that there are two
interventions/imaging-modalities/techniques in the study.  In addition to MRMC,
it can also be used to estimate sample sizes for standalone studies where
sensitivity or AUC are the primary endpoints.  The methods implemented are based
on the methods described in Zhou et.al. (2011) <doi:10.1002/9780470906514> and
Obuchowski (2000) <doi:10.1097/EDE.0b013e3181a663cc>.")
    (license license:expat)))

(define-public r-mrmcaov
  (package
    (name "r-mrmcaov")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRMCaov" version))
       (sha256
        (base32 "0nq4i1ymv3ifp39liz99l51605q2iiin75a02wp9pyaxf6k7b5hd"))))
    (properties `((upstream-name . "MRMCaov")))
    (build-system r-build-system)
    (propagated-inputs (list r-trust r-tibble r-progress r-mvtnorm r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brian-j-smith/MRMCaov")
    (synopsis "Multi-Reader Multi-Case Analysis of Variance")
    (description
     "Estimation and comparison of the performances of diagnostic tests in
multi-reader multi-case studies where true case statuses (or ground truths) are
known and one or more readers provide test ratings for multiple cases.  Reader
performance metrics are provided for area under and expected utility of ROC
curves, likelihood ratio of positive or negative tests, and sensitivity and
specificity.  ROC curves can be estimated empirically or with binormal or
binormal likelihood-ratio models.  Statistical comparisons of diagnostic tests
are based on the ANOVA model of Obuchowski-Rockette and the unified framework of
Hillis (2005) <doi:10.1002/sim.2024>.  The ANOVA can be conducted with data from
a full factorial, nested, or partially paired study design; with random or fixed
readers or cases; and covariances estimated with the @code{DeLong} method,
jackknifing, or an unbiased method.  Smith and Hillis (2020)
<doi:10.1117/12.2549075>.")
    (license license:gpl3)))

(define-public r-mri
  (package
    (name "r-mri")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mri" version))
       (sha256
        (base32 "1dssq556kid6c3djp0s6v853cranv2wjs2c9521l5ykg5g33hfmv"))))
    (properties `((upstream-name . "mri")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mri")
    (synopsis "Modified Rand and Wallace Indices")
    (description
     "It provides functions to compute the values of different modifications of the
Rand and Wallace indices.  The indices are used to measure the stability or
similarity of two partitions obtained on two different sets of units with a
non-empty intercept.  Splitting and merging of clusters can (depends on the
selected index) have a different effect on the value of the indices.  The
indices are proposed in Cugmas and Ferligoj (2018)
<http://ibmi.mf.uni-lj.si/mz/2018/no-1/Cugmas2018.pdf>.")
    (license license:gpl2+)))

(define-public r-mrhawkes
  (package
    (name "r-mrhawkes")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRHawkes" version))
       (sha256
        (base32 "0qhwm708jijv1w6iml18bg7fc2igdcmydwpb9dzpa4gwwads8b78"))))
    (properties `((upstream-name . "MRHawkes")))
    (build-system r-build-system)
    (propagated-inputs (list r-ihsep))
    (home-page "https://cran.r-project.org/package=MRHawkes")
    (synopsis "Multivariate Renewal Hawkes Process")
    (description
     "Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) self-exciting
process, with given immigrant hazard rate functions and offspring density
function.  Calculate the likelihood of a MRHawkes process with given hazard rate
functions and offspring density function for an (increasing) sequence of event
times.  Calculate the Rosenblatt residuals of the event times.  Predict future
event times based on observed event times up to a given time.  For details see
Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.")
    (license license:gpl2+)))

(define-public r-mrgsolve
  (package
    (name "r-mrgsolve")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrgsolve" version))
       (sha256
        (base32 "19c3bq58sca4i3iizpnvkgig62bhnq4c4lwi8gxihv00vrl9lil2"))))
    (properties `((upstream-name . "mrgsolve")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tibble
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-magrittr
                             r-lifecycle
                             r-glue
                             r-dplyr
                             r-bh))
    (home-page "https://mrgsolve.org/docs/")
    (synopsis "Simulate from ODE-Based Models")
    (description
     "Fast simulation from ordinary differential equation (ODE) based models typically
employed in quantitative pharmacology and systems biology.")
    (license license:gpl2+)))

(define-public r-mrgsim-sa
  (package
    (name "r-mrgsim-sa")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrgsim.sa" version))
       (sha256
        (base32 "1ag6zxfyrcm1lipab6kpjbphvbamgyv3wgz8yf0qdj5dy0ra06hy"))))
    (properties `((upstream-name . "mrgsim.sa")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-patchwork
                             r-mrgsolve
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kylebaron/mrgsim.sa")
    (synopsis "Sensitivity Analysis with 'mrgsolve'")
    (description
     "Perform sensitivity analysis on ordinary differential equation based models,
including ad-hoc graphical analyses based on structured sequences of parameters
as well as local sensitivity analysis.  Functions are provided for creating
inputs, simulating scenarios and plotting outputs.")
    (license license:gpl2+)))

(define-public r-mrgsim-parallel
  (package
    (name "r-mrgsim-parallel")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrgsim.parallel" version))
       (sha256
        (base32 "074c47fkwy5n9x89dswi3ybnck1f0rlbyad6by32jyslw8bz76ln"))))
    (properties `((upstream-name . "mrgsim.parallel")))
    (build-system r-build-system)
    (propagated-inputs (list r-mrgsolve
                             r-future-apply
                             r-future
                             r-fst
                             r-dplyr
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kylebaron/mrgsim.parallel")
    (synopsis "Simulate with 'mrgsolve' in Parallel")
    (description
     "Simulation from an mrgsolve <https://cran.r-project.org/package=mrgsolve> model
using a parallel backend.  Input data sets are split (chunked) and simulated in
parallel using mclapply() or future_lapply()
<https://cran.r-project.org/package=future.apply>.")
    (license license:gpl2+)))

(define-public r-mrg
  (package
    (name "r-mrg")
    (version "0.2.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRG" version))
       (sha256
        (base32 "07db15gpxpyq5ai5gj292kywissw42czm2l2wdnr6xczw6wjpmq8"))))
    (properties `((upstream-name . "MRG")))
    (build-system r-build-system)
    (propagated-inputs (list r-vardpoor
                             r-tidyselect
                             r-tidyr
                             r-terra
                             r-stars
                             r-sjmisc
                             r-sf
                             r-rlang
                             r-purrr
                             r-plyr
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MRG")
    (synopsis "Create Non-Confidential Multi-Resolution Grids")
    (description
     "Functionality for creating gridded data, respecting the confidentiality rules,
such as a minimum number of units and dominance by one or more units in the grid
cell.  The functions also include the possibility for contextual suppression of
data.")
    (license license:gpl3+)))

(define-public r-mrfse
  (package
    (name "r-mrfse")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrfse" version))
       (sha256
        (base32 "1kk6j7x4z4vaiwnd2mv2brzd1z7g4kyxxylxf31971i6klnsn4zb"))))
    (properties `((upstream-name . "mrfse")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-rcpp r-gtools))
    (home-page "https://cran.r-project.org/package=mrfse")
    (synopsis "Markov Random Field Structure Estimator")
    (description
     "Three algorithms for estimating a Markov random field structure.Two of them are
an exact version and a simulated annealing version of a penalized maximum
conditional likelihood method similar to the Bayesian Information Criterion.
These algorithm are described in Frondana (2016)
<doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy
algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).")
    (license license:gpl3+)))

(define-public r-mrfdepth
  (package
    (name "r-mrfdepth")
    (version "1.0.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrfDepth" version))
       (sha256
        (base32 "05k7pzjry3dd7api52wlybl7k83jq0v6a306sdl9wcsqv95lw0mc"))))
    (properties `((upstream-name . "mrfDepth")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-matrixstats
                             r-ggplot2
                             r-geometry
                             r-abind))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=mrfDepth")
    (synopsis
     "Depth Measures in Multivariate, Regression and Functional Settings")
    (description
     "This package provides tools to compute depth measures and implementations of
related tasks such as outlier detection, data exploration and classification of
multivariate, regression and functional data.")
    (license license:gpl2+)))

(define-public r-mrfcov
  (package
    (name "r-mrfcov")
    (version "1.0.39")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRFcov" version))
       (sha256
        (base32 "0vgmm9lwkpfahzlhcyaixh2x9d85rrpxipzsc0wy66j43vgn4jhp"))))
    (properties `((upstream-name . "MRFcov")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfsmisc
                             r-reshape2
                             r-purrr
                             r-plyr
                             r-pbapply
                             r-mgcv
                             r-matrix
                             r-mass
                             r-magrittr
                             r-igraph
                             r-gridextra
                             r-glmnet
                             r-ggplot2
                             r-dplyr
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nicholasjclark/MRFcov")
    (synopsis "Markov Random Fields with Additional Covariates")
    (description
     "Approximate node interaction parameters of Markov Random Fields graphical
networks.  Models can incorporate additional covariates, allowing users to
estimate how interactions between nodes in the graph are predicted to change
across covariate gradients.  The general methods implemented in this package are
described in Clark et al. (2018) <doi:10.1002/ecy.2221>.")
    (license license:gpl3)))

(define-public r-mrfa
  (package
    (name "r-mrfa")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRFA" version))
       (sha256
        (base32 "0j4ij0w0crwcbxhgsnr3pi52lpzlz9s23df5k05w8580p412s3a3"))))
    (properties `((upstream-name . "MRFA")))
    (build-system r-build-system)
    (propagated-inputs (list r-randtoolbox
                             r-plyr
                             r-grplasso
                             r-glmnet
                             r-foreach
                             r-fields))
    (home-page "https://cran.r-project.org/package=MRFA")
    (synopsis
     "Fitting and Predicting Large-Scale Nonlinear Regression Problems using Multi-Resolution Functional ANOVA (MRFA) Approach")
    (description
     "This package performs the MRFA approach proposed by Sung et al. (2020)
<doi:10.1080/01621459.2019.1595630> to fit and predict nonlinear regression
problems, particularly for large-scale and high-dimensional problems.  The
application includes deterministic or stochastic computer experiments, spatial
datasets, and so on.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mrf2d
  (package
    (name "r-mrf2d")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrf2d" version))
       (sha256
        (base32 "0fmxn178bkw77iz51bnjxala9xmhs44km57m8msvlw0k69sddyif"))))
    (properties `((upstream-name . "mrf2d")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Freguglia/mrf2d")
    (synopsis "Markov Random Field Models for Image Analysis")
    (description
     "Model fitting, sampling and visualization for the (Hidden) Markov Random Field
model with pairwise interactions and general interaction structure from
Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>, which has many popular
models used in 2-dimensional lattices as particular cases, like the Ising Model
and Potts Model.  A complete manuscript describing the package is available in
Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.")
    (license license:gpl3)))

(define-public r-mrf
  (package
    (name "r-mrf")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrf" version))
       (sha256
        (base32 "0jqpx0ng0l8f7y941awpi82yqbv8ir0sixiw6zdkqpj82d9a7j9m"))))
    (properties `((upstream-name . "mrf")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnfor r-monmlp r-limsolve r-forecast r-deoptim))
    (native-inputs (list r-knitr))
    (home-page "https://www.deepbionics.org")
    (synopsis "Multiresolution Forecasting")
    (description
     "Forecasting of univariate time series using feature extraction with variable
prediction methods is provided.  Feature extraction is done with a redundant
Haar wavelet transform with filter h = (0.5, 0.5).  The advantage of the
approach compared to typical Fourier based methods is an dynamic adaptation to
varying seasonalities.  Currently implemented prediction methods based on the
selected wavelets levels and scales are a regression and a multi-layer
perceptron.  Forecasts can be computed for horizon 1 or higher.  Model selection
is performed with an evolutionary optimization.  Selection criteria are
currently the AIC criterion, the Mean Absolute Error or the Mean Root Error.
The data is split into three parts for model selection: Training, test, and
evaluation dataset.  The training data is for computing the weights of a
parameter set.  The test data is for choosing the best parameter set.  The
evaluation data is for assessing the forecast performance of the best parameter
set on new data unknown to the model.  This work is published in Stier, Q.;
Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial
Applications.  Processes 2021, 9, 1697. <doi:10.3390/pr9101697>.")
    (license license:gpl3)))

(define-public r-mregions2
  (package
    (name "r-mregions2")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mregions2" version))
       (sha256
        (base32 "1hhl3y8x777yfnn5l92nv6cc4kfk1alg64anzaz589g8802al7aw"))))
    (properties `((upstream-name . "mregions2")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-wrapr
                             r-sf
                             r-rdflib
                             r-memoise
                             r-magrittr
                             r-isocodes
                             r-httr2
                             r-glue
                             r-dplyr
                             r-digest
                             r-curl
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/mregions2")
    (synopsis "Access Data from Marineregions.org: Gazetteer & Data Products")
    (description
     "Explore and retrieve marine geospatial data from the Marine Regions Gazetteer
<https://marineregions.org/gazetteer.php?p=webservices> and the Marine Regions
Data Products <https://marineregions.org/webservices.php>.")
    (license license:expat)))

(define-public r-mreg
  (package
    (name "r-mreg")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mreg" version))
       (sha256
        (base32 "1j958jkibjlvjh0nwbwmqk7imcv3srhnzg28gs9j5p8xhyx12i0f"))))
    (properties `((upstream-name . "mreg")))
    (build-system r-build-system)
    (home-page "https://github.com/shug0131/mreg")
    (synopsis "Fits Regression Models When the Outcome is Partially Missing")
    (description
     "This package implements the methods described in Bond S, Farewell V, 2006, Exact
Likelihood Estimation for a Negative Binomial Regression Model with Missing
Outcomes, Biometrics.")
    (license license:gpl3+)))

(define-public r-mrds
  (package
    (name "r-mrds")
    (version "2.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrds" version))
       (sha256
        (base32 "1arp1nk393wbbvasvsp3mb08lbvl3y38bw06sxyk148za3lnhkhq"))))
    (properties `((upstream-name . "mrds")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-optimx r-numderiv r-mgcv))
    (home-page "https://github.com/DistanceDevelopment/mrds/")
    (synopsis "Mark-Recapture Distance Sampling")
    (description
     "Animal abundance estimation via conventional, multiple covariate and
mark-recapture distance sampling (CDS/MCDS/MRDS).  Detection function fitting is
performed via maximum likelihood.  Also included are diagnostics and plotting
for fitted detection functions.  Abundance estimation is via a
Horvitz-Thompson-like estimator.")
    (license license:gpl2+)))

(define-public r-mrct
  (package
    (name "r-mrct")
    (version "0.0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrct" version))
       (sha256
        (base32 "0sgdkkg7x3zly7s35gz7mz79wrwlvnrp8mm7rabqs2lw7lc93lza"))))
    (properties `((upstream-name . "mrct")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase
                             r-reshape2
                             r-rdpack
                             r-ggplot2
                             r-fdapace
                             r-fda))
    (home-page "https://cran.r-project.org/package=mrct")
    (synopsis
     "Outlier Detection of Functional Data Based on the Minimum Regularized Covariance Trace Estimator")
    (description
     "Detect outlying observations in functional data sets based on the minimum
regularized covariance trace (MRCT) estimator.  Includes implementation of
Oguamalam et al. (2023) <@code{arXiv:2307.13509>}.")
    (license license:gpl2+)))

(define-public r-mrce
  (package
    (name "r-mrce")
    (version "2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MRCE" version))
       (sha256
        (base32 "0j5dhzmn1bpplc5jird966gcg0zjwlajv93r2p0kh48msi5kbaqc"))))
    (properties `((upstream-name . "MRCE")))
    (build-system r-build-system)
    (propagated-inputs (list r-glasso))
    (home-page "https://cran.r-project.org/package=MRCE")
    (synopsis "Multivariate Regression with Covariance Estimation")
    (description
     "Compute and select tuning parameters for the MRCE estimator proposed by Rothman,
Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>.  This estimator fits the
multiple output linear regression model with a sparse estimator of the error
precision matrix and a sparse estimator of the regression coefficient matrix.")
    (license license:gpl2)))

(define-public r-mrc
  (package
    (name "r-mrc")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mRc" version))
       (sha256
        (base32 "0v8kyvgliss8lxbzcikd3vhmmq2ysl1fp87r37r5f8w9xsx5ba9z"))))
    (properties `((upstream-name . "mRc")))
    (build-system r-build-system)
    (home-page "https://github.com/k-dettloff/mRc")
    (synopsis "Multi-Visit Closed Population Mark-Recapture Estimates")
    (description
     "Compute bootstrap confidence intervals for the adjusted Schnabel and
Schumacher-Eschmeyer multi-visit mark-recapture estimators based on Dettloff
(2023) <doi:10.1016/j.fishres.2023.106756>.")
    (license license:expat)))

(define-public r-mrbsizer
  (package
    (name "r-mrbsizer")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrbsizeR" version))
       (sha256
        (base32 "150ipzlkfzl3dpl4pkjdnrcxr82x9pb2rzhmgl3f02xd35x6am5z"))))
    (properties `((upstream-name . "mrbsizeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-maps r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/romanflury/mrbsizeR")
    (synopsis "Scale Space Multiresolution Analysis of Random Signals")
    (description
     "This package provides a method for the multiresolution analysis of spatial
fields and images to capture scale-dependent features. @code{mrbsizeR} is based
on scale space smoothing and uses differences of smooths at neighbouring scales
for finding features on different scales.  To infer which of the captured
features are credible, Bayesian analysis is used.  The scale space
multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
(2) Using differences of smooths, scale-dependent features of the reconstructed
signal can be found. (3) Posterior credibility analysis of the differences of
smooths created.  The method has first been proposed by Holmstrom, Pasanen,
Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury,
Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.")
    (license license:gpl2)))

(define-public r-mrbin
  (package
    (name "r-mrbin")
    (version "1.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrbin" version))
       (sha256
        (base32 "1pa6acqry480xf2hng10m2i3wlh9k5rxfxl8zarlpcczg8pb1pv2"))))
    (properties `((upstream-name . "mrbin")))
    (build-system r-build-system)
    (native-inputs (list))
    (home-page "https://github.com/kleinomicslab/mrbin")
    (synopsis "Metabolomics Data Analysis Functions")
    (description
     "This package provides a collection of functions for processing and analyzing
metabolite data.  The namesake function mrbin() converts 1D or 2D Nuclear
Magnetic Resonance data into a matrix of values suitable for further data
analysis and performs basic processing steps in a reproducible way.  Negative
values, a common issue in such data, can be replaced by positive values
(<doi:10.1021/acs.jproteome.0c00684>).  All used parameters are stored in a
readable text file and can be restored from that file to enable exact
reproduction of the data at a later time.  The function fia() ranks features
according to their impact on classifier models, especially artificial neural
network models.")
    (license license:gpl3)))

(define-public r-mrbayes
  (package
    (name "r-mrbayes")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mrbayes" version))
       (sha256
        (base32 "0jvfqfyds3g8dfh946l08vkgx3kqwjkp5vph20mhi20qqqiw21di"))))
    (properties `((upstream-name . "mrbayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-desctools
                             r-bh))
    (home-page "https://github.com/okezie94/mrbayes")
    (synopsis
     "Bayesian Summary Data Models for Mendelian Randomization Studies")
    (description
     "Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013)
<doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015)
<doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization
analyses.")
    (license license:gpl3)))

(define-public r-mratios
  (package
    (name "r-mratios")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mratios" version))
       (sha256
        (base32 "18pi0dks0qmw0gv76yqm4gvxdqrcvr25j1vb8k4mkp5p35kzkzff"))))
    (properties `((upstream-name . "mratios")))
    (build-system r-build-system)
    (propagated-inputs (list r-survpresmooth r-survival r-mvtnorm r-multcomp))
    (home-page "https://cran.r-project.org/package=mratios")
    (synopsis "Ratios of Coefficients in the General Linear Model")
    (description
     "This package performs (simultaneous) inferences for ratios of linear
combinations of coefficients in the general linear model, linear mixed model,
and for quantiles in a one-way layout.  Multiple comparisons and simultaneous
confidence interval estimations can be performed for ratios of treatment means
in the normal one-way layout with homogeneous and heterogeneous treatment
variances, according to Dilba et al. (2007)
<https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn
(2008) <doi:10.1002/bimj.200710466>.  Confidence interval estimations for ratios
of linear combinations of linear model parameters like in (multiple) slope ratio
and parallel line assays can be carried out.  Moreover, it is possible to
calculate the sample sizes required in comparisons with a control based on
relative margins.  For the simple two-sample problem, functions for a t-test for
ratio-formatted hypotheses and the corresponding confidence interval are
provided assuming homogeneous or heterogeneous group variances.")
    (license license:gpl2)))

(define-public r-mr-rgm
  (package
    (name "r-mr-rgm")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MR.RGM" version))
       (sha256
        (base32 "116jbzjzkf2d366r4k3j4wjk5i8k4x57k3b4nsvlb6s7yhwwq9a4"))))
    (properties `((upstream-name . "MR.RGM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/bitansa/MR.RGM")
    (synopsis "Multivariate Bidirectional Mendelian Randomization Networks")
    (description
     "Addressing a central challenge encountered in Mendelian randomization (MR)
studies, where MR primarily focuses on discerning the effects of individual
exposures on specific outcomes and establishes causal links between them.  Using
a network-based methodology, the intricacy involving interdependent outcomes due
to numerous factors has been tackled through this routine.  Based on Ni et al.
(2018) <doi:10.1214/17-BA1087>, MR.RGM extends to a broader exploration of the
causal landscape by leveraging on network structures and involves the
construction of causal graphs that capture interactions between response
variables and consequently between responses and instrument variables.  MR.RGM
facilitates the navigation of various data availability scenarios effectively by
accommodating three input formats, i.e., individual-level data and two types of
summary-level data.  In the process, causal effects, adjacency matrices, and
other essential parameters of the complex biological networks, are estimated.
Besides, MR.RGM provides uncertainty quantification for specific network
structures among response variables.")
    (license license:gpl3+)))

(define-public r-mr-raps
  (package
    (name "r-mr-raps")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mr.raps" version))
       (sha256
        (base32 "069vyvsdgc5m7n2fd0h7jrllz789zvsxka7h5wiy36dc7lagd6f8"))))
    (properties `((upstream-name . "mr.raps")))
    (build-system r-build-system)
    (propagated-inputs (list r-nortest))
    (home-page "https://cran.r-project.org/package=mr.raps")
    (synopsis
     "Two Sample Mendelian Randomization using Robust Adjusted Profile Score")
    (description
     "Mendelian randomization is a method of identifying and estimating a confounded
causal effect using genetic instrumental variables.  This packages implements
methods for two-sample Mendelian randomization with summary statistics by using
Robust Adjusted Profile Score (RAPS).  References: Qingyuan Zhao, Jingshu Wang,
Jack Bowden, Dylan S. Small.  Statistical inference in two-sample summary-data
Mendelian randomization using robust adjusted profile score.
<@code{arXiv:1801.09652>}.")
    (license license:gpl3)))

(define-public r-mr-pivw
  (package
    (name "r-mr-pivw")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mr.pivw" version))
       (sha256
        (base32 "1y3kfbwvy5a01527d18ca4p10nm8x4rm8zgr7h71irbjl3jlj70v"))))
    (properties `((upstream-name . "mr.pivw")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mr.pivw")
    (synopsis
     "Penalized Inverse-Variance Weighted Estimator for Mendelian Randomization")
    (description
     "The penalized inverse-variance weighted (@code{pIVW}) estimator is a Mendelian
randomization method for estimating the causal effect of an exposure variable on
an outcome of interest based on summary-level GWAS data.  The @code{pIVW}
estimator accounts for weak instruments and balanced horizontal pleiotropy
simultaneously.  See Xu S., Wang P., Fung W.K. and Liu Z. (2022)
<doi:10.1111/biom.13732>.")
    (license license:gpl2)))

(define-public r-mqrcm
  (package
    (name "r-mqrcm")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mqrcm" version))
       (sha256
        (base32 "14fdwjqvw9kq4ghgj7wzis5ipvnlbh1z2m14vrjrzjkz7q9pkwgw"))))
    (properties `((upstream-name . "Mqrcm")))
    (build-system r-build-system)
    (propagated-inputs (list r-pch r-hmisc))
    (home-page "https://cran.r-project.org/package=Mqrcm")
    (synopsis "M-Quantile Regression Coefficients Modeling")
    (description
     "Parametric modeling of M-quantile regression coefficient functions.")
    (license license:gpl2)))

(define-public r-mqmf
  (package
    (name "r-mqmf")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MQMF" version))
       (sha256
        (base32 "1qw5dswq9p4k5rj8dxzr8f1j3mw232jgn18idbhb4inigha3sa5c"))))
    (properties `((upstream-name . "MQMF")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass))
    (home-page "https://github.com/haddonm/MQMF/")
    (synopsis "Modelling and Quantitative Methods in Fisheries")
    (description
     "Complements the book \"Using R for Modelling and Quantitative Methods in
Fisheries\" ISBN 9780367469894, published in 2021 by Chapman & Hall in their
\"Using R series\".  There are numerous functions and data-sets that are used in
the book's many practical examples.")
    (license license:gpl3)))

(define-public r-mpwr
  (package
    (name "r-mpwr")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpwR" version))
       (sha256
        (base32 "16j81a4minl8710fl32k05g4xcpii7r3jqzypkmvxyhrb1mdvc1q"))))
    (properties `((upstream-name . "mpwR")))
    (build-system r-build-system)
    (propagated-inputs (list r-upsetr
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-plotly
                             r-magrittr
                             r-ggplot2
                             r-forcats
                             r-flowtracer
                             r-dplyr
                             r-data-table
                             r-comprehenr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mpwR")
    (synopsis
     "Standardized Comparison of Workflows in Mass Spectrometry-Based Bottom-Up Proteomics")
    (description
     "Useful functions to analyze proteomic workflows including number of
identifications, data completeness, missed cleavages, quantitative and retention
time precision etc.  Various software outputs are supported such as
@code{ProteomeDiscoverer}', Spectronaut', DIA-NN and @code{MaxQuant}'.")
    (license license:expat)))

(define-public r-mpv
  (package
    (name "r-mpv")
    (version "1.64")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPV" version))
       (sha256
        (base32 "0alvs1jm1qqnswhm62a0ky070dskk6syjsa5bkcssg1zin8xcyhv"))))
    (properties `((upstream-name . "MPV")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-lattice r-kernsmooth))
    (home-page "https://cran.r-project.org/package=MPV")
    (synopsis "Data Sets from Montgomery, Peck and Vining")
    (description
     "Most of this package consists of data sets from the textbook Introduction to
Linear Regression Analysis, by Montgomery, Peck and Vining.  All data sets from
the 3rd edition are included and many from the 6th edition are also included.
The package also contains some additional data sets and functions.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-mptmultiverse
  (package
    (name "r-mptmultiverse")
    (version "0.4-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPTmultiverse" version))
       (sha256
        (base32 "0fjfj8jk0dk1s3jc9rl9kza6gm5pm6dgsqyk0aq7pir2hrsr3qwy"))))
    (properties `((upstream-name . "MPTmultiverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-treebugs
                             r-tidyr
                             r-tibble
                             r-runjags
                             r-rlang
                             r-reshape2
                             r-readr
                             r-purrr
                             r-mptinr
                             r-magrittr
                             r-limsolve
                             r-ggplot2
                             r-dplyr
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mpt-network/MPTmultiverse")
    (synopsis "Multiverse Analysis of Multinomial Processing Tree Models")
    (description
     "Statistical or cognitive modeling usually requires a number of more or less
arbitrary choices creating one specific path through a garden of forking paths'.
 The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016,
<doi:10.1177/1745691616658637>) offers a principled alternative in which results
for all possible combinations of reasonable modeling choices are reported.
MPTmultiverse performs a multiverse analysis for multinomial processing tree
(MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models
combining maximum-likelihood/frequentist and Bayesian estimation approaches with
different levels of pooling (i.e., data aggregation).  For the frequentist
approaches, no pooling (with and without parametric or nonparametric bootstrap)
and complete pooling are implemented using MP@code{TinR}
<https://cran.r-project.org/package=MP@code{TinR>}.  For the Bayesian
approaches, no pooling, complete pooling, and three different variants of
partial pooling are implemented using @code{TreeBUGS}
<https://cran.r-project.org/package=@code{TreeBUGS>}.  The main function is
fit_mpt() who performs the multiverse analysis in one call.")
    (license license:gpl2)))

(define-public r-mptinr
  (package
    (name "r-mptinr")
    (version "1.14.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPTinR" version))
       (sha256
        (base32 "0jywv5kim3vdfq0dfaxjksq8l2i6ny45rn5ibmb7ximwfjm3syp0"))))
    (properties `((upstream-name . "MPTinR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-numderiv r-brobdingnag))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MPTinR")
    (synopsis "Analyze Multinomial Processing Tree Models")
    (description
     "This package provides a user-friendly way for the analysis of multinomial
processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988].
 Multinomial modeling and the measurement of cognitive processes.  Psychological
Review, 95, 318-339) for single and multiple datasets.  The main functions
perform model fitting and model selection.  Model selection can be done using
AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the
Minimum Description Length (MDL) framework.  The model and restrictions can be
specified in external files or within an R script in an intuitive syntax or
using the context-free language for MPTs.  The classical .EQN file format for
model files is also supported.  Besides MPTs, this package can fit a wide
variety of other cognitive models such as SDT models (see fit.model).  It also
supports multicore fitting and FIA calculation (using the snowfall package), can
generate or bootstrap data for simulations, and plot predicted versus observed
data.")
    (license license:gpl2+)))

(define-public r-mpt
  (package
    (name "r-mpt")
    (version "1.0-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpt" version))
       (sha256
        (base32 "1gv3gfak0mdng15md78fg57dfylskvdrhr6aycw0hyss40gpglvq"))))
    (properties `((upstream-name . "mpt")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://www.mathpsy.uni-tuebingen.de/wickelmaier/")
    (synopsis "Multinomial Processing Tree Models")
    (description
     "Fitting and testing multinomial processing tree (MPT) models, a class of
nonlinear models for categorical data.  The parameters are the link
probabilities of a tree-like graph and represent the latent cognitive processing
steps executed to arrive at observable response categories (Batchelder & Riefer,
1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009
<doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988
<doi:10.1037/0033-295x.95.3.318>).")
    (license license:gpl2+)))

(define-public r-mpsychor
  (package
    (name "r-mpsychor")
    (version "0.10-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPsychoR" version))
       (sha256
        (base32 "19gp2pjy8bllk4pbx139vcydnw04nldfqq9q7291vr5krylfjcqv"))))
    (properties `((upstream-name . "MPsychoR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MPsychoR")
    (synopsis "Modern Psychometrics with R")
    (description
     "Supplementary materials and datasets for the book \"Modern Psychometrics With R\"
(Mair, 2018, Springer @code{useR}! series).")
    (license license:gpl2)))

(define-public r-mpspline2
  (package
    (name "r-mpspline2")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpspline2" version))
       (sha256
        (base32 "1zh53lxsy895lljxlyhas5xb9ln5q772h26yil7ydq6zlh4w8vr0"))))
    (properties `((upstream-name . "mpspline2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mpspline2")
    (synopsis "Mass-Preserving Spline Functions for Soil Data")
    (description
     "This package provides a low-dependency implementation of GSIF::mpspline()
<https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>,
which applies a mass-preserving spline to soil attributes.  Splining soil data
is a safe way to make continuous down-profile estimates of attributes measured
over discrete, often discontinuous depth intervals.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mpsem
  (package
    (name "r-mpsem")
    (version "0.4-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPSEM" version))
       (sha256
        (base32 "0g3rv4gizsdjhiip6inv0pcmbjd9px8c4wn6vsz77ya05g4427an"))))
    (properties `((upstream-name . "MPSEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MPSEM")
    (synopsis "Modelling Phylogenetic Signals using Eigenvector Maps")
    (description
     "Computational tools to represent phylogenetic signals using adapted eigenvector
maps.")
    (license license:gpl3)))

(define-public r-mps
  (package
    (name "r-mps")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPS" version))
       (sha256
        (base32 "08yzynznf33y7y3pf7fayks5hi3cdvzwzn8axg7aky5kvi2bfv22"))))
    (properties `((upstream-name . "MPS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MPS")
    (synopsis "Estimating Through the Maximum Product Spacing Approach")
    (description
     "Developed for computing the probability density function, computing the
cumulative distribution function, computing the quantile function, random
generation, drawing q-q plot, and estimating the parameters of 24 G-family of
statistical distributions via the maximum product spacing approach introduced in
<https://www.jstor.org/stable/2345411>.  The set of families contains: beta G
distribution, beta exponential G distribution, beta extended G distribution,
exponentiated G distribution, exponentiated exponential Poisson G distribution,
exponentiated generalized G distribution, exponentiated Kumaraswamy G
distribution, gamma type I G distribution, gamma type II G distribution, gamma
uniform G distribution, gamma-X generated of log-logistic family of G
distribution, gamma-X family of modified beta exponential G distribution,
geometric exponential Poisson G distribution, generalized beta G distribution,
generalized transmuted G distribution, Kumaraswamy G distribution, log gamma
type I G distribution, log gamma type II G distribution, Marshall Olkin G
distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G
distribution, odd log-logistic G distribution, truncated-exponential
skew-symmetric G distribution, and Weibull G distribution.")
    (license license:gpl2+)))

(define-public r-mpr
  (package
    (name "r-mpr")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpr" version))
       (sha256
        (base32 "155cjdcyvxnmqxx3zg2dvzk7av2jcn67lj2vdpd2ahbznax76ghk"))))
    (properties `((upstream-name . "mpr")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=mpr")
    (synopsis "Multi-Parameter Regression (MPR)")
    (description
     "Fitting Multi-Parameter Regression (MPR) models to right-censored survival data.
 These are flexible parametric regression models which extend standard models,
for example, proportional hazards.  See Burke & @code{MacKenzie} (2016)
<doi:10.1111/biom.12625> and Burke et al (2020) <doi:10.1111/rssc.12398>.")
    (license license:gpl3)))

(define-public r-mppr
  (package
    (name "r-mppr")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mppR" version))
       (sha256
        (base32 "0ghpsckphrhnjdsxjvbq9v5c3mdpkq14wp9m7g7w9gf5izpa989s"))))
    (properties `((upstream-name . "mppR")))
    (build-system r-build-system)
    (propagated-inputs (list r-qtl
                             r-nlme
                             r-matrix
                             r-igraph
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/vincentgarin/mppR")
    (synopsis "Multi-Parent Population QTL Analysis")
    (description
     "Analysis of experimental multi-parent populations to detect regions of the
genome (called quantitative trait loci, QTLs) influencing phenotypic traits
measured in unique and multiple environments.  The population must be composed
of crosses between a set of at least three parents (e.g. factorial design,
diallel', or nested association mapping).  The functions cover data processing,
QTL detection, and results visualization.  The implemented methodology is
described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017)
<doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti and van Eeuwijk (2020)
<doi: 10.1007/s00122-020-03621-0>, and in Garin, Diallo, Tekete, Thera, ..., and
Rami (2024) <doi: 10.1093/genetics/iyae003>.")
    (license license:gpl3)))

(define-public r-mpower
  (package
    (name "r-mpower")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpower" version))
       (sha256
        (base32 "0bg2q6dgnh17v5rwa50qj6yivn902p34nmpkbvqhn7qsf45vadmf"))))
    (properties `((upstream-name . "mpower")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-snow
                             r-sbgcop
                             r-rlang
                             r-reshape2
                             r-purrr
                             r-mass
                             r-magrittr
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-dosnow
                             r-boot
                             r-abind))
    (home-page "https://cran.r-project.org/package=mpower")
    (synopsis "Power Analysis via Monte Carlo Simulation for Correlated Data")
    (description
     "This package provides a flexible framework for power analysis using Monte Carlo
simulation for settings in which considerations of the correlations between
predictors are important.  Users can set up a data generative model that
preserves dependence structures among predictors given existing data
(continuous, binary, or ordinal).  Users can also generate power curves to
assess the trade-offs between sample size, effect size, and power of a design.
This package includes several statistical models common in environmental
mixtures studies.  For more details and tutorials, see Nguyen et al. (2022)
<@code{arXiv:2209.08036>}.")
    (license license:lgpl2.0+)))

(define-public r-mpoly
  (package
    (name "r-mpoly")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpoly" version))
       (sha256
        (base32 "10is69ks22ka1cqw3xk0fvy4z3b2drm8lanqwj178qrzj7ipf7i0"))))
    (properties `((upstream-name . "mpoly")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-stringi
                             r-polynom
                             r-plyr
                             r-partitions
                             r-orthopolynom
                             r-ggplot2))
    (home-page "https://github.com/dkahle/mpoly")
    (synopsis "Symbolic Computation and More with Multivariate Polynomials")
    (description "Symbolic computing with multivariate polynomials in R.")
    (license license:gpl2)))

(define-public r-mpn
  (package
    (name "r-mpn")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPN" version))
       (sha256
        (base32 "0rrigkq5bkarncswv8kgvvwd1vrq5cqnb5vypa9d39q2ksqf8j3d"))))
    (properties `((upstream-name . "MPN")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://mpncalc.galaxytrakr.org/")
    (synopsis
     "Most Probable Number and Other Microbial Enumeration Techniques")
    (description
     "Calculates the Most Probable Number (MPN) to quantify the concentration
(density) of microbes in serial dilutions of a laboratory sample (described in
Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>).  Also calculates the
Aerobic Plate Count (APC) for similar microbial enumeration experiments.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-mpmsim
  (package
    (name "r-mpmsim")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpmsim" version))
       (sha256
        (base32 "0qvjicciqd3zgkz6s9pnp6z98g0irwz0bbmj0pvvy8bhjcs7yln4"))))
    (properties `((upstream-name . "mpmsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape
                             r-rcompadre
                             r-rage
                             r-popdemo
                             r-popbio
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jonesor/mpmsim")
    (synopsis
     "Simulation of Matrix Population Models with Defined Life History Characteristics")
    (description
     "Allows users to simulate matrix population models with particular
characteristics based on aspects of life history such as mortality trajectories
and fertility trajectories.  Also allows the exploration of sampling error due
to small sample size.")
    (license license:cc-by-sa4.0)))

(define-public r-mpmi
  (package
    (name "r-mpmi")
    (version "0.43.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpmi" version))
       (sha256
        (base32 "12chkhaqy14ql9gn33dwvq8fjd1rrbhlcpn0w0dgxscij3h3la5n"))))
    (properties `((upstream-name . "mpmi")))
    (build-system r-build-system)
    (propagated-inputs (list r-kernsmooth))
    (native-inputs (list gfortran))
    (home-page "https://r-forge.r-project.org/projects/mpmi/")
    (synopsis "Mixed-Pair Mutual Information Estimators")
    (description
     "Uses a kernel smoothing approach to calculate Mutual Information for comparisons
between all types of variables including continuous vs continuous, continuous vs
discrete and discrete vs discrete.  Uses a nonparametric bias correction giving
Bias Corrected Mutual Information (BCMI).  Implemented efficiently in Fortran 95
with @code{OpenMP} and suited to large genomic datasets.")
    (license license:gpl3)))

(define-public r-mpmcorrelogram
  (package
    (name "r-mpmcorrelogram")
    (version "0.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpmcorrelogram" version))
       (sha256
        (base32 "0gv9xswrfvndcjal0csab1w2cnl4rg8pznyxbz84b0qr95jg81ag"))))
    (properties `((upstream-name . "mpmcorrelogram")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan))
    (home-page "https://cran.r-project.org/package=mpmcorrelogram")
    (synopsis "Multivariate Partial Mantel Correlogram")
    (description
     "This package provides functions to compute and plot multivariate (partial)
Mantel correlograms.")
    (license license:gpl2+)))

(define-public r-mplustrees
  (package
    (name "r-mplustrees")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MplusTrees" version))
       (sha256
        (base32 "0jbh3ixamj2ra1jfrzdfrxg7q3w2h82ymwxbsl8ac59bh06hzk46"))))
    (properties `((upstream-name . "MplusTrees")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-rpart-plot r-rpart r-nlme r-mplusautomation))
    (home-page "https://cran.r-project.org/package=MplusTrees")
    (synopsis "Decision Trees with Structural Equation Models Fit in 'Mplus'")
    (description
     "Uses recursive partitioning to create homogeneous subgroups based on structural
equation models fit in Mplus', a stand-alone program developed by Muthen and
Muthen.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mplusparallel-automation
  (package
    (name "r-mplusparallel-automation")
    (version "0.0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mplusParallel.automation" version))
       (sha256
        (base32 "012zbd09vqln1xb8lj6akiv1nyhiayiwiwbk8q70yv1mfpj5jgpm"))))
    (properties `((upstream-name . "mplusParallel.automation")))
    (build-system r-build-system)
    (propagated-inputs (list r-mplusautomation r-future r-furrr r-dplyr))
    (home-page "https://cran.r-project.org/package=mplusParallel.automation")
    (synopsis "Parallel Processing Automation for 'Mplus'")
    (description
     "Offers automation tools to parallelize Mplus operations when using R for data
generation.  It facilitates streamlined integration between Mplus and R',
allowing users to run and manage multiple Mplus models simultaneously and
efficiently in R'.")
    (license license:gpl3)))

(define-public r-mplusautomation
  (package
    (name "r-mplusautomation")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MplusAutomation" version))
       (sha256
        (base32 "0a95m9m7m988ky4sswqyzmyh0fhbbfz1kzsf9mnaswm5xh1gwh4k"))))
    (properties `((upstream-name . "MplusAutomation")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-texreg
                             r-plyr
                             r-pander
                             r-lattice
                             r-gsubfn
                             r-ggplot2
                             r-fastdummies
                             r-digest
                             r-data-table
                             r-coda
                             r-checkmate
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://michaelhallquist.github.io/MplusAutomation/")
    (synopsis
     "An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus")
    (description
     "Leverages the R language to automate latent variable model estimation and
interpretation using Mplus', a powerful latent variable modeling program
developed by Muthen and Muthen (<https://www.statmodel.com>).  Specifically,
this package provides routines for creating related groups of models, running
batches of models, and extracting and tabulating model parameters and fit
statistics.")
    (license license:lgpl3)))

(define-public r-mplot
  (package
    (name "r-mplot")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mplot" version))
       (sha256
        (base32 "15cmwk4lphxy5p3azy9lwhij76127h9g4g1j1sv59mbqvrqid0sk"))))
    (properties `((upstream-name . "mplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-shinydashboard
                             r-shiny
                             r-scales
                             r-reshape2
                             r-plyr
                             r-magrittr
                             r-leaps
                             r-googlevis
                             r-glmnet
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-dorng
                             r-doparallel
                             r-bestglm))
    (home-page "https://garthtarr.github.io/mplot/")
    (synopsis "Graphical Model Stability and Variable Selection Procedures")
    (description
     "Model stability and variable inclusion plots [Mueller and Welsh (2010,
<doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller (2013,
<doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al. (2008,
<doi:10.1214/07-AOS517>); Jiang et al. (2009, <doi:10.1016/j.spl.2008.10.014>)]
for linear and generalised linear models.")
    (license license:gpl2+)))

(define-public r-mpitbr
  (package
    (name "r-mpitbr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpitbR" version))
       (sha256
        (base32 "01p82d0dzjlq3b0zyg25i7fh6l0amj55kcavcysqsjy2i0352fvb"))))
    (properties `((upstream-name . "mpitbR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-purrr))
    (home-page "https://github.com/girelaignacio/mpitbR")
    (synopsis "Calculate Alkire-Foster Multidimensional Poverty Measures")
    (description
     "Estimate Multidimensional Poverty Indices disaggregated by population subgroups
based on the Alkire and Foster method (2011)
<doi:10.1016/j.jpubeco.2010.11.006>.  This includes the calculation of standard
errors and confidence intervals.  Other partial indices such as incidence,
intensity and indicator-specific measures as well as intertemporal changes
analysis can also be estimated.  The standard errors and confidence intervals
are calculated considering the complex survey design.")
    (license license:gpl3+)))

(define-public r-mpindex
  (package
    (name "r-mpindex")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpindex" version))
       (sha256
        (base32 "1csza6krcjclp98b9wj424vkmq5jfg2pglqrsp72pwagpas4dsg7"))))
    (properties `((upstream-name . "mpindex")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-openxlsx
                             r-jsonlite
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yng-me/mpindex")
    (synopsis "Multidimensional Poverty Index (MPI)")
    (description
     "This package provides a set of easy-to-use functions for computing the
Multidimensional Poverty Index (MPI).")
    (license license:expat)))

(define-public r-mpi
  (package
    (name "r-mpi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPI" version))
       (sha256
        (base32 "081yhbk3qz5935yz5n1391x3hpj8m0cbj1p81snhb9n8h0agwgv0"))))
    (properties `((upstream-name . "MPI")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-purrr r-foreach r-dplyr r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/9POINTEIGHT/MPI")
    (synopsis "Computation of Multidimensional Poverty Index (MPI)")
    (description
     "Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster
method.  Given N individuals, each person has D indicators of deprivation, the
package compute MPI value to represent the degree of poverty in a population.
The inputs are 1) an N by D matrix, which has the element (i,j) represents
whether an individual i is deprived in an indicator j (1 is deprived and 0 is
not deprived), and 2) the deprivation threshold.  The main output is the MPI
value, which has the range between zero and one.  MPI value is approaching one
if almost all people are deprived in all indicators, and it is approaching zero
if almost no people are deprived in any indicator.  Please see Alkire S.,
Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014)
<doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.")
    (license license:expat)))

(define-public r-mpge
  (package
    (name "r-mpge")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPGE" version))
       (sha256
        (base32 "10lzkk8fhpxd1z4yp051q3k36fsvddrwssv7nn03r2bkp6q8rpzb"))))
    (properties `((upstream-name . "MPGE")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ArunabhaCodes/MPGE")
    (synopsis
     "Two-Step Approach to Testing Overall Effect of Gene-Environment Interaction for Multiple Phenotypes")
    (description
     "Interaction between a genetic variant (e.g., a single nucleotide polymorphism)
and an environmental variable (e.g., physical activity) can have a shared effect
on multiple phenotypes (e.g., blood lipids).  We implement a two-step method to
test for an overall interaction effect on multiple phenotypes.  In first step,
the method tests for an overall marginal genetic association between the genetic
variant and the multivariate phenotype.  The genetic variants which show an
evidence of marginal overall genetic effect in the first step are prioritized
while testing for an overall gene-environment interaction effect in the second
step.  Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B
Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.")
    (license license:gpl3)))

(define-public r-mpdir
  (package
    (name "r-mpdir")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPDiR" version))
       (sha256
        (base32 "0n7zrcxqfvd0y4qiqsx0qf5rgqdrci5026wkdpffngamxlm80f86"))))
    (properties `((upstream-name . "MPDiR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MPDiR")
    (synopsis "Data Sets and Scripts for Modeling Psychophysical Data in R")
    (description
     "Data sets and scripts for Modeling Psychophysical Data in R (Springer).")
    (license license:gpl2)))

(define-public r-mpcr
  (package
    (name "r-mpcr")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPCR" version))
       (sha256
        (base32 "157614jz041imn2clbrq9wf6chhcgg4gvx37xm5kcy5rv2abjkqh"))))
    (properties `((upstream-name . "MPCR")))
    (build-system r-build-system)
    (inputs (list git cmake))
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/stsds/MPCR")
    (synopsis "Multi- And Mixed-Precision Computations")
    (description
     "Designed for multi- and mixed-precision computations, accommodating 64-bit and
32-bit data structures.  This flexibility enables fast execution across various
applications.  The package enhances performance by optimizing operations in both
precision levels, which is achieved by integrating with high-speed BLAS and
LAPACK libraries like MKL and @code{OpenBLAS}'.  Including a 32-bit option
caters to applications where high precision is unnecessary, accelerating
computational processes whenever feasible.  The package also provides support
for tile-based algorithms in three linear algebra operations: CHOL(), TRSM(),
and GEMM().  The tile-based algorithm splits the matrix into smaller tiles,
facilitating parallelization through a predefined Directed Acyclic Graph (DAG)
for each operation.  Enabling @code{OpenMP} enhances the efficiency of these
operations, leveraging multi-core parallelism.  In this case, MPCR facilitates
mixed-precision execution by permitting varying precision levels for different
tiles.  This approach is advantageous in numerous applications, as it maintains
the accuracy of the application while accelerating execution in scenarios where
single-precision alone does not significantly affect the accuracy of the
application.")
    (license license:gpl3+)))

(define-public r-mpci
  (package
    (name "r-mpci")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPCI" version))
       (sha256
        (base32 "1l55q09lliv0y4q1hc0jgzls47wkmsfag6b4iq5y6wrllr5wq7sa"))))
    (properties `((upstream-name . "MPCI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MPCI")
    (synopsis "Multivariate Process Capability Indices (MPCI)")
    (description
     "It performs the followings Multivariate Process Capability Indices: Shahriari et
al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate
Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings
based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and
Perakis (2002) and Wang (2005).  Two datasets are included.")
    (license license:gpl2)))

(define-public r-mpboost
  (package
    (name "r-mpboost")
    (version "0.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MPBoost" version))
       (sha256
        (base32 "0fi8hx271893dak7whlcxyafqslnqj9kih3jk801s8lwg8g5zvp1"))))
    (properties `((upstream-name . "MPBoost")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MPBoost")
    (synopsis
     "Treatment Allocation in Clinical Trials by the Maximal Procedure")
    (description
     "This package performs treatment allocation in two-arm clinical trials by the
maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>.  To
that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014>
is implemented.")
    (license license:gpl2+)))

(define-public r-mpathsenser
  (package
    (name "r-mpathsenser")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpathsenser" version))
       (sha256
        (base32 "12hhcw76gn7mfd3xjsb7an7bwz95any8l89pglrk0bmj8iwhsns2"))))
    (properties `((upstream-name . "mpathsenser")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rsqlite
                             r-rlang
                             r-purrr
                             r-lubridate
                             r-lifecycle
                             r-jsonlite
                             r-furrr
                             r-dplyr
                             r-dbplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page
     "https://gitlab.kuleuven.be/ppw-okpiv/researchers/u0134047/mpathsenser/")
    (synopsis "Process and Analyse Data from m-Path Sense")
    (description
     "Overcomes one of the major challenges in mobile (passive) sensing, namely being
able to pre-process the raw data that comes from a mobile sensing app,
specifically m-Path Sense <https://m-path.io>.  The main task of mpathsenser is
therefore to read m-Path Sense JSON files into a database and provide several
convenience functions to aid in data processing.")
    (license license:gpl3+)))

(define-public r-mpath
  (package
    (name "r-mpath")
    (version "0.4-2.26")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpath" version))
       (sha256
        (base32 "1pzmcp3kznkfi0bh7pcpgciwla3wgzbb2h1mrngfd1pmm3406pvr"))))
    (properties `((upstream-name . "mpath")))
    (build-system r-build-system)
    (propagated-inputs (list r-weightsvm
                             r-pscl
                             r-numderiv
                             r-mass
                             r-glmnet
                             r-foreach
                             r-doparallel
                             r-bst))
    (native-inputs (list r-r-rsp r-knitr gfortran))
    (home-page "https://github.com/zhuwang46/mpath")
    (synopsis "Regularized Linear Models")
    (description
     "Algorithms compute robust estimators for loss functions in the concave convex
(CC) family by the iteratively reweighted convex optimization (IRCO), an
extension of the iteratively reweighted least squares (IRLS).  The IRCO reduces
the weight of the observation that leads to a large loss; it also provides
weights to help identify outliers.  Applications include robust (penalized)
generalized linear models and robust support vector machines.  The package also
contains penalized Poisson, negative binomial, zero-inflated Poisson,
zero-inflated negative binomial regression models and robust models with
non-convex loss functions.  Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et
al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016)
<doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>,
Wang (2024) <doi:10.1111/anzs.12409>.")
    (license license:gpl2)))

(define-public r-mpae
  (package
    (name "r-mpae")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpae" version))
       (sha256
        (base32 "0a6fbnqvp0fhbni0qivnrcdmbvlzy6biv5qgk9d13ydqaz15vbms"))))
    (properties `((upstream-name . "mpae")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcmdrmisc r-caret))
    (home-page "https://github.com/rubenfcasal/mpae")
    (synopsis
     "Metodos Predictivos de Aprendizaje Estadistico (Statistical Learning Predictive Methods)")
    (description
     "This package provides functions and datasets used in the book: Fernandez-Casal,
R., Costa, J. and Oviedo-de la Fuente, M. (2024) \"Metodos predictivos de
aprendizaje estadistico\"
<https://rubenfcasal.github.io/aprendizaje_estadistico/>.")
    (license license:gpl2+)))

(define-public r-mpactr
  (package
    (name "r-mpactr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mpactr" version))
       (sha256
        (base32 "0hb1d2afjsl9zj6bmjh6hzzc07h0ip1lc8r40w9c9ikl1klm2x23"))))
    (properties `((upstream-name . "mpactr")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-treemapify
                             r-readr
                             r-r6
                             r-ggplot2
                             r-data-table
                             r-cli))
    (home-page "https://mums2.github.io/mpactr/")
    (synopsis "Correction of Preprocessed MS Data")
    (description
     "An R implementation of the python program Metabolomics Peak Analysis
Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J.
Balunas (2023) <doi:10.1021/acs.analchem.2c04632>).  Filters in the package
serve to address common errors in tandem mass spectrometry preprocessing,
including: (1) isotopic patterns that are incorrectly split during
preprocessing, (2) features present in solvent blanks due to carryover between
samples, (3) features whose abundance is greater than user-defined abundance
threshold in a specific group of samples, for example media blanks, (4) ions
that are inconsistent between technical replicates, and (5) in-source fragment
ions created during ionization before fragmentation in the tandem mass
spectrometry workflow.")
    (license license:expat)))

(define-public r-mp
  (package
    (name "r-mp")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mp" version))
       (sha256
        (base32 "0awvwqwb25q47j14b450k1k5mh2yzwhn7gizjv5j7lyiamk30iwq"))))
    (properties `((upstream-name . "mp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=mp")
    (synopsis "Multidimensional Projection Techniques")
    (description
     "Multidimensional projection techniques are used to create two dimensional
representations of multidimensional data sets.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-movmf
  (package
    (name "r-movmf")
    (version "0.2-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "movMF" version))
       (sha256
        (base32 "1rxi0qk5lg4nyhsbmpdkfj9ygnhx8zdkamb6s5hyjgw5z6lm6ni6"))))
    (properties `((upstream-name . "movMF")))
    (build-system r-build-system)
    (propagated-inputs (list r-slam r-skmeans r-clue))
    (home-page "https://cran.r-project.org/package=movMF")
    (synopsis "Mixtures of von Mises-Fisher Distributions")
    (description
     "Fit and simulate mixtures of von Mises-Fisher distributions.")
    (license license:gpl2)))

(define-public r-movieroc
  (package
    (name "r-movieroc")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "movieROC" version))
       (sha256
        (base32 "0znqdqdl7hszg0q9a5sbaj5m9h0lglk33jrxbvfhyw1dfkg29v93"))))
    (properties `((upstream-name . "movieROC")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-rsolnp
                             r-robustbase
                             r-rms
                             r-ks
                             r-intrval
                             r-gtools
                             r-e1071
                             r-animation))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=movieROC")
    (synopsis
     "Visualizing the Decision Rules Underlying Binary Classification")
    (description
     "Visualization of decision rules for binary classification and Receiver Operating
Characteristic (ROC) curve estimation under different generalizations proposed
in the literature: - making the classification subsets flexible to cover those
scenarios where both extremes of the marker are associated with a higher risk of
being positive, considering two thresholds (@code{gROC}() function); -
transforming the marker by a proper function trying to improve the
classification performance (@code{hROC}() function); - when dealing with
multivariate markers, considering a proper transformation to univariate space
trying to maximize the resulting AUC of the TPR for each FPR (@code{multiROC}()
function).  The classification regions behind each point of the ROC curve are
displayed in both static graphics (plot_@code{buildROC}(), plot_regions() or
plot_funregions() function) or videos (@code{movieROC}() function).")
    (license license:gpl3)))

(define-public r-movewindspeed
  (package
    (name "r-movewindspeed")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moveWindSpeed" version))
       (sha256
        (base32 "0gj8qivwgxh9xzyfldb5hqmd8qi2q9xcazdl9iz39fxvwdshravz"))))
    (properties `((upstream-name . "moveWindSpeed")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-move))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/bartk/moveWindSpeed")
    (synopsis "Estimate Wind Speeds from Bird Trajectories")
    (description
     "Estimating wind speed from trajectories of individually tracked birds using a
maximum likelihood approach.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-movementsync
  (package
    (name "r-movementsync")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "movementsync" version))
       (sha256
        (base32 "132m7df76hj96xpbh1k9s1p8j8jy51mv9v6xrlm35kgwxgk912zk"))))
    (properties `((upstream-name . "movementsync")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-waveletcomp
                             r-tidyr
                             r-signal
                             r-scales
                             r-rlang
                             r-osfr
                             r-lmtest
                             r-igraph
                             r-hms
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-circular))
    (home-page "https://cran.r-project.org/package=movementsync")
    (synopsis
     "Analysis and Visualisation of Musical Audio and Video Movement Synchrony Data")
    (description
     "Analysis and visualisation of synchrony, interaction, and joint movements from
audio and video movement data of a group of music performers.  The demo is data
described in Clayton, Leante, and Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>,
while example analyses can be found in Clayton, Jakubowski, and Eerola (2019)
<doi:10.1177/1029864919844809>.  Additionally, wavelet analysis techniques have
been applied to examine movement-related musical interactions, as shown in
Eerola et al. (2018) <doi:10.1098/rsos.171520>.")
    (license license:expat)))

(define-public r-movehmm
  (package
    (name "r-movehmm")
    (version "1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moveHMM" version))
       (sha256
        (base32 "0dq6p13rfv0dryl323zqc0ix12vzn0qaykb9qnxiipwvj0j3qmz2"))))
    (properties `((upstream-name . "moveHMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-mass
                             r-ggplot2
                             r-ggmap
                             r-geosphere
                             r-circstats
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/TheoMichelot/moveHMM")
    (synopsis "Animal Movement Modelling using Hidden Markov Models")
    (description
     "This package provides tools for animal movement modelling using hidden Markov
models.  These include processing of tracking data, fitting hidden Markov models
to movement data, visualization of data and fitted model, decoding of the state
process, etc. <doi:10.1111/2041-210X.12578>.")
    (license license:gpl3)))

(define-public r-movegroup
  (package
    (name "r-movegroup")
    (version "2024.03.05")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "movegroup" version))
       (sha256
        (base32 "0f4488nrkd6zdd1sr8n2zs6mqpff2zrq8wzx0z9g8mbs5z3nw0jf"))))
    (properties `((upstream-name . "movegroup")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tidyselect
                             r-tidyr
                             r-terra
                             r-stringr
                             r-starsextra
                             r-stars
                             r-sp
                             r-sf
                             r-rlang
                             r-raster
                             r-purrr
                             r-move
                             r-magick
                             r-lubridate
                             r-knitr
                             r-ggplot2
                             r-ggmap
                             r-dplyr
                             r-beepr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=movegroup")
    (synopsis
     "Visualizing and Quantifying Space Use Data for Groups of Animals")
    (description
     "Offers an easy and automated way to scale up individual-level space use analysis
to that of groups.  Contains a function from the move package to calculate a
dynamic Brownian bridge movement model from movement data for individual
animals, as well as functions to visualize and quantify space use for
individuals aggregated in groups.  Originally written with passive acoustic
telemetry in mind, this package also provides functionality to account for
unbalanced acoustic receiver array designs, and satellite tag data.")
    (license license:expat)))

(define-public r-movecost
  (package
    (name "r-movecost")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "movecost" version))
       (sha256
        (base32 "1rq0q8myszcssdxn77lrf7z6yisc5318k32qxaj98rpggr7pfs93"))))
    (properties `((upstream-name . "movecost")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sp
                             r-sf
                             r-raster
                             r-matrix
                             r-gdistance
                             r-elevatr
                             r-chron))
    (home-page "https://cran.r-project.org/package=movecost")
    (synopsis
     "Calculation of Slope-Dependant Accumulated Cost Surface, Least-Cost Paths, Least-Cost Corridors, Least-Cost Networks Related to Human Movement Across the Landscape")
    (description
     "This package provides the facility to calculate non-isotropic accumulated cost
surface, least-cost paths, least-cost corridors, least-cost networks using a
number of human-movement-related cost functions that can be selected by the
user.  It just requires a Digital Terrain Model, a start location and
(optionally) destination locations.  See Alberti (2019)
<doi:10.1016/j.softx.2019.100331>.")
    (license license:gpl2+)))

(define-public r-move2
  (package
    (name "r-move2")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "move2" version))
       (sha256
        (base32 "0ms6s0znjdrf7sfs7ydy6jfic39xi8fbd4r89z3scks7lhm0q1qb"))))
    (properties `((upstream-name . "move2")))
    (build-system r-build-system)
    (propagated-inputs (list r-vroom
                             r-vctrs
                             r-units
                             r-tidyselect
                             r-tibble
                             r-sf
                             r-rlang
                             r-dplyr
                             r-cli
                             r-bit64
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://bartk.gitlab.io/move2/")
    (synopsis "Processing and Analysing Animal Trajectories")
    (description
     "This package provides tools to handle, manipulate and explore trajectory data,
with an emphasis on data from tracked animals.  The package is designed to
support large studies with several million location records and keep track of
units where possible.  Data import directly from movebank
<https://www.movebank.org/cms/movebank-main> and files is facilitated.")
    (license license:gpl3+)))

(define-public r-move
  (package
    (name "r-move")
    (version "4.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "move" version))
       (sha256
        (base32 "01yc69xy35byl0mnr8f9578175zvil2z201bwzf0zkw1m72jwjzc"))))
    (properties `((upstream-name . "move")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-terra
                             r-sp
                             r-rcpp
                             r-raster
                             r-memoise
                             r-httr
                             r-geosphere))
    (native-inputs (list r-knitr))
    (home-page "https://bartk.gitlab.io/move/")
    (synopsis "Visualizing and Analyzing Animal Track Data")
    (description
     "This package contains functions to access movement data stored in movebank.org
as well as tools to visualize and statistically analyze animal movement data,
among others functions to calculate dynamic Brownian Bridge Movement Models.
Move helps addressing movement ecology questions.")
    (license license:gpl3+)))

(define-public r-mousetrap
  (package
    (name "r-mousetrap")
    (version "3.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mousetrap" version))
       (sha256
        (base32 "05dxbzwghm8gj6jwwy9wyz0xwyrj614x7db00zlb3gwz1kkyd9sz"))))
    (properties `((upstream-name . "mousetrap")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-rlang
                             r-rcpp
                             r-rcolorbrewer
                             r-psych
                             r-pracma
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-fields
                             r-fastcluster
                             r-dplyr
                             r-diptest
                             r-cstab))
    (home-page "https://pascalkieslich.github.io/mousetrap/")
    (synopsis "Process and Analyze Mouse-Tracking Data")
    (description
     "Mouse-tracking, the analysis of mouse movements in computerized experiments, is
a method that is becoming increasingly popular in the cognitive sciences.  The
mousetrap package offers functions for importing, preprocessing, analyzing,
aggregating, and visualizing mouse-tracking data.  An introduction into
mouse-tracking analyses using mousetrap can be found in Wulff, Kieslich,
Henninger, Haslbeck, & Schulte-Mecklenbeck (2023) <doi:10.31234/osf.io/v685r>
(preprint: <https://osf.io/preprints/psyarxiv/v685r>).")
    (license license:gpl3)))

(define-public r-mousetrajectory
  (package
    (name "r-mousetrajectory")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mousetRajectory" version))
       (sha256
        (base32 "1l4ivaklplpwqr78i190agfijrjx87b1r7i798j5mh3lcxx88r4x"))))
    (properties `((upstream-name . "mousetRajectory")))
    (build-system r-build-system)
    (propagated-inputs (list r-signal r-lifecycle))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mc-schaaf/mousetRajectory")
    (synopsis "Mouse Trajectory Analyses for Behavioural Scientists")
    (description
     "Helping psychologists and other behavioural scientists to analyze mouse movement
(and other 2-D trajectory) data.  Bundles together several functions that
compute spatial measures (e.g., maximum absolute deviation, area under the
curve, sample entropy) or provide a shorthand for procedures that are frequently
used (e.g., time normalization, linear interpolation, extracting initiation and
movement times).  For more information on these dependent measures, see Wirth et
al. (2020) <doi:10.3758/s13428-020-01409-0>.")
    (license license:gpl3+)))

(define-public r-mountainplot
  (package
    (name "r-mountainplot")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mountainplot" version))
       (sha256
        (base32 "11fz2p6hpamk38n41z97af4az55j69rlais6ff61j9imdkq6f1pr"))))
    (properties `((upstream-name . "mountainplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (native-inputs (list r-knitr))
    (home-page "https://kwstat.github.io/mountainplot/")
    (synopsis "Mountain Plots, Folded Empirical Cumulative Distribution Plots")
    (description
     "Lattice functions for drawing folded empirical cumulative distribution plots, or
mountain plots.  A mountain plot is similar to an empirical CDF plot, except
that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an
inverted scale at the right side.  See Monti (1995)
<doi:10.1080/00031305.1995.10476179>.")
    (license license:gpl3)))

(define-public r-moult
  (package
    (name "r-moult")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moult" version))
       (sha256
        (base32 "0w6gavrybcmav3skglc787d4i7ddfhsbm52lbqcybgqkc9r7361z"))))
    (properties `((upstream-name . "moult")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-formula))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=moult")
    (synopsis "Models for Analysing Moult in Birds")
    (description
     "This package provides functions to estimate start and duration of moult from
moult data, based on models developed in Underhill and Zucchini (1988, 1990).")
    (license license:gpl2)))

(define-public r-motorneuron
  (package
    (name "r-motorneuron")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "motoRneuron" version))
       (sha256
        (base32 "1hdsf4c1v5hfaqpi5y1v60zqq5fdmlqdvzpsq77diclp9vzdshja"))))
    (properties `((upstream-name . "motoRneuron")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-dygraphs))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/tweedell/motoRneuron")
    (synopsis
     "Analyzing Paired Neuron Discharge Times for Time-Domain Synchronization")
    (description
     "The temporal relationship between motor neurons can offer explanations for
neural strategies.  We combined functions to reduce neuron action potential
discharge data and analyze it for short-term, time-domain synchronization.  Even
more so, @code{motoRneuron} combines most available methods for the determining
cross correlation histogram peaks and most available indices for calculating
synchronization into simple functions.  See Nordstrom, Fuglevand, and Enoka
(1992) <doi:10.1113/jphysiol.1992.sp019244> for a more thorough introduction.")
    (license license:gpl2)))

(define-public r-motmot
  (package
    (name "r-motmot")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "motmot" version))
       (sha256
        (base32 "09qsyc4ifdl316kjqqkarmflpjcd0ivkhx5fzi3502anj6dxpjnq"))))
    (properties `((upstream-name . "motmot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-mvtnorm
                             r-ks
                             r-coda
                             r-caper
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://puttickbiology.wordpress.com/motmot/")
    (synopsis "Models of Trait Macroevolution on Trees")
    (description
     "This package provides functions for fitting models of trait evolution on
phylogenies for continuous traits.  The majority of functions described in
Thomas and Freckleton (2012) <doi:10.1111/j.2041-210X.2011.00132.x> 	and include
functions that allow for tests of variation in the rates of trait evolution.")
    (license license:gpl2+)))

(define-public r-motifr
  (package
    (name "r-motifr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "motifr" version))
       (sha256
        (base32 "01synariq39mzhx12jbcpwn982piakakl97sgs33xa8xrbwzcxgm"))))
    (properties `((upstream-name . "motifr")))
    (build-system r-build-system)
    (inputs (list python python-pandas python-numpy))
    (propagated-inputs (list r-tidygraph
                             r-tibble
                             r-scales
                             r-rlang
                             r-reticulate
                             r-reshape2
                             r-rcolorbrewer
                             r-purrr
                             r-network
                             r-intergraph
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://marioangst.github.io/motifr/")
    (synopsis "Motif Analysis in Multi-Level Networks")
    (description
     "This package provides tools for motif analysis in multi-level networks.
Multi-level networks combine multiple networks in one, e.g. social-ecological
networks.  Motifs are small configurations of nodes and edges (subgraphs)
occurring in networks.  motifr can visualize multi-level networks, count
multi-level network motifs and compare motif occurrences to baseline models.  It
also identifies contributions of existing or potential edges to motifs to find
critical or missing edges.  The package is in many parts an R wrapper for the
excellent SES@code{MotifAnalyser} Python package written by Tim Seppelt.")
    (license license:expat)))

(define-public r-motifcluster
  (package
    (name "r-motifcluster")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "motifcluster" version))
       (sha256
        (base32 "1ynfjz9di1f1cm6bmx81a8mzkjkhclk21mx8b6css953dih06ngb"))))
    (properties `((upstream-name . "motifcluster")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-matrix r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wgunderwood/motifcluster")
    (synopsis "Motif-Based Spectral Clustering of Weighted Directed Networks")
    (description
     "This package provides tools for spectral clustering of weighted directed
networks using motif adjacency matrices.  Methods perform well on large and
sparse networks, and random sampling methods for generating weighted directed
networks are also provided.  Based on methodology detailed in Underwood, Elliott
and Cucuringu (2020) <@code{arXiv:2004.01293>}.")
    (license license:gpl3)))

(define-public r-motif
  (package
    (name "r-motif")
    (version "0.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "motif" version))
       (sha256
        (base32 "0khvplcnw868cspzvn3mimvsvygxaw7ay6py2da9pjzmcbij9g36"))))
    (properties `((upstream-name . "motif")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stars
                             r-sf
                             r-rcpparmadillo
                             r-rcpp
                             r-philentropy
                             r-comat))
    (native-inputs (list r-knitr))
    (home-page "https://jakubnowosad.com/motif/")
    (synopsis "Local Pattern Analysis")
    (description
     "Describes spatial patterns of categorical raster data for any defined regular
and irregular areas.  Patterns are described quantitatively using built-in
signatures based on co-occurrence matrices but also allows for any user-defined
functions.  It enables spatial analysis such as search, change detection, and
clustering to be performed on spatial patterns (Nowosad (2021)
<doi:10.1007/s10980-020-01135-0>).")
    (license license:expat)))

(define-public r-mote
  (package
    (name "r-mote")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOTE" version))
       (sha256
        (base32 "17y2l2rmw7fhvknyh5z09i7hd8nhamx12g00rcymp75gqkz9lzc7"))))
    (properties `((upstream-name . "MOTE")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-mbess r-ez))
    (home-page "https://cran.r-project.org/package=MOTE")
    (synopsis "Effect Size and Confidence Interval Calculator")
    (description
     "Measure of the Effect ('MOTE') is an effect size calculator, including a wide
variety of effect sizes in the mean differences family (all versions of d) and
the variance overlap family (eta, omega, epsilon, r).  MOTE provides non-central
confidence intervals for each effect size, relevant test statistics, and output
for reporting in APA Style (American Psychological Association, 2010,
<ISBN:1433805618>) with @code{LaTeX}'.  In research, an over-reliance on
p-values may conceal the fact that a study is under-powered (Halsey,
Curran-Everett, Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>).  A test may
be statistically significant, yet practically inconsequential (Fritz, Scherndl,
& KÃ¼hberger, 2012 <doi:10.1177/0959354312436870>).  Although the American
Psychological Association has long advocated for the inclusion of effect sizes
(Wilkinson & American Psychological Association Task Force on Statistical
Inference, 1999 <doi:10.1037/0003-066X.54.8.594>), the vast majority of
peer-reviewed, published academic studies stop short of reporting effect sizes
and confidence intervals (Cumming, 2013, <doi:10.1177/0956797613504966>).  MOTE
simplifies the use and interpretation of effect sizes and confidence intervals.
For more information, visit <https://www.aggieerin.com/shiny-server>.")
    (license license:lgpl3)))

(define-public r-motbfs
  (package
    (name "r-motbfs")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MoTBFs" version))
       (sha256
        (base32 "03c1k5vvswlhbsivw6yznw1v0cdl8avs514iaa2v2994j2yk40j6"))))
    (properties `((upstream-name . "MoTBFs")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-matrix r-lpsolve r-ggm r-bnlearn))
    (home-page "https://cran.r-project.org/package=MoTBFs")
    (synopsis
     "Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions")
    (description
     "Learning, manipulation and evaluation of mixtures of truncated basis functions
(@code{MoTBFs}), which include mixtures of polynomials (MOPs) and mixtures of
truncated exponentials (MTEs). @code{MoTBFs} are a flexible framework for
modelling hybrid Bayesian networks (I. PÃ©rez-BernabÃ©, A. SalmerÃ³n, H.
Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen,
I. PÃ©rez-BernabÃ©, A. SalmerÃ³n (2014) <doi:10.1016/j.ijar.2013.09.012>; I.
PÃ©rez-BernabÃ©, A. FernÃ¡ndez, R. RumÃ­, A. SalmerÃ³n (2016)
<doi:10.1007/s10618-015-0429-7>).  The package provides functionality for
learning univariate, multivariate and conditional densities, with the
possibility of incorporating prior knowledge.  Structural learning of hybrid
Bayesian networks is also provided.  A set of useful tools is provided,
including plotting, printing and likelihood evaluation.  This package makes use
of S3 objects, with two new classes called motbf and jointmotbf'.")
    (license license:lgpl3)))

(define-public r-mosum
  (package
    (name "r-mosum")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mosum" version))
       (sha256
        (base32 "1zixjf3s381d44s63qzz4hpx4cd2d3rgzvzgg75gpzfkvxnf5f8c"))))
    (properties `((upstream-name . "mosum")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-rcolorbrewer r-plot3d))
    (home-page "https://cran.r-project.org/package=mosum")
    (synopsis "Moving Sum Based Procedures for Changes in the Mean")
    (description
     "Implementations of MOSUM-based statistical procedures and algorithms for
detecting multiple changes in the mean.  This comprises the MOSUM procedure for
estimating multiple mean changes from Eichinger and Kirch (2018)
<doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and
Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap
procedure for generating confidence intervals about the locations of change
points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>.
See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which
accompanies the R package.")
    (license license:gpl3+)))

(define-public r-most
  (package
    (name "r-most")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOST" version))
       (sha256
        (base32 "07y3aw2ldgq4jiz2kk2rkbqd4l298wsa5sq3xs5p21k7g33gxxnv"))))
    (properties `((upstream-name . "MOST")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MOST")
    (synopsis "Multiphase Optimization Strategy")
    (description
     "This package provides functions similar to the SAS macros previously provided to
accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826> and Dziak,
Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers which outline
practical benefits and challenges of factorial and fractional factorial
experiments for scientists interested in developing biological and/or behavioral
interventions, especially in the context of the multiphase optimization strategy
(see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
currently contains three functions.  First, @code{RelativeCosts1}() draws a
graph of the relative cost of complete and reduced factorial designs versus
other alternatives.  Second, @code{RandomAssignmentGenerator}() returns a
dataframe which contains a list of random numbers that can be used to
conveniently assign participants to conditions in an experiment with many
conditions.  Third, @code{FactorialPowerPlan}() estimates the power, detectable
effect size, or required sample size of a factorial or fractional factorial
experiment, for main effects or interactions, given several possible choices of
effect size metric, and allowing pretests and clustering.")
    (license license:gpl2+)))

(define-public r-moss
  (package
    (name "r-moss")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOSS" version))
       (sha256
        (base32 "0gjsyv74hhjknlnplc8157mmypnfy1yjrs400pz0p4744k5c8c7s"))))
    (properties `((upstream-name . "MOSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtsne r-dbscan r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/agugonrey/MOSS")
    (synopsis "Multi-Omic Integration via Sparse Singular Value Decomposition")
    (description
     "High dimensionality, noise and heterogeneity among samples and features
challenge the omic integration task.  Here we present an omic integration method
based on sparse singular value decomposition (SVD) to deal with these
limitations, by: a.  obtaining the main axes of variation of the combined omics,
b.  imposing sparsity constraints at both subjects (rows) and features (columns)
levels using Elastic Net type of shrinkage, and c.  allowing both linear and
non-linear projections (via t-Stochastic Neighbor Embedding) of the omic data to
detect clusters in very convoluted data (Gonzalez-Reymundez et.  al, 2022)
<doi:10.1093/bioinformatics/btac179>.")
    (license license:gpl2)))

(define-public r-mosqcontrol
  (package
    (name "r-mosqcontrol")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mosqcontrol" version))
       (sha256
        (base32 "199rkfisagr4kfx358f8yrbls00fng0kq8siyrbfwwf8qrba9fla"))))
    (properties `((upstream-name . "mosqcontrol")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfsmisc
                             r-pracma
                             r-nloptr
                             r-nlcoptim
                             r-magrittr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mosqcontrol")
    (synopsis "Mosquito Control Resource Optimization")
    (description
     "This project aims to make an accessible model for mosquito control resource
optimization.  The model uses data provided by users to estimate the mosquito
populations in the sampling area for the sampling time period, and the optimal
time to apply a treatment or multiple treatments.")
    (license license:expat)))

(define-public r-mosaicmodel
  (package
    (name "r-mosaicmodel")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mosaicModel" version))
       (sha256
        (base32 "1gx4rzh0h922gyb50vz6q5vqqp5ry5hf2mq95948q2gcady91k5w"))))
    (properties `((upstream-name . "mosaicModel")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tidyr
                             r-tibble
                             r-testthat
                             r-mosaiccore
                             r-mass
                             r-lazyeval
                             r-knitr
                             r-ggplot2
                             r-ggformula
                             r-dplyr
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mosaicModel")
    (synopsis
     "An Interface to Statistical Modeling Independent of Model Architecture")
    (description
     "This package provides functions for evaluating, displaying, and interpreting
statistical models.  The goal is to abstract the operations on models from the
particular architecture of the model.  For instance, calculating effect sizes
rather than looking at coefficients.  The package includes interfaces to both
regression and classification architectures, including lm(), glm(), rlm() in
MASS', random forests and recursive partitioning, k-nearest neighbors, linear
and quadratic discriminant analysis, and models produced by the caret package's
train().  It's straightforward to add in other other model architectures.")
    (license license:expat)))

(define-public r-mosaiccalc
  (package
    (name "r-mosaiccalc")
    (version "0.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mosaicCalc" version))
       (sha256
        (base32 "1fq3fjsj7jwai5hg36bi1d3plf98mywyn08yrcwrphnh4h42i71r"))))
    (properties `((upstream-name . "mosaicCalc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-sp
                             r-ryacas
                             r-rlang
                             r-orthopolynom
                             r-mosaiccore
                             r-mosaic
                             r-metr
                             r-matrix
                             r-mass
                             r-glue
                             r-ggplot2
                             r-ggformula
                             r-dplyr
                             r-deriv
                             r-calculus))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ProjectMOSAIC/mosaicCalc")
    (synopsis "R-Language Based Calculus Operations for Teaching")
    (description
     "Software to support the introductory *MOSAIC Calculus* textbook
<https://www.mosaic-web.org/MOSAIC-Calculus/>), one of many data- and
modeling-oriented educational resources developed by Project MOSAIC
(<https://www.mosaic-web.org/>).  Provides symbolic and numerical
differentiation and integration, as well as support for applied linear algebra
(for data science), and differential equations/dynamics.  Includes
grammar-of-graphics-based functions for drawing vector fields, trajectories,
etc.  The software is suitable for general use, but intended mainly for teaching
calculus.")
    (license license:gpl2+)))

(define-public r-mortcast
  (package
    (name "r-mortcast")
    (version "2.7-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MortCast" version))
       (sha256
        (base32 "1hw20wb0sl8q09vv68hid90ma2ml866kglsjf38kqky1whmihfbc"))))
    (properties `((upstream-name . "MortCast")))
    (build-system r-build-system)
    (propagated-inputs (list r-wpp2017))
    (home-page "https://cran.r-project.org/package=MortCast")
    (synopsis "Estimation and Projection of Age-Specific Mortality Rates")
    (description
     "Age-specific mortality rates are estimated and projected using the Kannisto,
Lee-Carter and related methods as described in Sevcikova et al. (2016)
<doi:10.1007/978-3-319-26603-9_15>.")
    (license license:gpl2+)))

(define-public r-mortalitytables
  (package
    (name "r-mortalitytables")
    (version "2.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MortalityTables" version))
       (sha256
        (base32 "1vk69smq4px796xsk4j4zfzrddndwns86jr2qi56aqqg6mix341p"))))
    (properties `((upstream-name . "MortalityTables")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-pracma r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.open-tools.net/R/r-mortality-tables")
    (synopsis "Framework for Various Types of Mortality / Life Tables")
    (description
     "This package provides classes to implement, analyze and plot cohort life tables
for actuarial calculations.  Birth-year dependent cohort mortality tables using
a yearly trend to extrapolate from a base year are implemented, as well as
period life table, cohort life tables using an age shift, and merged life
tables.  Additionally, several data sets from various countries are included to
provide widely-used tables out of the box.")
    (license license:gpl2+)))

(define-public r-mortalitylaws
  (package
    (name "r-mortalitylaws")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MortalityLaws" version))
       (sha256
        (base32 "1jczy0brmma71cn2xhhnl1g2k9nr6l3zf5zw6bvwn10l1gid4908"))))
    (properties `((upstream-name . "MortalityLaws")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rvest
                             r-rcurl
                             r-pbapply
                             r-minpack-lm
                             r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mpascariu/MortalityLaws")
    (synopsis "Parametric Mortality Models, Life Tables and HMD")
    (description
     "Fit the most popular human mortality laws', and construct full and abridge life
tables given various input indices.  A mortality law is a parametric function
that describes the dying-out process of individuals in a population during a
significant portion of their life spans.  For a comprehensive review of the most
important mortality laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>.
Practical functions for downloading data from various human mortality databases
are provided as well.")
    (license license:expat)))

(define-public r-mortalitygaps
  (package
    (name "r-mortalitygaps")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MortalityGaps" version))
       (sha256
        (base32 "0nfm8xa7gnshrl8hsvmvr3za8nbppp7f7i5h4ni204w7xwvh8jxc"))))
    (properties `((upstream-name . "MortalityGaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-mass r-forecast r-crch))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mpascariu/MortalityGaps")
    (synopsis "The Double-Gap Life Expectancy Forecasting Model")
    (description
     "Life expectancy is highly correlated over time among countries and between males
and females.  These associations can be used to improve forecasts.  Here we have
implemented a method for forecasting female life expectancy based on analysis of
the gap between female life expectancy in a country compared with the record
level of female life expectancy in the world.  Second, to forecast male life
expectancy, the gap between male life expectancy and female life expectancy in a
country is analysed.  We named this method the Double-Gap model.  For a detailed
description of the method see Pascariu et al. (2017).
<doi:10.1016/j.insmatheco.2017.09.011>.")
    (license license:gpl3)))

(define-public r-mortaar
  (package
    (name "r-mortaar")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mortAAR" version))
       (sha256
        (base32 "1n3s704k2lqpcbzk8rqa28jcqz37g2bdjkj6x1bygcbfdic2z0dc"))))
    (properties `((upstream-name . "mortAAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-reshape2 r-rdpack r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ISAAKiel/mortAAR")
    (synopsis "Analysis of Archaeological Mortality Data")
    (description
     "This package provides a collection of functions for the analysis of
archaeological mortality data (on the topic see e.g. Chamberlain 2006
<https://books.google.de/books?id=@code{nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>}).
 It takes demographic data in different formats and displays the result in a
standard life table as well as plots the relevant indices (percentage of deaths,
survivorship, probability of death, life expectancy, percentage of population).")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mort
  (package
    (name "r-mort")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mort" version))
       (sha256
        (base32 "09z5j2kknm8jk9153hxkyw5wk08bl2733jjdgy3g0iwna6fl5wrr"))))
    (properties `((upstream-name . "mort")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lubridate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rosieluain/mort")
    (synopsis
     "Identifying Potential Mortalities and Expelled Tags in Aquatic Acoustic Telemetry Arrays")
    (description
     "This package provides a toolkit for identifying potential mortalities and
expelled tags in aquatic acoustic telemetry arrays.  Designed for arrays with
non-overlapping receivers.")
    (license license:gpl3+)))

(define-public r-morse
  (package
    (name "r-morse")
    (version "3.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morse" version))
       (sha256
        (base32 "1cq1xmabx4glgkxh6vn33ma1mb97079p5mmlydrm07h7r542k4vz"))))
    (properties `((upstream-name . "morse")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-tibble
                             r-rjags
                             r-reshape2
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-epitools
                             r-dplyr
                             r-desolve
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.in2p3.fr/mosaic-software/morse")
    (synopsis "Modelling Reproduction and Survival Data in Ecotoxicology")
    (description
     "Advanced methods for a valuable quantitative environmental risk assessment using
Bayesian inference of survival and reproduction Data.  Among others, it
facilitates Bayesian inference of the general unified threshold model of
survival (GUTS).  See our companion paper Baudrot and Charles (2021)
<doi:10.21105/joss.03200>, as well as complementary details in Baudrot et al.
(2018) <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al. (2017)
<doi:10.1021/acs.est.6b05326>.")
    (license license:expat)))

(define-public r-morrowplots
  (package
    (name "r-morrowplots")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morrowplots" version))
       (sha256
        (base32 "09kpsarbv32cylr0qp067m184lnj95p50ayi49w7sgwrs70xdanb"))))
    (properties `((upstream-name . "morrowplots")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://SandiCal.github.io/morrowplots/")
    (synopsis "Historical Agricultural Data from the University of Illinois")
    (description
     "Agricultural data for 1888-2021 from the Morrow Plots at the University of
Illinois.  The world's second oldest ongoing agricultural experiment, the Morrow
Plots measure the impact of crop rotation and fertility treatments on corn
yields.  The data includes planting information and annual yield measures for
corn grown continuously and in rotation with other crops, in treated and
untreated soil.")
    (license license:expat)))

(define-public r-morphotools2
  (package
    (name "r-morphotools2")
    (version "1.0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MorphoTools2" version))
       (sha256
        (base32 "18fvkklh4p89mcvjbnxb7cimylpj46ipslqsxkkpyzrb88wwlsjk"))))
    (properties `((upstream-name . "MorphoTools2")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-statmatch
                             r-plot3d
                             r-mass
                             r-heplots
                             r-ellipse
                             r-class
                             r-car
                             r-candisc
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MarekSlenker/MorphoTools2")
    (synopsis "Multivariate Morphometric Analysis")
    (description
     "This package provides tools for multivariate analyses of morphological data,
wrapped in one package, to make the workflow convenient and fast.  Statistical
and graphical tools provide a comprehensive framework for checking and
manipulating input data, statistical analyses, and visualization of results.
Several methods are provided for the analysis of raw data, to make the dataset
ready for downstream analyses.  Integrated statistical methods include
hierarchical classification, principal component analysis, principal coordinates
analysis, non-metric multidimensional scaling, and multiple discriminant
analyses: canonical, stepwise, and classificatory (linear, quadratic, and the
non-parametric k nearest neighbours).  The philosophy of the package is
described in Å lenker et al.  2022.")
    (license license:gpl3)))

(define-public r-morphoscape
  (package
    (name "r-morphoscape")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Morphoscape" version))
       (sha256
        (base32 "1f4cj5086r1849dwmha8drf7jq56p7bp0fj2liqksppy7gxmdrs4"))))
    (properties `((upstream-name . "Morphoscape")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-spatial
                             r-sp
                             r-scales
                             r-ggplot2
                             r-concaveman
                             r-automap
                             r-alphahull))
    (native-inputs (list r-knitr))
    (home-page "https://blakedickson.github.io/Morphoscape/")
    (synopsis "Computation and Visualization of Adaptive Landscapes")
    (description
     "This package implements adaptive landscape methods first described by Polly et
al. (2016) <doi:10.1080/02724634.2016.1111225> for the integration, analysis and
visualization of biological trait data on a phenotypic morphospace - typically
defined by shape metrics.")
    (license license:gpl2+)))

(define-public r-morphoregions
  (package
    (name "r-morphoregions")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MorphoRegions" version))
       (sha256
        (base32 "1w4b666m7whp8vydrvkn0b4va89s18iz74njkjcq1r23rpczvli1"))))
    (properties `((upstream-name . "MorphoRegions")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rcolorbrewer
                             r-pbapply
                             r-ggplot2
                             r-cluster
                             r-chk))
    (native-inputs (list r-knitr))
    (home-page "https://aagillet.github.io/MorphoRegions/")
    (synopsis
     "Analysis of Regionalization Patterns in Serially Homologous Structures")
    (description
     "Computes the optimal number of regions (or subdivisions) and their position in
serial structures without a priori assumptions and to visualize the results.
After reducing data dimensionality with the built-in function for data
ordination, regions are fitted as segmented linear regressions along the serial
structure.  Every region boundary position and increasing number of regions are
iteratively fitted and the best model (number of regions and boundary positions)
is selected with an information criterion.  This package expands on the previous
regions package (Jones et al., Science 2018) with improved computation and more
fitting and plotting options.")
    (license license:gpl2+)))

(define-public r-morphomap
  (package
    (name "r-morphomap")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morphomap" version))
       (sha256
        (base32 "0kin6jvg3vlsbga0zr34nnq1zixicw7vsphiw45aifyfbnwiw0av"))))
    (properties `((upstream-name . "morphomap")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rvcg
                             r-rgl
                             r-oce
                             r-morpho
                             r-mgcv
                             r-lattice
                             r-geometry
                             r-desctools
                             r-colorramps
                             r-arothron))
    (home-page "https://cran.r-project.org/package=morphomap")
    (synopsis
     "Morphometric Maps, Bone Landmarking and Cross Sectional Geometry")
    (description
     "Extract cross sections from long bone meshes at specified intervals along the
diaphysis.  Calculate two and three-dimensional morphometric maps,
cross-sectional geometric parameters, and semilandmarks on the periosteal and
endosteal contours of each cross section.")
    (license license:gpl2)))

(define-public r-morphemepiece-data
  (package
    (name "r-morphemepiece-data")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morphemepiece.data" version))
       (sha256
        (base32 "1fs1g5vv5qwrab0cj5wff4r9skiaa5mwfawa2j32skga6niaqydz"))))
    (properties `((upstream-name . "morphemepiece.data")))
    (build-system r-build-system)
    (home-page "https://github.com/macmillancontentscience/morphemepiece.data")
    (synopsis "Data for Morpheme Tokenization")
    (description
     "This package provides data about morphemes, the smallest units of meaning in a
language.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-morphemepiece
  (package
    (name "r-morphemepiece")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morphemepiece" version))
       (sha256
        (base32 "1xilq8sz0q35xs4wzig5x29h5lqfin2x6pvkygn5pacl6wcdp80n"))))
    (properties `((upstream-name . "morphemepiece")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-piecemaker
                             r-morphemepiece-data
                             r-memoise
                             r-magrittr
                             r-fastmatch
                             r-dlr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/macmillancontentscience/morphemepiece")
    (synopsis "Morpheme Tokenization")
    (description
     "Tokenize text into morphemes.  The morphemepiece algorithm uses a lookup table
to determine the morpheme breakdown of words, and falls back on a modified
wordpiece tokenization algorithm for words not found in the lookup table.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-morph
  (package
    (name "r-morph")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morph" version))
       (sha256
        (base32 "0qx3wdcrilcp1hlsgaxbb4afcnkyx0nxwampm59ssc3zps2wh2cq"))))
    (properties `((upstream-name . "morph")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rgl r-reshape2 r-igraph))
    (home-page "https://cran.r-project.org/package=morph")
    (synopsis "3D Segmentation of Voxels into Morphologic Classes")
    (description
     "Automatically segments a 3D array of voxels into mutually exclusive
morphological elements.  This package extends existing work for segmenting 2D
binary raster data.  A paper documenting this approach has been accepted for
publication in the journal Landscape Ecology.  Detailed references will be
updated here once those are known.")
    (license license:gpl3)))

(define-public r-morestopwords
  (package
    (name "r-morestopwords")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morestopwords" version))
       (sha256
        (base32 "0j7icm2kim2dr9wpddg2gkc4b0pp0amg5why9rxyg74c3wj6np5g"))))
    (properties `((upstream-name . "morestopwords")))
    (build-system r-build-system)
    (home-page "https://fatelarico.github.io/morestopwords.html")
    (synopsis "All Stop Words in One Place")
    (description
     "This package provides a standalone package combining several stop-word lists for
65 languages with a median of 329 stop words for language and over 1,000 entries
for English, Breton, Latin, Slovenian, and Ancient Greek! The user automatically
gets access to all the unique stop words contained in: the @code{StopwordISO}
repository; the Natural Language Toolkit for python'; the Snowball stop-word
list; the R package quanteda'; the marimo repository; the Perseus project; and
A. Berra's list of stop words for Ancient Greek and Latin.")
    (license license:expat)))

(define-public r-morepls
  (package
    (name "r-morepls")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "morepls" version))
       (sha256
        (base32 "14rpzq9833b5idf8jzx83rdmy3w7bk44iafpf5ynlnvz7lyc96fv"))))
    (properties `((upstream-name . "morepls")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-pls r-ggrepel r-ggplot2))
    (home-page "https://cran.r-project.org/package=morepls")
    (synopsis "Interpretation Tools for Partial Least Squares Regression")
    (description
     "Various kinds of plots (observations, variables, correlations, weights,
regression coefficients and Variable Importance in the Projection) and aids to
interpretation (coefficients, Q2, correlations, redundancies) for partial least
squares regressions computed with the pls package, following Tenenhaus (1998,
ISBN:2-7108-0735-1).")
    (license license:gpl2+)))

(define-public r-moreparty
  (package
    (name "r-moreparty")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moreparty" version))
       (sha256
        (base32 "0zny9bffj8dfl6i76ylrcgrbfbawk6aksdfkn4vf57zg7qfv0yqh"))))
    (properties `((upstream-name . "moreparty")))
    (build-system r-build-system)
    (propagated-inputs (list r-vip
                             r-varimp
                             r-shinywidgets
                             r-shiny
                             r-rlang
                             r-rclipboard
                             r-plyr
                             r-phosphoricons
                             r-pdp
                             r-partykit
                             r-party
                             r-measures
                             r-mass
                             r-iml
                             r-ggplot2
                             r-foreach
                             r-dt
                             r-datamods))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=moreparty")
    (synopsis "Toolbox for Conditional Inference Trees and Random Forests")
    (description
     "Additions to party and partykit packages : tools for the interpretation of
forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti
et al (2017) <@code{arXiv:1310.5726>}, Hapfelmeier and Ulm (2013)
<doi:10.1016/j.csda.2012.09.020>, Altmann et al (2010)
<doi:10.1093/bioinformatics/btq134>) and parallelized versions of conditional
forest and variable importance functions.  Also modules and a shiny app for
conditional inference trees.")
    (license license:gpl2+)))

(define-public r-moranajp
  (package
    (name "r-moranajp")
    (version "0.9.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moranajp" version))
       (sha256
        (base32 "1vzid1j09s7v20y8bpfrl7xcz8lf8f5dg5npw3iz2ibvp2w5vjl7"))))
    (properties `((upstream-name . "moranajp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-rvest
                             r-rlang
                             r-purrr
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/matutosi/moranajp")
    (synopsis "Morphological Analysis for Japanese")
    (description
     "Supports morphological analysis for Japanese by using @code{MeCab}
<https://taku910.github.io/mecab/>, Sudachi
<https://github.com/@code{WorksApplications/Sudachi>}, Chamame
<https://chamame.ninjal.ac.jp/>, or Ginza
<https://github.com/megagonlabs/ginza>.  Can input a data.frame and obtain all
results of @code{MeCab} and the row number of the original data.frame as a text
id.")
    (license license:expat)))

(define-public r-moqa
  (package
    (name "r-moqa")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOQA" version))
       (sha256
        (base32 "17w4iwf38k0r2fiwqr420hkbbz1a3r89sqwnd2yn300zmaz64hpr"))))
    (properties `((upstream-name . "MOQA")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr r-psych r-gplots))
    (home-page "https://cran.r-project.org/package=MOQA")
    (synopsis "Basic Quality Data Assurance for Epidemiological Research")
    (description
     "With the provision of several tools and templates the MOSAIC project (DFG-Grant
Number HO 1937/2-1) supports the implementation of a central data management in
epidemiological research projects.  The MOQA package enables epidemiologists
with none or low experience in R to generate basic data quality reports for a
wide range of application scenarios.  See <https://mosaic-greifswald.de/> for
more information.  Please read and cite the corresponding open access
publication (using the former package-name) in METHODS OF INFORMATION IN
MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann
(2017) <doi:10.3414/ME16-01-0123>.
<https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.")
    (license license:agpl3)))

(define-public r-mopac
  (package
    (name "r-mopac")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mopac" version))
       (sha256
        (base32 "07ljy6rwicyrv20l7zyxq2lb7qqfpl5mpv834m2n5c4air7sy23m"))))
    (properties `((upstream-name . "mopac")))
    (build-system r-build-system)
    (home-page "https://github.com/sccmckenzie/mopac")
    (synopsis "Collection of Datasets Pertaining to Loop 1 \"Mopac\"")
    (description
     "This package provides real & simulated datasets containing time-series traffic
observations and additional information pertaining to Loop 1 \"Mopac\" located in
Austin, Texas.")
    (license license:expat)))

(define-public r-mop
  (package
    (name "r-mop")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mop" version))
       (sha256
        (base32 "1x17mnh2lrgkqr1nzhk5dxmwpv9812i3ryvlspqhbrfadj3s76l7"))))
    (properties `((upstream-name . "mop")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-snow r-rcpp r-foreach r-dosnow))
    (home-page "https://github.com/marlonecobos/mop")
    (synopsis "Mobility Oriented-Parity Metric")
    (description
     "This package provides a set of tools to perform multiple versions of the
Mobility Oriented-Parity metric.  This multivariate analysis helps to
characterize levels of dissimilarity between a set of conditions of reference
and another set of conditions of interest.  If predictive models are transferred
to conditions different from those over which models were calibrated (trained),
this metric helps to identify transfer conditions that differ substantially from
those of calibration.  These tools are implemented following principles proposed
in Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, and expanded to
obtain more detailed results that aid in interpretation.")
    (license license:gpl3+)))

(define-public r-moose
  (package
    (name "r-moose")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moose" version))
       (sha256
        (base32 "06zjg3dbb4ascnbi3k2cpzswi2mybdbm55d3d5zj17bpa6j0j4cz"))))
    (properties `((upstream-name . "moose")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=moose")
    (synopsis "Mean Squared Out-of-Sample Error Projection")
    (description
     "Projects mean squared out-of-sample error for a linear regression based upon the
methodology developed in Rohlfs (2022) <doi:10.48550/@code{arXiv.2209.01493>}.
It consumes as inputs the lm object from an estimated OLS regression (based on
the \"training sample\") and a data.frame of out-of-sample cases (the \"test
sample\") that have non-missing values for the same predictors.  The test sample
may or may not include data on the outcome variable; if it does, that variable
is not used.  The aim of the exercise is to project what what mean squared
out-of-sample error can be expected given the predictor values supplied in the
test sample.  Output consists of a list of three elements: the projected mean
squared out-of-sample error, the projected out-of-sample R-squared, and a vector
of out-of-sample \"hat\" or \"leverage\" values, as defined in the paper.")
    (license license:expat)))

(define-public r-moodler
  (package
    (name "r-moodler")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moodleR" version))
       (sha256
        (base32 "0wbrnbs03i6xzyhq82d75yjryfqfl11k55sj9h0j9f7z46dq7glp"))))
    (properties `((upstream-name . "moodleR")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-tidytext
                             r-stringr
                             r-scales
                             r-rsqlite
                             r-rpostgres
                             r-rmariadb
                             r-rlang
                             r-lifecycle
                             r-glue
                             r-ggwordcloud
                             r-ggplot2
                             r-dplyr
                             r-dbi
                             r-config
                             r-cli
                             r-anytime))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/chi2labs/moodleR")
    (synopsis "Helper Functions to Work with 'Moodle' Data")
    (description
     "This package provides a collection of functions to connect to a Moodle database,
cache relevant tables locally and generate learning analytics.  Moodle is an
open source Learning Management System (LMS) developed by @code{MoodleHQ}.  For
more information about Moodle, visit <https://moodle.org>.")
    (license license:expat)))

(define-public r-moodlequizr
  (package
    (name "r-moodlequizr")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moodlequizR" version))
       (sha256
        (base32 "1bigckdga0jqn0qx5y2x0g74g1ln2hmsdyrjvfmp0cvmscr9w271"))))
    (properties `((upstream-name . "moodlequizR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-nmcalc r-mvtnorm r-base64))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=moodlequizR")
    (synopsis "Easily Create Fully Randomized 'Moodle' Test Questions")
    (description
     "Routines to generate fully randomized moodle quizzes.  It also contains 15
examples and a shiny app.")
    (license license:gpl2+)))

(define-public r-moodef
  (package
    (name "r-moodef")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moodef" version))
       (sha256
        (base32 "03crry7lkk04nm6vh9fgzmyq27xvzs4c8nal1j46bbrkb8i984bq"))))
    (properties `((upstream-name . "moodef")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-xlsx
                             r-tidyr
                             r-tibble
                             r-snakecase
                             r-readxl
                             r-readr
                             r-purrr
                             r-magick
                             r-glue
                             r-blastula))
    (native-inputs (list r-knitr))
    (home-page "https://josesamos.github.io/moodef/")
    (synopsis "Defining 'Moodle' Elements from R")
    (description
     "The main objective of this package is to support the definition of Moodle
elements taking advantage of the power that R offers.  In this first version, it
allows the definition of questions to be included in the question bank.")
    (license license:expat)))

(define-public r-moocore
  (package
    (name "r-moocore")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moocore" version))
       (sha256
        (base32 "0qn95fv5j3z2vgks9y0kny1pq99ixadfmyr0ff1a778blgn7w7jf"))))
    (properties `((upstream-name . "moocore")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-matrixstats))
    (home-page "https://multi-objective.github.io/moocore/r/")
    (synopsis "Core Mathematical Functions for Multi-Objective Optimization")
    (description
     "Fast implementation of mathematical operations and performance metrics for
multi-objective optimization, including filtering and ranking of dominated
vectors according to Pareto optimality, computation of the empirical attainment
function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001)
<doi:10.1007/3-540-44719-9_15>, hypervolume metric, C.M. Fonseca, L. Paquete, M.
LÃ³pez-IbÃ¡Ã±ez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator,
inverted generational distance, and Vorob'ev threshold, expectation and
deviation, M. Binois, D. Ginsbourger, O. Roustant (2015)
<doi:10.1016/j.ejor.2014.07.032>, among others.")
    (license license:lgpl2.0+)))

(define-public r-montecarlosem
  (package
    (name "r-montecarlosem")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MonteCarloSEM" version))
       (sha256
        (base32 "0l9c6a8yxjd2n1fvsswx3csgz18g7d33ybbmpxlx6csl5f299ljd"))))
    (properties `((upstream-name . "MonteCarloSEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-lavaan))
    (home-page "https://cran.r-project.org/package=MonteCarloSEM")
    (synopsis "Monte Carlo Data Simulation Package")
    (description
     "Monte Carlo simulation allows testing different conditions given to the correct
structural equation models.  This package runs Monte Carlo simulations under
different conditions (such as sample size or normality of data).  Within the
package data sets can be simulated and run based on the given model.  First,
continuous and normal data sets are generated based on the given model.  Later
Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add
non-normality if exists.  When data generation is completed (or when generated
data sets are given) model test can also be run.  Please cite as \"OrÃ§an, F.
(2021). @code{MonteCarloSEM}: An R Package to Simulate Data for SEM.
International Journal of Assessment Tools in Education, 8 (3), 704-713.\".")
    (license license:gpl3)))

(define-public r-montecarlo
  (package
    (name "r-montecarlo")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MonteCarlo" version))
       (sha256
        (base32 "1jwq3by8zfy6sbzahcj5l0vicqn7yyqpb7xhfsaymfspm7xyq6pj"))))
    (properties `((upstream-name . "MonteCarlo")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall
                             r-snow
                             r-rlecuyer
                             r-reshape
                             r-codetools
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/FunWithR/MonteCarlo")
    (synopsis "Automatic Parallelized Monte Carlo Simulations")
    (description
     "Simplifies Monte Carlo simulation studies by automatically setting up loops to
run over parameter grids and parallelising the Monte Carlo repetitions.  It also
generates @code{LaTeX} tables.")
    (license license:gpl2)))

(define-public r-monte-carlo-se
  (package
    (name "r-monte-carlo-se")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Monte.Carlo.se" version))
       (sha256
        (base32 "1bkmah8s8hm3yxs8gz1ssr15s4l9s37675g7agx3g7f9a72v9zbs"))))
    (properties `((upstream-name . "Monte.Carlo.se")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Monte.Carlo.se")
    (synopsis "Monte Carlo Standard Errors")
    (description
     "Computes Monte Carlo standard errors for summaries of Monte Carlo output.
Summaries and their standard errors are based on columns of Monte Carlo
simulation output.  Dennis D. Boos and Jason A. Osborne (2015)
<doi:10.1111/insr.12087>.")
    (license license:gpl3)))

(define-public r-monreg
  (package
    (name "r-monreg")
    (version "0.1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monreg" version))
       (sha256
        (base32 "1i0dxv6azb6x2i7yd9w0254b50qgiidccrn5jn6rqvb5y3dj2rn1"))))
    (properties `((upstream-name . "monreg")))
    (build-system r-build-system)
    (home-page "https://gitlab.com/scottkosty/monreg")
    (synopsis "Nonparametric Monotone Regression")
    (description
     "Estimates monotone regression and variance functions in a nonparametric model,
based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.")
    (license license:gpl2+)))

(define-public r-monotonicity
  (package
    (name "r-monotonicity")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monotonicity" version))
       (sha256
        (base32 "0bb6sl8yykry25a86qfggi36ggr1xrxcqsnzp6rb6na228hbwa2z"))))
    (properties `((upstream-name . "monotonicity")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-mass r-lmtest))
    (home-page "https://github.com/skoestlmeier/monotonicity")
    (synopsis
     "Test for Monotonicity in Expected Asset Returns, Sorted by Portfolios")
    (description
     "Test for monotonicity in financial variables sorted by portfolios.  It is
conventional practice in empirical research to form portfolios of assets ranked
by a certain sort variable.  A t-test is then used to consider the mean return
spread between the portfolios with the highest and lowest values of the sort
variable.  Yet comparing only the average returns on the top and bottom
portfolios does not provide a sufficient way to test for a monotonic relation
between expected returns and the sort variable.  This package provides
nonparametric tests for the full set of monotonic patterns by Patton, A. and
Timmermann, A. (2010) <doi:10.1016/j.jfineco.2010.06.006> and compares the
proposed results with extant alternatives such as t-tests, Bonferroni bounds,
and multivariate inequality tests through empirical applications and
simulations.")
    (license license:bsd-3)))

(define-public r-monotone
  (package
    (name "r-monotone")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monotone" version))
       (sha256
        (base32 "0izj9p51f5g291n7zvgh9ncjjm88aacz6imy08kpwb0d9bgxl1mf"))))
    (properties `((upstream-name . "monotone")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=monotone")
    (synopsis "Performs Monotone Regression")
    (description
     "The monotone package contains a fast up-and-down-blocks implementation for the
pool-adjacent-violators algorithm for simple linear ordered monotone regression,
including two spin-off functions for unimodal and bivariate monotone regression
(see <doi:10.18637/jss.v102.c01>).")
    (license license:gpl3)))

(define-public r-monoreg
  (package
    (name "r-monoreg")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monoreg" version))
       (sha256
        (base32 "1ra6mnn9m3989h6pcrpfhv5bl4q5i6856w4wp6lmaxipr6swlnk2"))))
    (properties `((upstream-name . "monoreg")))
    (build-system r-build-system)
    (inputs (list gsl))
    (home-page "https://cran.r-project.org/package=monoreg")
    (synopsis
     "Bayesian Monotonic Regression Using a Marked Point Process Construction")
    (description
     "An extended version of the nonparametric Bayesian monotonic regression procedure
described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>,
allowing for multiple additive monotonic components in the linear predictor, and
time-to-event outcomes through case-base sampling.  The extension and its
applications, including estimation of absolute risks, are described in Saarela &
Arjas (2015) <DOI:10.1111/sjos.12125>.  The package also implements the
nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas
<DOI:10.1214/22-BA1310>.")
    (license license:gpl2+)))

(define-public r-monopoly
  (package
    (name "r-monopoly")
    (version "0.3-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MonoPoly" version))
       (sha256
        (base32 "0ls3sjfkinp9c5002ww0xm4l2galf20w2f45nm5irkk9sbqas0jq"))))
    (properties `((upstream-name . "MonoPoly")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=MonoPoly")
    (synopsis "Functions to Fit Monotone Polynomials")
    (description
     "This package provides functions for fitting monotone polynomials to data.
Detailed discussion of the methodologies used can be found in Murray, Mueller
and Turlach (2013) <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and
Turlach (2016) <doi:10.1080/00949655.2016.1139582>.")
    (license license:gpl2+)))

(define-public r-monophy
  (package
    (name "r-monophy")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MonoPhy" version))
       (sha256
        (base32 "131bvqz7gcdh8q4a670dbn20ms7hgykb1gmxb66qm4hi1mzi8hg1"))))
    (properties `((upstream-name . "MonoPhy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-phytools r-phangorn r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MonoPhy")
    (synopsis "Explore Monophyly of Taxonomic Groups in a Phylogeny")
    (description
     "Requires rooted phylogeny as input and creates a table of genera, their
monophyly-status, which taxa cause problems in monophyly etc.  Different
information can be extracted from the output and a plot function allows
visualization of the results in a number of ways. \"@code{MonoPhy}: a simple R
package to find and visualize monophyly issues.\" Schwery, O. & O'Meara, B.C.
(2016) <doi:10.7717/peerj-cs.56>.")
    (license license:gpl3)))

(define-public r-monomvn
  (package
    (name "r-monomvn")
    (version "1.9-21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monomvn" version))
       (sha256
        (base32 "1wjbxd0zzfpjlqin4f69f3vzjksjyafr3mbyi3hjkh64pv3kpgph"))))
    (properties `((upstream-name . "monomvn")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-pls r-mvtnorm r-mass r-lars))
    (native-inputs (list gfortran))
    (home-page "https://bobby.gramacy.com/r_packages/monomvn/")
    (synopsis
     "Estimation for MVN and Student-t Data with Monotone Missingness")
    (description
     "Estimation of multivariate normal (MVN) and student-t data of arbitrary
dimension where the pattern of missing data is monotone.  See Pantaleo and
Gramacy (2010) <doi:10.48550/@code{arXiv.0907.2135>}.  Through the use of
parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where
standard regressions fail, the package can handle a nearly arbitrary amount of
missing data.  The current version supports maximum likelihood inference and a
full Bayesian approach employing scale-mixtures for Gibbs sampling.  Monotone
data augmentation extends this Bayesian approach to arbitrary missingness
patterns.  A fully functional standalone interface to the Bayesian lasso (from
Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho,
Polson, & Scott), and ridge regression with model selection via Reversible Jump,
and student-t errors (from Geweke) is also provided.")
    (license license:lgpl2.0+)))

(define-public r-monolix2rx
  (package
    (name "r-monolix2rx")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monolix2rx" version))
       (sha256
        (base32 "03hwmyp0vgm6a1q8jiy57abwnk5bqnra97cy5sazxfza6f3lbdxc"))))
    (properties `((upstream-name . "monolix2rx")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-stringi
                             r-rxode2
                             r-rcpp
                             r-magrittr
                             r-lotri
                             r-ggplot2
                             r-ggforce
                             r-dparser
                             r-crayon
                             r-cli
                             r-checkmate))
    (home-page "https://nlmixr2.github.io/monolix2rx/")
    (synopsis "Converts 'Monolix' Models to 'rxode2'")
    (description
     "Monolix is a tool for running mixed effects model using saem'.  This tool allows
you to convert Monolix models to rxode2 (Wang, Hallow and James (2016)
<doi:10.1002/psp4.12052>) using the form compatible with nlmixr2 (Fidler et al
(2019) <doi:10.1002/psp4.12445>).  If available, the rxode2 model will read in
the Monolix data and compare the simulation for the population model individual
model and residual model to immediately show how well the translation is
performing.  This saves the model development time for people who are creating
an rxode2 model manually.  Additionally, this package reads in all the
information to allow simulation with uncertainty (that is the number of
observations, the number of subjects, and the covariance matrix) with a rxode2
model.  This is complementary to the babelmixr2 package that translates nlmixr2
models to Monolix and can convert the objects converted from monolix2rx to a
full nlmixr2 fit.  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 used to load its
model library instead manually setting up text files (which only works with old
versions of Monolix').")
    (license license:expat)))

(define-public r-monoinc
  (package
    (name "r-monoinc")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MonoInc" version))
       (sha256
        (base32 "14rykw3bfj0vznz0rw4vsg1k4vanfv9cy867vspw966ncl4bw70h"))))
    (properties `((upstream-name . "MonoInc")))
    (build-system r-build-system)
    (propagated-inputs (list r-sitar r-iterators r-foreach r-doparallel
                             r-compare))
    (home-page "https://cran.r-project.org/package=MonoInc")
    (synopsis "Monotonic Increasing")
    (description
     "Various imputation methods are utilized in this package, where one can flag and
impute non-monotonic data that is outside of a prespecified range.")
    (license license:gpl3)))

(define-public r-monographar
  (package
    (name "r-monographar")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monographaR" version))
       (sha256
        (base32 "0sqhz3zpbk57s2sm56csqr198fclk9x8zq3bfamzardpax8r2cjr"))))
    (properties `((upstream-name . "monographaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sp
                             r-shinywidgets
                             r-shinythemes
                             r-shinydashboard
                             r-shiny
                             r-sf
                             r-rpart
                             r-rnaturalearth
                             r-rmarkdown
                             r-raster
                             r-png
                             r-circular))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=monographaR")
    (synopsis "Taxonomic Monographs Tools")
    (description
     "This package contains functions intended to facilitate the production of plant
taxonomic monographs.  The package includes functions to convert tables into
taxonomic descriptions, lists of collectors, examined specimens, identification
keys (dichotomous and interactive), and can generate a monograph skeleton.
Additionally, wrapper functions to batch the production of phenology histograms
and distributional and diversity maps are also available.")
    (license license:gpl2+)))

(define-public r-monoclust
  (package
    (name "r-monoclust")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monoClust" version))
       (sha256
        (base32 "18zb4wfb09rpzzr9bc0nr71994nhgsk74s512dxlss50isvfqjqd"))))
    (properties `((upstream-name . "monoClust")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-permute
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://vinhtantran.github.io/monoClust/")
    (synopsis "Perform Monothetic Clustering with Extensions to Circular Data")
    (description
     "Implementation of the Monothetic Clustering algorithm (Chavent, 1998
<doi:10.1016/S0167-8655(98)00087-7>) on continuous data sets.  A lot of
extensions are included in the package, including applying Monothetic clustering
on data sets with circular variables, visualizations with the results, and
permutation and cross-validation based tests to support the decision on the
number of clusters.")
    (license license:gpl2+)))

(define-public r-monochromer
  (package
    (name "r-monochromer")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monochromeR" version))
       (sha256
        (base32 "013mmssczx6ii810vmb0x4k7miwzh4a5yzlr4skb5yzppxdy629s"))))
    (properties `((upstream-name . "monochromeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-ggplot2))
    (home-page "https://github.com/cararthompson/monochromeR")
    (synopsis "Easily Create, View and Use Monochrome Colour Palettes")
    (description
     "Generate a monochrome palette from a starting colour for a specified number of
colours.  The package can also be used to display colour palettes in the plot
window, with or without hex codes and colour labels.")
    (license license:expat)))

(define-public r-monobinshiny
  (package
    (name "r-monobinshiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monobinShiny" version))
       (sha256
        (base32 "1ix376j39fby5571irf8i5bmz8i7nnvfzbvs5cv0k42dcgq93m0g"))))
    (properties `((upstream-name . "monobinShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs
                             r-shinydashboard
                             r-shiny
                             r-monobin
                             r-dt
                             r-dplyr))
    (home-page "https://github.com/andrija-djurovic/monobinShiny")
    (synopsis "Shiny User Interface for 'monobin' Package")
    (description
     "This is an add-on package to the monobin package that simplifies its use.  It
provides shiny-based user interface (UI) that is especially handy for less
experienced R users as well as for those who intend to perform quick scanning of
numeric risk factors when building credit rating models.  The additional
functions implemented in @code{monobinShiny} that do no exist in monobin package
are: descriptive statistics, special case and outliers imputation.  The function
descriptive statistics is exported and can be used in R sessions independently
from the user interface, while special case and outlier imputation functions are
written to be used with shiny UI.")
    (license license:gpl3+)))

(define-public r-monobin
  (package
    (name "r-monobin")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monobin" version))
       (sha256
        (base32 "1060c7g2dz4cnvlfp2dnrl5fblvifmcskrgykqbfswpyfna1lw4a"))))
    (properties `((upstream-name . "monobin")))
    (build-system r-build-system)
    (propagated-inputs (list r-hmisc r-dplyr))
    (home-page "https://github.com/andrija-djurovic/monobin")
    (synopsis "Monotonic Binning for Credit Rating Models")
    (description
     "This package performs monotonic binning of numeric risk factor in credit rating
models (PD, LGD, EAD) development.  All functions handle both binary and
continuous target variable.  Functions that use isotonic regression in the first
stage of binning process have an additional feature for correction of minimum
percentage of observations and minimum target rate per bin.  Additionally,
monotonic trend can be identified based on raw data or, if known in advance,
forced by functions argument.  Missing values and other possible special values
are treated separately from so-called complete cases.")
    (license license:gpl3+)))

(define-public r-monmlp
  (package
    (name "r-monmlp")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monmlp" version))
       (sha256
        (base32 "19cx60csm8mnf6ksg64vlhi8wi1c7bmv9l55y9zmaljblr0kmzxf"))))
    (properties `((upstream-name . "monmlp")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimx))
    (home-page "https://cran.r-project.org/package=monmlp")
    (synopsis
     "Multi-Layer Perceptron Neural Network with Optional Monotonicity Constraints")
    (description
     "Train and make predictions from a multi-layer perceptron neural network with
optional partial monotonicity constraints.")
    (license license:gpl2)))

(define-public r-monitos
  (package
    (name "r-monitos")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monitOS" version))
       (sha256
        (base32 "0hdyxb1f2hy3fjbmjf60xnb0x61cga047g007jr9rpb5cimnpk7b"))))
    (properties `((upstream-name . "monitOS")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard r-shiny r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://opensource.nibr.com/monitOS/")
    (synopsis
     "Monitoring Overall Survival in Pivotal Trials in Indolent Cancers")
    (description
     "These guidelines are meant to provide a pragmatic, yet rigorous, help to drug
developers and decision makers, since they are shaped by three fundamental
ingredients: the clinically determined margin of detriment on OS that is
unacceptably high (delta null); the benefit on OS that is plausible given the
mechanism of action of the novel intervention (delta alt); and the quantity of
information (i.e.  survival events) it is feasible to accrue given the clinical
and drug development setting.  The proposed guidelines facilitate transparent
discussions between stakeholders focusing on the risks of erroneous decisions
and what might be an acceptable trade-off between power and the false positive
error rate.")
    (license license:gpl3+)))

(define-public r-monitor
  (package
    (name "r-monitor")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monitoR" version))
       (sha256
        (base32 "1h1g0m1j13mz37xxwck4s7a0bfqvi0nrzfw60j7bkzaah8zxcc9n"))))
    (properties `((upstream-name . "monitoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tuner))
    (native-inputs (list r-knitr))
    (home-page "http://www.uvm.edu/rsenr/vtcfwru/R/?Page=monitoR/monitoR.htm")
    (synopsis "Acoustic Template Detection in R")
    (description
     "Acoustic template detection and monitoring database interface.  Create, modify,
save, and use templates for detection of animal vocalizations.  View, verify,
and extract results.  Upload a @code{MySQL} schema to a existing instance,
manage survey metadata, write and read templates and detections locally or to
the database.")
    (license license:gpl2)))

(define-public r-mongopipe
  (package
    (name "r-mongopipe")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mongopipe" version))
       (sha256
        (base32 "081l6d1kwh30wq5kwz9dd2a9221x9139x1xbvfiqixwv0g9sjjy0"))))
    (properties `((upstream-name . "mongopipe")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-jsonlite))
    (home-page "https://rpkgs.gitlab.io/mongopipe")
    (synopsis "Query MongoDB Documents with R")
    (description "Translate R code into @code{MongoDB} aggregation pipelines.")
    (license license:expat)))

(define-public r-mongolite
  (package
    (name "r-mongolite")
    (version "2.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mongolite" version))
       (sha256
        (base32 "0745ahm8ckmw7r3a72h4kjsndgwbk7jwi7i0z0i8aww2k3z8v37z"))))
    (properties `((upstream-name . "mongolite")))
    (build-system r-build-system)
    (inputs (list zlib openssl openssl))
    (propagated-inputs (list r-openssl r-mime r-jsonlite))
    (native-inputs (list pkg-config))
    (home-page "https://jeroen.r-universe.dev/mongolite")
    (synopsis "Fast and Simple 'MongoDB' Client for R")
    (description
     "High-performance @code{MongoDB} client based on mongo-c-driver and jsonlite'.
Includes support for aggregation, indexing, map-reduce, streaming, encryption,
enterprise authentication, and @code{GridFS}.  The online user manual provides
an overview of the available methods in the package:
<https://jeroen.github.io/mongolite/>.")
    (license license:asl2.0)))

(define-public r-monetdb-r
  (package
    (name "r-monetdb-r")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MonetDB.R" version))
       (sha256
        (base32 "0b5flc9l4nshy2fmwxbzcsn7pdivwy3x6kic94ppyyiqzz41h0b1"))))
    (properties `((upstream-name . "MonetDB.R")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-testthat r-digest r-dbi r-codetools))
    (home-page "http://www.monetdb.org")
    (synopsis "Connect MonetDB to R")
    (description "Allows to pull data from @code{MonetDB} into R.")
    (license (license:fsdg-compatible "MPL (== 2.0)"))))

(define-public r-mondrian
  (package
    (name "r-mondrian")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mondrian" version))
       (sha256
        (base32 "0di35qc1hgx3l8gnr2l6gv87yhqsw9b7wi2wc497ydby6c9m3307"))))
    (properties `((upstream-name . "Mondrian")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer))
    (home-page "https://cran.r-project.org/package=Mondrian")
    (synopsis
     "Simple Graphical Representation of the Relative Occurrence and Co-Occurrence of Events")
    (description
     "The unique function of this package allows representing in a single graph the
relative occurrence and co-occurrence of events measured in a sample.  As
examples, the package was applied to describe the occurrence and co-occurrence
of different species of bacterial or viral symbionts infecting arthropods at the
individual level.  The graphics allows determining the prevalence of each
symbiont and the patterns of multiple infections (i.e.  how different symbionts
share or not the same individual hosts).  We named the package after the famous
painter as the graphical output recalls Mondrianâs paintings.")
    (license license:gpl2+)))

(define-public r-mondate
  (package
    (name "r-mondate")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mondate" version))
       (sha256
        (base32 "1spz3xv450mw0i984hjp6r4lk653nmjawf9cipv9z7isc4zv3ac3"))))
    (properties `((upstream-name . "mondate")))
    (build-system r-build-system)
    (home-page "https://www.R-project.org")
    (synopsis "Keep Track of Dates in Terms of Months")
    (description
     "Keep track of dates in terms of fractional calendar months per Damien Laker
\"Time Calculations for Annualizing Returns: the Need for Standardization\", The
Journal of Performance Measurement, 2008.  Model dates as of close of business.
Perform date arithmetic in units of \"months\" and \"years\".  Allow \"infinite\"
dates to model \"ultimate\" time.")
    (license (list (license:fsdg-compatible "MPL-2.0")
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-monashtipr
  (package
    (name "r-monashtipr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monashtipr" version))
       (sha256
        (base32 "050xa9ghhf5xy76vndry7rhqn3hn0lhz6wjwz9vxk6mcfy5j2wq5"))))
    (properties `((upstream-name . "monashtipr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-rvest
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-httr
                             r-glue))
    (home-page "https://jimmyday12.github.io/monash_tipr/")
    (synopsis
     "An R API Wrapper for the Monash University Probabilistic Footy Tipping Competition")
    (description
     "An API wrapper for the Monash University Probabilistic Footy Tipping Competition
<https://probabilistic-footy.monash.edu/~footy/index.shtml>.  Allows users to
submit tips directly to the competition from R.")
    (license license:expat)))

(define-public r-monan
  (package
    (name "r-monan")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MoNAn" version))
       (sha256
        (base32 "1swa9wmcnlf63sjv92vp0v24di809syp48d79pzanvwwhiflwh27"))))
    (properties `((upstream-name . "MoNAn")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall))
    (home-page "https://cran.r-project.org/package=MoNAn")
    (synopsis "Mobility Network Analysis")
    (description
     "This package implements the method to analyse weighted mobility networks or
distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G.
(2022) <doi:10.1016/j.socnet.2021.08.003>.  The purpose of the model is to
analyse the structure of mobility, incorporating exogenous predictors pertaining
to individuals and locations known from classical mobility analyses, as well as
modelling emergent mobility patterns akin to structural patterns known from the
statistical analysis of social networks.")
    (license license:gpl3+)))

(define-public r-monaco
  (package
    (name "r-monaco")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "monaco" version))
       (sha256
        (base32 "109m9rvy39bp7j3jj8y6qdr4dran0qqvcx267mvil9c8ipxr89ms"))))
    (properties `((upstream-name . "monaco")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-rstudioapi r-htmlwidgets r-htmltools))
    (native-inputs (list esbuild))
    (home-page "https://github.com/stla/monaco")
    (synopsis "The 'Monaco' Editor as a HTML Widget")
    (description
     "This package provides a HTML widget rendering the Monaco editor.  The Monaco
editor is the code editor which powers VS Code'.  It is particularly well
developed for @code{JavaScript}'.  In addition to the built-in features of the
Monaco editor, the widget allows to prettify multiple languages, to view the
HTML rendering of Markdown code, and to view and resize SVG images.")
    (license license:gpl3)))

(define-public r-momtrunc
  (package
    (name "r-momtrunc")
    (version "6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MomTrunc" version))
       (sha256
        (base32 "0nfvq6ry1c5g1xjvf2qyr5pzpnsy420m6xgj0n3aj00wi0ifdyiz"))))
    (properties `((upstream-name . "MomTrunc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tlrmvnmvt r-rcpparmadillo r-rcpp r-mvtnorm
                             r-hypergeo))
    (home-page "https://cran.r-project.org/package=MomTrunc")
    (synopsis
     "Moments of Folded and Doubly Truncated Multivariate Distributions")
    (description
     "It computes arbitrary products moments (mean vector and variance-covariance
matrix), for some double truncated (and folded) multivariate distributions.
These distributions belong to the family of selection elliptical distributions,
which includes well known skewed distributions as the unified skew-t
distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t
(ST) and the symmetric student-t (T) distribution.  Analogous normal cases
unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and
symmetric normal (N) are also included.  Density, probabilities and random
deviates are also offered for these members.")
    (license license:gpl2+)))

(define-public r-momocs
  (package
    (name "r-momocs")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Momocs" version))
       (sha256
        (base32 "0g8mxk62pdif47p96pzqzfjdjarqwyk71zw9avsgy160mz8mpd81"))))
    (properties `((upstream-name . "Momocs")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tibble
                             r-sp
                             r-sf
                             r-rcolorbrewer
                             r-progress
                             r-mass
                             r-magrittr
                             r-jpeg
                             r-ggplot2
                             r-geomorph
                             r-geometry
                             r-dplyr
                             r-dendextend
                             r-cluster))
    (home-page "https://github.com/MomX/Momocs/")
    (synopsis "Morphometrics using R")
    (description
     "The goal of Momocs is to provide a complete, convenient, reproducible and
open-source toolkit for 2D morphometrics.  It includes most common 2D
morphometrics approaches on outlines, open outlines, configurations of
landmarks, traditional morphometrics, and facilities for data preparation,
manipulation and visualization with a consistent grammar throughout.  It allows
reproducible, complex morphometrics analyses and other morphometrics approaches
should be easy to plug in, or develop from, on top of this canvas.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-momentuhmm
  (package
    (name "r-momentuhmm")
    (version "1.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "momentuHMM" version))
       (sha256
        (base32 "0isd18b0fdf75gk6lbm34hgrhwj1fnl7airpv56xffsdvhvj6xjq"))))
    (properties `((upstream-name . "momentuHMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-raster
                             r-numderiv
                             r-mvtnorm
                             r-mass
                             r-foreach
                             r-dorng
                             r-doparallel
                             r-crawl
                             r-circstats
                             r-brobdingnag))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/bmcclintock/momentuHMM")
    (synopsis
     "Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models")
    (description
     "Extended tools for analyzing telemetry data using generalized hidden Markov
models.  Features of @code{momentuHMM} (pronounced ``momentum'') include data
pre-processing and visualization, fitting HMMs to location and auxiliary
biotelemetry or environmental data, biased and correlated random walk movement
models, hierarchical HMMs, multiple imputation for incorporating location
measurement error and missing data, user-specified design matrices and
constraints for covariate modelling of parameters, random effects, decoding of
the state process, visualization of fitted models, model checking and selection,
and simulation.  See @code{McClintock} and Michelot (2018)
<doi:10.1111/2041-210X.12995>.")
    (license license:gpl3)))

(define-public r-momentfit
  (package
    (name "r-momentfit")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "momentfit" version))
       (sha256
        (base32 "0867yx1w8fxi7z4qqx6awihm726i0wgs4jd1dc6pj8zi8l2yjs2z"))))
    (properties `((upstream-name . "momentfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=momentfit")
    (synopsis "Methods of Moments")
    (description
     "Several classes for moment-based models are defined.  The classes are defined
for moment conditions derived from a single equation or a system of equations.
The conditions can also be expressed as functions or formulas.  Several methods
are also offered to facilitate the development of different estimation
techniques.  The methods that are currently provided are the Generalized method
of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and
systems of equation, and the Generalized Empirical Likelihood (Smith 1997;
<doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997;
<doi:10.1214/aos/1069362388>, Newey and Smith 2004;
<doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005
<doi:10.1111/j.1468-0262.2005.00601.x>).")
    (license license:gpl2+)))

(define-public r-momentchi2
  (package
    (name "r-momentchi2")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "momentchi2" version))
       (sha256
        (base32 "1fv0jmwws4mkl9dsaybfpj29ncvqx2idsijgwz2qjblcg309a6k9"))))
    (properties `((upstream-name . "momentchi2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=momentchi2")
    (synopsis
     "Moment-Matching Methods for Weighted Sums of Chi-Squared Random Variables")
    (description
     "This package provides a collection of moment-matching methods for computing the
cumulative distribution function of a positively-weighted sum of chi-squared
random variables.  Methods include the Satterthwaite-Welch method,
Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak
method.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mombf
  (package
    (name "r-mombf")
    (version "3.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mombf" version))
       (sha256
        (base32 "0pyq8jlx36075b77p5kalq5fn0y8f3s7l8crn3gvpjmp9shqv22y"))))
    (properties `((upstream-name . "mombf")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sparsematrixstats
                             r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-ncvreg
                             r-mvtnorm
                             r-mgcv
                             r-mclust
                             r-matrix
                             r-intervals
                             r-glmnet
                             r-glasso
                             r-dplyr))
    (home-page "https://github.com/davidrusi/mombf")
    (synopsis "Model Selection with Bayesian Methods and Information Criteria")
    (description
     "Model selection and averaging for regression and mixtures, inclusing Bayesian
model selection and information criteria (BIC, EBIC, AIC, GIC).")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-molhd
  (package
    (name "r-molhd")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOLHD" version))
       (sha256
        (base32 "1lrl24x7iig15jwxigvqm3jdwnxkn948sxmywn6zi2b1a5hqz05k"))))
    (properties `((upstream-name . "MOLHD")))
    (build-system r-build-system)
    (propagated-inputs (list r-fields r-arrangements))
    (home-page "https://cran.r-project.org/package=MOLHD")
    (synopsis "Multiple Objective Latin Hypercube Design")
    (description
     "Generate the optimal maximin distance, minimax distance (only for low
dimensions), and maximum projection designs within the class of Latin hypercube
designs efficiently for computer experiments.  Generate Pareto front optimal
designs for each two of the three criteria and all the three criteria within the
class of Latin hypercube designs efficiently.  Provide criterion computing
functions.  References of this package can be found in Morris, M. D. and
Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine
M. Anderson-@code{CookTimothy} J. Robinson (2011) <doi:10.1198/Tech.2011.10087>,
Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.")
    (license license:lgpl2.0+)))

(define-public r-molgenisauth
  (package
    (name "r-molgenisauth")
    (version "0.0.25")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MolgenisAuth" version))
       (sha256
        (base32 "1ggij166zy0z05hmpwzdx2s1j38s9zr2ldayxljmcl348ds7rk7n"))))
    (properties `((upstream-name . "MolgenisAuth")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools r-httr))
    (home-page "https://github.com/molgenis/molgenis-r-auth/")
    (synopsis "'OpenID Connect' Discovery and Authentication")
    (description
     "Discover @code{OpenID} Connect endpoints and authenticate using device flow.
Used by MOLGENIS packages.")
    (license license:gpl3)))

(define-public r-molgenisarmadillo
  (package
    (name "r-molgenisarmadillo")
    (version "2.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MolgenisArmadillo" version))
       (sha256
        (base32 "12js86ys9d80cf69qjl30q01i9a9nki500nq173hn4xym9sc2pcy"))))
    (properties `((upstream-name . "MolgenisArmadillo")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlist
                             r-readr
                             r-purrr
                             r-molgenisauth
                             r-httr2
                             r-httr
                             r-dplyr
                             r-cli
                             r-base64enc
                             r-arrow))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/molgenis/molgenis-r-armadillo/")
    (synopsis "Armadillo Client for the Armadillo Service")
    (description
     "This package provides a set of functions to manage data shared on a MOLGENIS
Armadillo server.")
    (license license:lgpl2.1+)))

(define-public r-mole
  (package
    (name "r-mole")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MoLE" version))
       (sha256
        (base32 "0bq19vwqjn5hl4mw5imkydbp39gal992vnh2dwrq8bb8cmj3qh91"))))
    (properties `((upstream-name . "MoLE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MoLE")
    (synopsis "Modeling Language Evolution")
    (description
     "Model for simulating language evolution in terms of cultural evolution (Smith &
Kirby (2008) <DOI:10.1098/rstb.2008.0145>; Deacon 1997).  The focus is on the
emergence of argument-marking systems (Dowty (1991) <DOI:10.1353/lan.1991.0021>,
Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e.  noun marking (Aristar (1997)
<DOI:10.1075/sl.21.2.04ari>, Lestrade (2010) <DOI:10.7282/T3ZG6R4S>), person
indexing (Ariel 1999, Dahl (2000) <DOI:10.1075/fol.7.1.03dah>, Bhat 2004), and
word order (Dryer 2013), but extensions are foreseen.  Agents start out with a
protolanguage (a language without grammar; Bickerton (1981)
<DOI:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015)
<DOI:10.1002/9781118346136.ch27>) and interact through language games (Steels
1997).  Over time, grammatical constructions emerge that may or may not become
obligatory (for which the tolerance principle is assumed; Yang 2016).
Throughout the simulation, uniformitarianism of principles is assumed (Hopper
(1987) <DOI:10.3765/bls.v13i0.1834>, Givon (1995) <DOI:10.1075/z.74>, Croft
(2000), Saffran (2001) <DOI:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in
which maximal psychological validity is aimed at (Grice (1975)
<DOI:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language
representation is usage based (Tomasello 2003, Bybee 2010).  In Lestrade (2015b)
<DOI:10.15496/publikation-8640>, Lestrade (2015c) <DOI:10.1075/avt.32.08les>,
and Lestrade (2016) <DOI:10.17617/2.2248195>), which reported on the results of
preliminary versions, this package was announced as WDWTW (for who does what to
whom), but for reasons of pronunciation and generalization the title was
changed.")
    (license license:gpl2)))

(define-public r-molar
  (package
    (name "r-molar")
    (version "5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "molaR" version))
       (sha256
        (base32 "01l68a4jsp0dlylnp9ij1db0q4510l38jlql787bi6ah07xwf8zq"))))
    (properties `((upstream-name . "molaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rvcg r-rgl r-pracma r-alphahull))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=molaR")
    (synopsis "Dental Surface Complexity Measurement Tools")
    (description
     "Surface topography calculations of Dirichlet's normal energy, relief index,
surface slope, and orientation patch count for teeth using scans of enamel caps.
 Importantly, for the relief index and orientation patch count calculations to
work, the scanned tooth files must be oriented with the occlusal plane parallel
to the x and y axes, and perpendicular to the z axis.  The files should also be
simplified, and smoothed in some other software prior to uploading into R.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mokken
  (package
    (name "r-mokken")
    (version "3.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mokken" version))
       (sha256
        (base32 "0wfm4yg9pzl5ng3mlng7p9qzvql51sxbs4gv006xanif5pkrn9dd"))))
    (properties `((upstream-name . "mokken")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-polca))
    (home-page
     "https://sites.google.com/a/tilburguniversity.edu/avdrark/mokken")
    (synopsis "Conducts Mokken Scale Analysis")
    (description
     "This package contains functions for performing Mokken scale analysis on test and
questionnaire data.  It includes an automated item selection algorithm, and
various checks of model assumptions.")
    (license license:gpl2+)))

(define-public r-mojson
  (package
    (name "r-mojson")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mojson" version))
       (sha256
        (base32 "077c92jhizpvg5gdwq1zb0d74m743l5qylj6df8hhgyvahswnhnq"))))
    (properties `((upstream-name . "mojson")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rjsonio
                             r-magrittr
                             r-iterators
                             r-comparedf))
    (home-page "https://github.com/chriswweibo/mojson")
    (synopsis "Serialization-Style Flattening and Description for JSON")
    (description
     "Support JSON flattening in a long data frame way, where the nesting keys will be
stored in the absolute path.  It also provides an easy way to summarize the
basic description of a JSON list.  The idea of mojson is to transform a JSON
object in an absolute serialization way, which means the early key-value pairs
will appear in the heading rows of the resultant data frame.  mojson also
provides an alternative way of comparing two different JSON lists, returning the
left/inner/right-join style results.")
    (license license:expat)))

(define-public r-mogavs
  (package
    (name "r-mogavs")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mogavs" version))
       (sha256
        (base32 "0lhmwdv991bv7j8k066jr7ii94w60ydjpf5pzmv8jlblf3w2pr02"))))
    (properties `((upstream-name . "mogavs")))
    (build-system r-build-system)
    (propagated-inputs (list r-cvtools))
    (home-page "https://cran.r-project.org/package=mogavs")
    (synopsis
     "Multiobjective Genetic Algorithm for Variable Selection in Regression")
    (description
     "This package provides functions for exploring the best subsets in regression
with a genetic algorithm.  The package is much faster than methods relying on
complete enumeration, and is suitable for data sets with large number of
variables.  For more information, see Sinha, Malo & Kuosmanen (2015)
<doi:10.1080/10618600.2014.899236>.")
    (license license:gpl2)))

(define-public r-mofat
  (package
    (name "r-mofat")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOFAT" version))
       (sha256
        (base32 "00b9ss777zr5ysh98yllyicqwgnzccmjf20g7w20zvn2blhs2np0"))))
    (properties `((upstream-name . "MOFAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-slhd))
    (home-page "https://cran.r-project.org/package=MOFAT")
    (synopsis "Maximum One-Factor-at-a-Time Designs")
    (description
     "Identifying important factors from a large number of potentially important
factors of a highly nonlinear and computationally expensive black box model is a
difficult problem.  Xiao, Joseph, and Ray (2022)
<doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time
(MOFAT) designs for doing this.  A MOFAT design can be viewed as an improvement
to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991)
<doi:10.1080/00401706.1991.10484804>.  The improvement is achieved by exploiting
the connection between Morris screening designs and Monte Carlo-based Sobol
designs, and optimizing the design using a space-filling criterion.  This work
is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646
<https://www.nsf.gov/awardsearch/@code{showAward?AWD_ID=1921646>}.")
    (license license:gpl2+)))

(define-public r-moexer
  (package
    (name "r-moexer")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moexer" version))
       (sha256
        (base32 "09jr2vhj00k1cv1a0ki68mkd3mhky3smxwkaxw2m2d0h8w34jsyi"))))
    (properties `((upstream-name . "moexer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-cli
                             r-assertthat))
    (home-page "https://github.com/x1o/moexer")
    (synopsis
     "Interact with Moscow Exchange Informational and Statistical Server ('ISS')")
    (description
     "This is a thin wrapper around the MOEX ISS REST interface, see
<https://iss.moex.com>.  It allows to quickly fetch price candles for a
particular security, obtain its profile information and so on.")
    (license license:gpl3)))

(define-public r-moeclust
  (package
    (name "r-moeclust")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MoEClust" version))
       (sha256
        (base32 "0wixcga2f06kpp7mq1nb32ya2v2wlx5r9h74danjfbhi3nvky903"))))
    (properties `((upstream-name . "MoEClust")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcd
                             r-nnet
                             r-mvnfast
                             r-mclust
                             r-matrixstats
                             r-lattice))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MoEClust")
    (synopsis
     "Gaussian Parsimonious Clustering Models with Covariates and a Noise Component")
    (description
     "Clustering via parsimonious Gaussian Mixtures of Experts using the
@code{MoEClust} models introduced by Murphy and Murphy (2020)
<doi:10.1007/s11634-019-00373-8>.  This package fits finite Gaussian mixture
models with a formula interface for supplying gating and/or expert network
covariates using a range of parsimonious covariance parameterisations from the
GPCM family via the EM/CEM algorithm.  Visualisation of the results of such
models using generalised pairs plots and the inclusion of an additional noise
component is also facilitated.  A greedy forward stepwise search algorithm is
provided for identifying the optimal model in terms of the number of components,
the GPCM covariance parameterisation, and the subsets of gating/expert network
covariates.")
    (license license:gpl3+)))

(define-public r-moeadr
  (package
    (name "r-moeadr")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOEADr" version))
       (sha256
        (base32 "19x7km2bpq0srm2lyafg3jcc4khwv077kjsqhhbgkv7r4c7xl6id"))))
    (properties `((upstream-name . "MOEADr")))
    (build-system r-build-system)
    (propagated-inputs (list r-fnn r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://fcampelo.github.io/MOEADr/")
    (synopsis "Component-Wise MOEA/D Implementation")
    (description
     "Modular implementation of Multiobjective Evolutionary Algorithms based on
Decomposition (MOEA/D) [Zhang and Li (2007), <DOI:10.1109/TEVC.2007.892759>] for
quick assembling and testing of new algorithmic components, as well as easy
replication of published MOEA/D proposals.  The full framework is documented in
a paper published in the Journal of Statistical Software
[<doi:10.18637/jss.v092.i06>].")
    (license license:gpl2)))

(define-public r-modules
  (package
    (name "r-modules")
    (version "0.13.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modules" version))
       (sha256
        (base32 "1z67nyjxcvz8hrmzbj2igvb43lgrn7s977r7wmfjdnr7cvr44fsm"))))
    (properties `((upstream-name . "modules")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wahani/modules")
    (synopsis "Self Contained Units of Source Code")
    (description
     "This package provides modules as an organizational unit for source code.
Modules enforce to be more rigorous when defining dependencies and have a local
search path.  They can be used as a sub unit within packages or in scripts.")
    (license license:expat)))

(define-public r-modulecolor
  (package
    (name "r-modulecolor")
    (version "1.8-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moduleColor" version))
       (sha256
        (base32 "0p45cc21vxjgjn91n0s09l20mf4a8vrkh1d9i27ndn5msrpwn15k"))))
    (properties `((upstream-name . "moduleColor")))
    (build-system r-build-system)
    (propagated-inputs (list r-impute r-dynamictreecut))
    (home-page
     "https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/BranchCutting/")
    (synopsis "Basic Module Functions")
    (description
     "This package provides methods for color labeling, calculation of eigengenes,
merging of closely related modules.")
    (license license:gpl2+)))

(define-public r-modturpoint
  (package
    (name "r-modturpoint")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modTurPoint" version))
       (sha256
        (base32 "1m24vsi80ln1r7sr40pipaaanyc60yfxmjzf8l7nfy3yvyr8zqpn"))))
    (properties `((upstream-name . "modTurPoint")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=modTurPoint")
    (synopsis "Estimate ED50 Based on Modified Turning Point Method")
    (description
     "Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453>
to estimate 50 percent effective dose (ED50) in the study of drug sensitivity.
The method has its own advantages for that it can provide robust ED50
estimation.  This package contains the modified function of Choi's turning point
method.")
    (license license:gpl3)))

(define-public r-modtools
  (package
    (name "r-modtools")
    (version "0.9.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ModTools" version))
       (sha256
        (base32 "0xp2kzfp1vqly18md52a0bhk43ds3b8yfi3bxj08vibcfg1jp78r"))))
    (properties `((upstream-name . "ModTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sandwich
                             r-rpart-plot
                             r-rpart
                             r-robustbase
                             r-relaimpo
                             r-randomforest
                             r-pscl
                             r-proc
                             r-nnet
                             r-neuralnettools
                             r-naivebayes
                             r-mass
                             r-lmtest
                             r-lattice
                             r-e1071
                             r-desctools
                             r-class
                             r-car
                             r-c50
                             r-boot
                             r-aer))
    (home-page "https://andrisignorell.github.io/ModTools/")
    (synopsis "Building Regression and Classification Models")
    (description
     "Consistent user interface to the most common regression and classification
algorithms, such as random forest, neural networks, C5 trees and support vector
machines, complemented with a handful of auxiliary functions, such as variable
importance and a tuning function for the parameters.")
    (license license:gpl2+)))

(define-public r-modstatr
  (package
    (name "r-modstatr")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ModStatR" version))
       (sha256
        (base32 "0rind0v0s9n1z9852hr2cc36c2jpkasrhkprqqvz1qvfjkhg4c6v"))))
    (properties `((upstream-name . "ModStatR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jmuoutlier r-hypergeo r-gsl r-ellipse r-boot))
    (home-page "https://fbertran.github.io/homepage/")
    (synopsis "Statistical Modelling in Action with R")
    (description
     "Datasets and functions for the book \"ModÃ©lisation statistique par la pratique
avec R\", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525,
Dunod, Paris).  The first chapter of the book is dedicated to an introduction to
the R statistical software.  The second chapter deals with correlation analysis:
Pearson, Spearman and Kendall simple, multiple and partial correlation
coefficients.  New wrapper functions for permutation tests or bootstrap of
matrices of correlation are provided with the package.  The third chapter is
dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and
clustering.  The fourth chapter is dedicated to regression analysis: fitting and
model diagnostics are detailed.  The exercises focus on covariance analysis,
logistic regression, Poisson regression, two-way analysis of variance for fixed
or random factors.  Various example datasets are shipped with the package: for
instance on pokemon, world of warcraft, house tasks or food nutrition analyses.")
    (license license:gpl3)))

(define-public r-modsem
  (package
    (name "r-modsem")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modsem" version))
       (sha256
        (base32 "1fh0nw5vckfpl3s6jjl87dc6yidv97x8q6l29qz3avlkgjhd1j6a"))))
    (properties `((upstream-name . "modsem")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-nlme
                             r-mvtnorm
                             r-mvnfast
                             r-mplusautomation
                             r-lavaan
                             r-ggplot2
                             r-fastghquad
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Kss2k/modsem")
    (synopsis
     "Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)")
    (description
     "Estimation of interaction (i.e., moderation) effects between latent variables in
structural equation models (SEM).  The supported methods are: The constrained
approach (Algina & Moulder, 2001).  The unconstrained approach (Marsh et al.,
2004).  The residual centering approach (Little et al., 2006).  The double
centering approach (Lin et al., 2010).  The latent moderated structural
equations (LMS) approach (Klein & Moosbrugger, 2000).  The quasi-maximum
likelihood (QML) approach (Klein & MuthÃ©n, 2007) (temporarily unavailable) The
constrained- unconstrained, residual- and double centering- approaches are
estimated via lavaan (Rosseel, 2012), whilst the LMS- and QML- approaches are
estimated via by modsem it self.  Alternatively model can be estimated via Mplus
(MuthÃ©n & MuthÃ©n, 1998-2017).  References: Algina, J., & Moulder, B. C.
(2001). <doi:10.1207/S15328007SEM0801_3>. \"A note on estimating the
JÃ¶reskog-Yang model for latent variable interaction using LISREL 8.3.\" Klein,
A., & Moosbrugger, H. (2000). <doi:10.1007/BF02296338>. \"Maximum likelihood
estimation of latent interaction effects with the LMS method.\" Klein, A. G., &
MuthÃ©n, B. O. (2007). <doi:10.1080/00273170701710205>. \"Quasi-maximum
likelihood estimation of structural equation models with multiple interaction
and quadratic effects.\" Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010).
<doi:10.1080/10705511.2010.488999>. \"Structural equation models of latent
interactions: Clarification of orthogonalizing and double-mean-centering
strategies.\" Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006).
<doi:10.1207/s15328007sem1304_1>. \"On the merits of orthogonalizing powered and
product terms: Implications for modeling interactions among latent variables.\"
Marsh, H. W., Wen, Z., & Hau, K. T. (2004). <doi:10.1037/1082-989X.9.3.275>.
\"Structural equation models of latent interactions: evaluation of alternative
estimation strategies and indicator construction.\" MuthÃ©n, L.K. and MuthÃ©n,
B.O. (1998-2017). \"'Mplus Userâs Guide.  Eighth Edition.\"
<https://www.statmodel.com/>.  Rosseel Y (2012). <doi:10.18637/jss.v048.i02>.
\"'lavaan': An R Package for Structural Equation Modeling.\".")
    (license license:expat)))

(define-public r-modqr
  (package
    (name "r-modqr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modQR" version))
       (sha256
        (base32 "15ks5i7s19r4s9faxn36qlcf4y150b6xji970kxgilcgxvfqkgd8"))))
    (properties `((upstream-name . "modQR")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve r-geometry))
    (home-page "https://cran.r-project.org/package=modQR")
    (synopsis "Multiple-Output Directional Quantile Regression")
    (description
     "This package contains basic tools for performing multiple-output quantile
regression and computing regression quantile contours by means of directional
regression quantiles.  In the location case, one can thus obtain halfspace depth
contours in two to six dimensions.  Hallin, M., Paindaveine, D. and Å iman, M.
(2010) Multivariate quantiles and multiple-output regression quantiles: from L1
optimization to halfspace depth.  Annals of Statistics 38, 635-669 For more
references about the method, see Help pages.")
    (license license:lgpl2.0)))

(define-public r-modopt-matlab
  (package
    (name "r-modopt-matlab")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modopt.matlab" version))
       (sha256
        (base32 "0g5mswalv4y7p1k3n8a6qf7wb072m4bjq2ml2j83dz2j2bmvygsd"))))
    (properties `((upstream-name . "modopt.matlab")))
    (build-system r-build-system)
    (propagated-inputs (list r-roi-plugin-quadprog r-roi-plugin-glpk r-roi))
    (home-page "http://www.finance-r.com/")
    (synopsis "'MatLab'-Style Modeling of Optimization Problems")
    (description
     "@code{MatLab'-Style} Modeling of Optimization Problems with R'.  This package
provides a set of convenience functions to transform a @code{MatLab'-style}
optimization modeling structure to its ROI equivalent.")
    (license license:expat)))

(define-public r-modnets
  (package
    (name "r-modnets")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modnets" version))
       (sha256
        (base32 "01mh7sbgq3796h6ciih5xww8hm0xn8975jv0784yw15hxhzfchci"))))
    (properties `((upstream-name . "modnets")))
    (build-system r-build-system)
    (propagated-inputs (list r-systemfit
                             r-reshape2
                             r-qgraph
                             r-psych
                             r-plyr
                             r-pbapply
                             r-mvtnorm
                             r-matrix
                             r-lmertest
                             r-lme4
                             r-leaps
                             r-interactiontest
                             r-igraph
                             r-gtools
                             r-gridextra
                             r-glmnet
                             r-glinternet
                             r-ggplot2
                             r-corpcor
                             r-abind))
    (home-page "https://github.com/tswanson222/modnets")
    (synopsis "Modeling Moderated Networks")
    (description
     "This package provides methods for modeling moderator variables in
cross-sectional, temporal, and multi-level networks.  Includes model selection
techniques and a variety of plotting functions.  Implements the methods
described by Swanson (2020)
<https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.")
    (license license:gpl3+)))

(define-public r-modmax
  (package
    (name "r-modmax")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modMax" version))
       (sha256
        (base32 "1mx4623az7vzaqf530pklx7j92qwwq93pa2416lnr24jjcxgva2h"))))
    (properties `((upstream-name . "modMax")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-gtools))
    (home-page "https://cran.r-project.org/package=modMax")
    (synopsis "Community Structure Detection via Modularity Maximization")
    (description
     "The algorithms implemented here are used to detect the community structure of a
network.  These algorithms follow different approaches, but are all based on the
concept of modularity maximization.")
    (license license:gpl2)))

(define-public r-modmarg
  (package
    (name "r-modmarg")
    (version "0.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modmarg" version))
       (sha256
        (base32 "1l57sqhv1xm5g3z9rdlhkkq6gpbn7a3z8bk4dhvzm44xcfv8hi3i"))))
    (properties `((upstream-name . "modmarg")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/anniejw6/modmarg")
    (synopsis "Calculating Marginal Effects and Levels with Errors")
    (description
     "Calculate predicted levels and marginal effects, using the delta method to
calculate standard errors.  This is an R-based version of the margins command
from Stata.")
    (license license:gpl3)))

(define-public r-modistools
  (package
    (name "r-modistools")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MODISTools" version))
       (sha256
        (base32 "1qxkipbrxqfs6wmg8q9ycwzwmp5qg3g2iv55w6cy492lhri3fsqa"))))
    (properties `((upstream-name . "MODISTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sp
                             r-sf
                             r-memoise
                             r-jsonlite
                             r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bluegreen-labs/MODISTools")
    (synopsis "Interface to the 'MODIS Land Products Subsets' Web Services")
    (description
     "Programmatic interface to the Oak Ridge National Laboratories MODIS Land
Products Subsets web services
(<https://modis.ornl.gov/data/modis_webservice.html>).  Allows for easy
downloads of MODIS time series directly to your R workspace or your computer.")
    (license license:agpl3)))

(define-public r-modisfast
  (package
    (name "r-modisfast")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modisfast" version))
       (sha256
        (base32 "1h3r5iwqrjzvfavsmvr6i5x2gvl76g44d712gnir9jl8sdhk0ynr"))))
    (properties `((upstream-name . "modisfast")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-terra
                             r-stringr
                             r-sf
                             r-rvest
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-dplyr
                             r-curl))
    (home-page "https://github.com/ptaconet/modisfast")
    (synopsis "Fast and Efficient Access to MODIS Earth Observation Data")
    (description
     "Programmatic interface to several NASA Earth Observation O@code{PeNDAP} servers
(Open-source Project for a Network Data Access Protocol)
(<https://www.opendap.org/>).  Allows for easy downloads of MODIS subsets, as
well as other Earth Observation datacubes, in a time-saving and efficient way :
by sampling it at the very downloading phase (spatially, temporally and
dimensionally).")
    (license license:gpl3+)))

(define-public r-modifiedmk
  (package
    (name "r-modifiedmk")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modifiedmk" version))
       (sha256
        (base32 "1m1c36vyzyl0v0gfy9fqlprvs3pvanj20dn56kxpniykdylscn3w"))))
    (properties `((upstream-name . "modifiedmk")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=modifiedmk")
    (synopsis
     "Modified Versions of Mann Kendall and Spearman's Rho Trend Tests")
    (description
     "Power of non-parametric Mann-Kendall test and Spearmanâs Rho test is highly
influenced by serially correlated data.  To address this issue, trend tests may
be applied on the modified versions of the time series data by Block
Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias
Corrected Prewhitening and Variance Correction Approach by calculating effective
sample size.  Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>.  Kendall,
M. (1975).  Multivariate analysis.  Charles Griffin&Company Ltd,.  sen, P. K.
(1968).<doi:10.2307/2285891>.  ÃnÃ¶z, B., & Bayazit, M. (2012)
<doi:10.1002/hyp.8438>.  Hamed, K. H.
(2009).<doi:10.1016/j.jhydrol.2009.01.040>.  Yue, S., & Wang, C. Y. (2002)
<doi:10.1029/2001WR000861>.  Yue, S., Pilon, P., Phinney, B., & Cavadias, G.
(2002) <doi:10.1002/hyp.1095>.  Hamed, K. H., & Ramachandra Rao, A. (1998)
<doi:10.1016/S0022-1694(97)00125-X>.  Yue, S., & Wang, C. Y. (2004)
<doi:10.1023/B:WARM.0000043140.61082.60>.")
    (license license:agpl3)))

(define-public r-modi
  (package
    (name "r-modi")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modi" version))
       (sha256
        (base32 "1rs80j6f2aq202mpls2dxwifiaipqphi1hsydnicfxl0m8qmcnvc"))))
    (properties `((upstream-name . "modi")))
    (build-system r-build-system)
    (propagated-inputs (list r-norm r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/martinSter/modi")
    (synopsis
     "Multivariate Outlier Detection and Imputation for Incomplete Survey Data")
    (description
     "Algorithms for multivariate outlier detection when missing values occur.
Algorithms are based on Mahalanobis distance or data depth.  Imputation is based
on the multivariate normal model or uses nearest neighbour donors.  The
algorithms take sample designs, in particular weighting, into account.  The
methods are described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.")
    (license license:expat)))

(define-public r-modgo
  (package
    (name "r-modgo")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modgo" version))
       (sha256
        (base32 "03c48akfdmnr47i88pzix7kz11nyrmppb94grjvy98yp4c1phnl9"))))
    (properties `((upstream-name . "modgo")))
    (build-system r-build-system)
    (propagated-inputs (list r-wesanderson
                             r-survival
                             r-psych
                             r-patchwork
                             r-matrix
                             r-mass
                             r-gridextra
                             r-gp
                             r-gldex
                             r-ggplot2
                             r-ggcorrplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=modgo")
    (synopsis "Mock Data Generation")
    (description
     "Generation of synthetic data from a real dataset using the combination of rank
normal inverse transformation with the calculation of correlation matrix
<doi:10.1055/a-2048-7692>.  Completely artificial data may be generated through
the use of Generalized Lambda Distribution and Generalized Poisson Distribution
<doi:10.1201/9781420038040>.  Quantitative, binary, ordinal categorical, and
survival data may be simulated.  Functionalities are offered to generate
synthetic data sets according to user's needs.")
    (license license:gpl3)))

(define-public r-modgetxl
  (package
    (name "r-modgetxl")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modgetxl" version))
       (sha256
        (base32 "0j9fmhy3wra9qr8753a495ligfz5fwgydv3ixlfzx3sgbq882hic"))))
    (properties `((upstream-name . "modgetxl")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard r-shiny r-readxl r-dt))
    (home-page "https://cran.r-project.org/package=modgetxl")
    (synopsis "'shiny' Module for Reading Excel Sheets")
    (description
     "This is a shiny module that presents a file picker user interface to get an
Excel file name, and reads the Excel sheets using readxl package and returns the
resulting sheet(s) as a vector and data in dataframe(s).")
    (license license:expat)))

(define-public r-modeva
  (package
    (name "r-modeva")
    (version "3.18.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modEvA" version))
       (sha256
        (base32 "176m1r3ml30iqsk4mrhsi994ymnpqs2yk1pg0z6lk6vhxgnjrzn1"))))
    (properties `((upstream-name . "modEvA")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra))
    (home-page "http://modeva.r-forge.r-project.org/")
    (synopsis "Model Evaluation and Analysis")
    (description
     "Analyses species distribution models and evaluates their performance.  It
includes functions for variation partitioning, extracting variable importance,
computing several metrics of model discrimination and calibration performance,
optimizing prediction thresholds based on a number of criteria, performing
multivariate environmental similarity surface (MESS) analysis, and displaying
various analytical plots.  Initially described in Barbosa et al. (2013)
<doi:10.1111/ddi.12100>.")
    (license license:gpl3)))

(define-public r-modesto
  (package
    (name "r-modesto")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modesto" version))
       (sha256
        (base32 "12p5azbbng1hnvs5b86g85871dn5534iv7r6fw5v1iv1h1rcypcr"))))
    (properties `((upstream-name . "modesto")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-markovchain))
    (home-page "https://cran.r-project.org/package=modesto")
    (synopsis "Modeling and Analysis of Stochastic Systems")
    (description
     "Compute important quantities when we consider stochastic systems that are
observed continuously.  Such as, Cost model, Limiting distribution, Transition
matrix, Transition distribution and Occupancy matrix.  The methods are
described, for example, Ross S. (2014), Introduction to Probability Models.
Eleven Edition.  Academic Press.")
    (license license:gpl3)))

(define-public r-modestm
  (package
    (name "r-modestm")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ModEstM" version))
       (sha256
        (base32 "136g7rdphprx5b2xmb4jnixaam23g78m3fyfwz3mfx1w23ajf4r1"))))
    (properties `((upstream-name . "ModEstM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-dplyr))
    (home-page "https://cran.r-project.org/package=ModEstM")
    (synopsis "Mode Estimation, Even in the Multimodal Case")
    (description
     "Function @code{ModEstM}() is the only one of this package, it estimates the
modes of an empirical univariate distribution.  It relies on the
stats::density() function, even for input control.  Due to very good performance
of the density estimation, computation time is not an issue.  The multiple modes
are handled using dplyr::group_by().  For conditions and rates of convergences,
see Eddy (1980) <doi:10.1214/aos/1176345080>.")
    (license license:gpl3)))

(define-public r-modest
  (package
    (name "r-modest")
    (version "0.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modest" version))
       (sha256
        (base32 "07rs014hdcabp2n0gg14pz2gmqgbw38vjv7a80vkzzh3601hxqws"))))
    (properties `((upstream-name . "modest")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinybs r-shiny r-rhandsontable r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=modest")
    (synopsis "Model-Based Dose-Escalation Trials")
    (description
     "User-friendly Shiny apps for designing and evaluating phase I cancer clinical
trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel
drug, using a Bayesian decision procedure based on logistic regression.")
    (license license:gpl2)))

(define-public r-modernva
  (package
    (name "r-modernva")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modernVA" version))
       (sha256
        (base32 "0hk98m83gpy12yffj4hl3qjl6fc75hsmw8j2577w4j073aml8x6p"))))
    (properties `((upstream-name . "modernVA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=modernVA")
    (synopsis
     "An Implementation of Two Modern Education-Based Value-Added Models")
    (description
     "This package provides functions that fit two modern education-based value-added
models.  One of these models is the quantile value-added model.  This model
permits estimating a school's value-added based on specific quantiles of the
post-test distribution.  Estimating value-added based on quantiles of the
post-test distribution provides a more complete picture of an education
institution's contribution to learning for students of all abilities.  See Page,
G.L.; San MartÃ­n, E.; Orellana, J.; Gonzalez, J. (2017)
<doi:10.1111/rssa.12195> for more details.  The second model is a temporally
dependent value-added model.  This model takes into account the temporal
dependence that may exist in school performance between two cohorts in one of
two ways.  The first is by modeling school random effects with a non-stationary
AR(1) process.  The second is by modeling school effects based on previous
cohort's post-test performance.  In addition to more efficiently estimating
value-added, this model permits making statements about the persistence of a
schools effectiveness.  The standard value-added model is also an option.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-moderndive
  (package
    (name "r-moderndive")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moderndive" version))
       (sha256
        (base32 "0pvmqkgxnjj5sw8bj0m7d6k9imcyrfndn3g09qwk83k8jsbwg85d"))))
    (properties `((upstream-name . "moderndive")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-knitr
                             r-janitor
                             r-infer
                             r-glue
                             r-ggplot2
                             r-formula-tools
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://moderndive.github.io/moderndive/")
    (synopsis "Tidyverse-Friendly Introductory Linear Regression")
    (description
     "Datasets and wrapper functions for tidyverse-friendly introductory linear
regression, used in \"Statistical Inference via Data Science: A @code{ModernDive}
into R and the Tidyverse\" available at <https://moderndive.com/>.")
    (license license:gpl3)))

(define-public r-moderate-mediation
  (package
    (name "r-moderate-mediation")
    (version "0.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moderate.mediation" version))
       (sha256
        (base32 "1xpyjrp5mxlca9naiz7npa2gqqx9py74w8k4c81djwjxqgn93g60"))))
    (properties `((upstream-name . "moderate.mediation")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-reshape2
                             r-mvtnorm
                             r-ggplot2
                             r-foreach
                             r-earth
                             r-dosnow
                             r-distr
                             r-cowplot))
    (home-page "https://cran.r-project.org/package=moderate.mediation")
    (synopsis "Causal Moderated Mediation Analysis")
    (description
     "Causal moderated mediation analysis using the methods proposed by Qin and Wang
(2023) <doi:10.3758/s13428-023-02095-4>.  Causal moderated mediation analysis is
crucial for investigating how, for whom, and where a treatment is effective by
assessing the heterogeneity of mediation mechanism across individuals and
contexts.  This package enables researchers to estimate and test the conditional
and moderated mediation effects, assess their sensitivity to unmeasured
pre-treatment confounding, and visualize the results.  The package is built
based on the quasi-Bayesian Monte Carlo method, because it has relatively better
performance at small sample sizes, and its running speed is the fastest.  The
package is applicable to a treatment of any scale, a binary or continuous
mediator, a binary or continuous outcome, and one or more moderators of any
scale.")
    (license license:gpl2)))

(define-public r-moder
  (package
    (name "r-moder")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moder" version))
       (sha256
        (base32 "19lz6jl9lhzwafmxx8yp7sx5lkz2xixk2yl5ys5l82am402d1wwb"))))
    (properties `((upstream-name . "moder")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lhdjung/moder")
    (synopsis "Mode Estimation")
    (description
     "Determines single or multiple modes (most frequent values).  Checks if missing
values make this impossible, and returns NA in this case.  Dependency-free
source code.  See Franzese and Iuliano (2019)
<doi:10.1016/B978-0-12-809633-8.20354-3>.")
    (license license:expat)))

(define-public r-modelwordcloud
  (package
    (name "r-modelwordcloud")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelwordcloud" version))
       (sha256
        (base32 "0ardib0h923i7jk8bgcq6pn2zazx9acf9sdggifsk46hdz8hvqnm"))))
    (properties `((upstream-name . "modelwordcloud")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=modelwordcloud")
    (synopsis "Model Word Clouds")
    (description
     "Makes a word cloud of text, sized by the frequency of the word, and colored
either by user-specified colors or colored by the strength of the coefficient of
that text derived from a regression model.")
    (license license:lgpl2.1)))

(define-public r-modeltime-resample
  (package
    (name "r-modeltime-resample")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modeltime.resample" version))
       (sha256
        (base32 "0prc4q5s6iwhknxlbaq43cyhmzwnmi52akpaf1zizw4c6flgny3v"))))
    (properties `((upstream-name . "modeltime.resample")))
    (build-system r-build-system)
    (propagated-inputs (list r-yardstick
                             r-workflows
                             r-tune
                             r-timetk
                             r-tidyr
                             r-tictoc
                             r-tibble
                             r-stringr
                             r-rsample
                             r-rlang
                             r-recipes
                             r-purrr
                             r-progressr
                             r-plotly
                             r-parsnip
                             r-modeltime
                             r-magrittr
                             r-hardhat
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-dials
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/business-science/modeltime.resample")
    (synopsis "Resampling Tools for Time Series Forecasting")
    (description
     "This package provides a modeltime extension that implements forecast resampling
tools that assess time-based model performance and stability for a single time
series, panel data, and cross-sectional time series analysis.")
    (license license:expat)))

(define-public r-modeltime-ensemble
  (package
    (name "r-modeltime-ensemble")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modeltime.ensemble" version))
       (sha256
        (base32 "1262p4lp4vr3d6s5x5cwrbib5vfr9apl4fvyhxzpvvc1vck5a94p"))))
    (properties `((upstream-name . "modeltime.ensemble")))
    (build-system r-build-system)
    (propagated-inputs (list r-yardstick
                             r-workflows
                             r-tune
                             r-timetk
                             r-tidyr
                             r-tictoc
                             r-tibble
                             r-stringr
                             r-rsample
                             r-rlang
                             r-recipes
                             r-purrr
                             r-modeltime-resample
                             r-modeltime
                             r-magrittr
                             r-glmnet
                             r-generics
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://business-science.github.io/modeltime.ensemble/")
    (synopsis "Ensemble Algorithms for Time Series Forecasting with Modeltime")
    (description
     "This package provides a modeltime extension that implements time series ensemble
forecasting methods including model averaging, weighted averaging, and stacking.
 These techniques are popular methods to improve forecast accuracy and
stability.")
    (license license:expat)))

(define-public r-modeltime
  (package
    (name "r-modeltime")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modeltime" version))
       (sha256
        (base32 "035y6qcz4jkgf1k297ykyglp82303a69iswymx8c0qmcvw4x66nc"))))
    (properties `((upstream-name . "modeltime")))
    (build-system r-build-system)
    (propagated-inputs (list r-yardstick
                             r-xgboost
                             r-workflows
                             r-timetk
                             r-tidyr
                             r-tidymodels
                             r-tibble
                             r-stringr
                             r-stanheaders
                             r-scales
                             r-rlang
                             r-reactable
                             r-purrr
                             r-prophet
                             r-plotly
                             r-parsnip
                             r-parallelly
                             r-magrittr
                             r-janitor
                             r-hardhat
                             r-gt
                             r-glue
                             r-ggplot2
                             r-forecast
                             r-foreach
                             r-forcats
                             r-dplyr
                             r-doparallel
                             r-dials
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/business-science/modeltime")
    (synopsis "The Tidymodels Extension for Time Series Modeling")
    (description
     "The time series forecasting framework for use with the tidymodels ecosystem.
Models include ARIMA, Exponential Smoothing, and additional time series models
from the forecast and prophet packages.  Refer to \"Forecasting Principles &
Practice, Second edition\" (<https://otexts.com/fpp2/>).  Refer to \"Prophet:
forecasting at scale\"
(<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).")
    (license license:expat)))

(define-public r-modeltests
  (package
    (name "r-modeltests")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modeltests" version))
       (sha256
        (base32 "112pwkrg3d1lmcqhs946nm4dxhp9h7gvjibvhcjzlmd1d0qhlivc"))))
    (properties `((upstream-name . "modeltests")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-testthat r-purrr r-generics r-dplyr))
    (home-page "https://github.com/alexpghayes/modeltests")
    (synopsis "Testing Infrastructure for Broom Model Generics")
    (description
     "This package provides a number of testthat tests that can be used to verify that
tidy(), glance() and augment() methods meet consistent specifications.  This
allows methods for the same generic to be spread across multiple packages, since
all of those packages can make the same guarantees to users about returned
objects.")
    (license license:expat)))

(define-public r-modelsummary
  (package
    (name "r-modelsummary")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelsummary" version))
       (sha256
        (base32 "185nnazkvqfnhiafzp0kdgrclsqvkpwa477ckfz0np32v9sykzqn"))))
    (properties `((upstream-name . "modelsummary")))
    (build-system r-build-system)
    (propagated-inputs (list r-tinytable
                             r-tables
                             r-performance
                             r-parameters
                             r-insight
                             r-glue
                             r-generics
                             r-data-table
                             r-checkmate))
    (home-page "https://modelsummary.com")
    (synopsis
     "Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready")
    (description
     "Create beautiful and customizable tables to summarize several statistical models
side-by-side.  Draw coefficient plots, multi-level cross-tabs, dataset
summaries, balance tables (a.k.a. \"Table 1s\"), and correlation matrices.  This
package supports dozens of statistical models, and it can produce tables in
HTML, @code{LaTeX}, Word, Markdown, PDF, @code{PowerPoint}, Excel, RTF, JPG, or
PNG. Tables can easily be embedded in Rmarkdown or knitr dynamic documents.
Details can be found in Arel-Bundock (2022) <doi:10.18637/jss.v103.i01>.")
    (license license:gpl3)))

(define-public r-modelstudio
  (package
    (name "r-modelstudio")
    (version "3.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelStudio" version))
       (sha256
        (base32 "10d1z0l5wfbyl2dm4n705bmj8wa5a99jnp4w6z66bx7cqamxapa3"))))
    (properties `((upstream-name . "modelStudio")))
    (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-r2d3
                             r-progress
                             r-jsonlite
                             r-ingredients
                             r-ibreakdown
                             r-digest
                             r-dalex))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://modelstudio.drwhy.ai")
    (synopsis "Interactive Studio for Explanatory Model Analysis")
    (description
     "Automate the explanatory analysis of machine learning predictive models.
Generate advanced interactive model explanations in the form of a serverless
HTML site with only one line of code.  This tool is model-agnostic, therefore
compatible with most of the black-box predictive models and frameworks.  The
main function computes various (instance and model-level) explanations and
produces a customisable dashboard, which consists of multiple panels for plots
with their short descriptions.  It is possible to easily save the dashboard and
share it with others. @code{modelStudio} facilitates the process of Interactive
Explanatory Model Analysis introduced in Baniecki et al. (2023)
<doi:10.1007/s10618-023-00924-w>.")
    (license license:gpl3)))

(define-public r-modelsse
  (package
    (name "r-modelsse")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelSSE" version))
       (sha256
        (base32 "0fqxwdsvfpzd182pwwib2ci6q76hzlw5ykkwzk20l5hglhdf057q"))))
    (properties `((upstream-name . "modelSSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-delaporte))
    (home-page "https://cran.r-project.org/package=modelSSE")
    (synopsis
     "Modelling Infectious Disease Superspreading from Contact Tracing Data")
    (description
     "Comprehensive analytical tools are provided to characterize infectious disease
superspreading from contact tracing surveillance data.  The underlying
theoretical frameworks of this toolkit include branching process with
transmission heterogeneity (Lloyd-Smith et al. (2005)
<doi:10.1038/nature04153>), case cluster size distribution (Nishiura et al.
(2012) <doi:10.1016/j.jtbi.2011.10.039>, Blumberg et al. (2014)
<doi:10.1371/journal.ppat.1004452>, and Kucharski and Althaus (2015)
<doi:10.2807/1560-7917.ES2015.20.25.21167>), and decomposition of reproduction
number (Zhao et al. (2022) <doi:10.1371/journal.pcbi.1010281>).")
    (license license:gpl3)))

(define-public r-modelobj
  (package
    (name "r-modelobj")
    (version "4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelObj" version))
       (sha256
        (base32 "0qj8ri7aw9lc7cc7rcs618bcbarcs29zgbz8l74s55qajvwpssg0"))))
    (properties `((upstream-name . "modelObj")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=modelObj")
    (synopsis "Model Object Framework for Regression Analysis")
    (description
     "This package provides a utility library to facilitate the generalization of
statistical methods built on a regression framework.  Package developers can use
@code{modelObj} methods to initiate a regression analysis without concern for
the details of the regression model and the method to be used to obtain
parameter estimates.  The specifics of the regression step are left to the user
to define when calling the function.  The user of a function developed within
the @code{modelObj} framework creates as input a @code{modelObj} that contains
the model and the R methods to be used to obtain parameter estimates and to
obtain predictions.  In this way, a user can easily go from linear to non-linear
models within the same package.")
    (license license:gpl2)))

(define-public r-modelmatrixmodel
  (package
    (name "r-modelmatrixmodel")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ModelMatrixModel" version))
       (sha256
        (base32 "0w8j32b2yw7x8lr1a9bgs4ylxfsxw8m06q7xpgsiz6sjicspc714"))))
    (properties `((upstream-name . "ModelMatrixModel")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ModelMatrixModel")
    (synopsis "Create Model Matrix and Save the Transforming Parameters")
    (description
     "The model.matrix() function in R is convenient for transforming training dataset
for modeling.  But it does not save any parameter used in transformation, so it
is hard to apply the same transformation to test dataset or new dataset.  This
package is created to solve the problem.")
    (license license:gpl3)))

(define-public r-modelmap
  (package
    (name "r-modelmap")
    (version "3.4.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ModelMap" version))
       (sha256
        (base32 "022isqdfvrfblmw5lh0wqmyknzm11sdg011brdkwv3rc8xym3gaa"))))
    (properties `((upstream-name . "ModelMap")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster
                             r-randomforest
                             r-presenceabsence
                             r-mgcv
                             r-handtill2001
                             r-fields
                             r-corrplot))
    (home-page "https://cran.r-project.org/package=ModelMap")
    (synopsis
     "Modeling and Map Production using Random Forest and Related Stochastic Models")
    (description
     "This package creates sophisticated models of training data and validates the
models with an independent test set, cross validation, or Out Of Bag (OOB)
predictions on the training data.  Create graphs and tables of the model
validation results.  Applies these models to GIS .img files of predictors to
create detailed prediction surfaces.  Handles large predictor files for map
making, by reading in the .img files in chunks, and output to the .txt file the
prediction for each data chunk, before reading the next chunk of data.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-modelltest
  (package
    (name "r-modelltest")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modeLLtest" version))
       (sha256
        (base32 "0m9djx3d2k0y38h0wyscwvzj0pvlfjqz2ar7j1rb29mhm7y8np70"))))
    (properties `((upstream-name . "modeLLtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcpparmadillo
                             r-rcpp
                             r-quantreg
                             r-mass
                             r-coxrobust))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ShanaScogin/modeLLtest")
    (synopsis "Compare Models with Cross-Validated Log-Likelihood")
    (description
     "An implementation of the cross-validated difference in means (CVDM) test by
Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and
Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median
fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>.  These
tests use leave-one-out cross-validated log-likelihoods to assist in selecting
among model estimations.  You can also utilize data from Golder (2010)
<doi:10.1177/0010414009341714> and Joshi & Mason (2008)
<doi:10.1177/0022343308096155> that are included to facilitate examples from
real-world analysis.")
    (license license:gpl3)))

(define-public r-modelimpact
  (package
    (name "r-modelimpact")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelimpact" version))
       (sha256
        (base32 "16sy7r6bw5nzx77rd27ihgvp7bs39xchgid94ssklj6syc3kn2vq"))))
    (properties `((upstream-name . "modelimpact")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-dplyr))
    (home-page "https://github.com/PeerChristensen/modelimpact")
    (synopsis
     "Functions to Assess the Business Impact of Churn Prediction Models")
    (description
     "Calculate the financial impact of using a churn model in terms of cost, revenue,
profit and return on investment.")
    (license license:expat)))

(define-public r-modelgrid
  (package
    (name "r-modelgrid")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelgrid" version))
       (sha256
        (base32 "0i8sdyp5ngpwzjcqyk4galnn42vzqspndffj7kcimikvrk30b4d7"))))
    (properties `((upstream-name . "modelgrid")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-magrittr r-dplyr r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/smaakage85/modelgrid")
    (synopsis
     "Framework for Creating, Managing and Training Multiple 'caret' Models")
    (description
     "This package provides a minimalistic but flexible framework that facilitates the
creation, management and training of multiple caret models.  A model grid
consists of two components, (1) a set of settings that is shared by all models
by default, and (2) specifications that apply only to the individual models.
When the model grid is trained, model and training specifications are first
consolidated from the shared and the model specific settings into complete caret
model configurations.  These models are then trained with the train() function
from the caret package.")
    (license license:expat)))

(define-public r-modelfree
  (package
    (name "r-modelfree")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelfree" version))
       (sha256
        (base32 "08kqc5a5ray0wgnd8c6d1frqzg9q990j1p78j7j7c5xll402c8ns"))))
    (properties `((upstream-name . "modelfree")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsem r-polynomf))
    (home-page "www.R-project.org")
    (synopsis "Model-Free Estimation of a Psychometric Function")
    (description
     "Local linear estimation of psychometric functions.  Provides functions for
nonparametric estimation of a psychometric function and for estimation of a
derived threshold and slope, and their standard deviations and confidence
intervals.")
    (license license:lgpl3+)))

(define-public r-modelfactory
  (package
    (name "r-modelfactory")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelfactory" version))
       (sha256
        (base32 "1bdcfcbd1lnx683npvwbxigqg3mbgnm2ji46nfywbay6disrc15h"))))
    (properties `((upstream-name . "modelfactory")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-mass r-dplyr))
    (home-page "https://willtirone.github.io/modelfactory/")
    (synopsis "Combine Statistical Models into a Tibble for Comparison")
    (description
     "Statisticians often want to compare the fit of different models on the same data
set.  However, this usually involves a lot of manual code to fish items out of
summary() or plain model objects.  modelfactory offers the capability to pass
multiple models in and get out metrics or coefficients for quick comparison with
easy-to-remember syntax.")
    (license license:expat)))

(define-public r-modeler
  (package
    (name "r-modeler")
    (version "3.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Modeler" version))
       (sha256
        (base32 "15gbg5568qj81wp6yy31amb2b7wl1wvlmgpmdp29lvz1p33jx2y1"))))
    (properties `((upstream-name . "Modeler")))
    (build-system r-build-system)
    (propagated-inputs (list r-tailrank
                             r-rpart
                             r-randomforest
                             r-oompabase
                             r-nnet
                             r-neuralnet
                             r-e1071
                             r-classdiscovery
                             r-classcomparison
                             r-class))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis
     "Classes and Methods for Training and Using Binary Prediction Models")
    (description
     "Defines classes and methods to learn models and use them to predict binary
outcomes.  These are generic tools, but we also include specific examples for
many common classifiers.")
    (license license:asl2.0)))

(define-public r-modeldown
  (package
    (name "r-modeldown")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelDown" version))
       (sha256
        (base32 "1l0aggkh0ykpqrp59bdz1cyl1ai397cw4hya80b7fm7s49imwczg"))))
    (properties `((upstream-name . "modelDown")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker
                             r-svglite
                             r-psych
                             r-kableextra
                             r-ggplot2
                             r-dt
                             r-drifter
                             r-devtools
                             r-dalex
                             r-breakdown
                             r-auditor
                             r-archivist))
    (home-page "https://github.com/ModelOriented/modelDown")
    (synopsis "Make Static HTML Website for Predictive Models")
    (description
     "Website generator with HTML summaries for predictive models.  This package uses
DALEX explainers to describe global model behavior.  We can see how well models
behave (tabs: Model Performance, Auditor), how much each variable contributes to
predictions (tabs: Variable Response) and which variables are the most important
for a given model (tabs: Variable Importance).  We can also compare Concept
Drift for pairs of models (tabs: Drifter).  Additionally, data available on the
website can be easily recreated in current R session.  Work on this package was
financially supported by the NCN Opus grant 2017/27/B/ST6/01307 at Warsaw
University of Technology, Faculty of Mathematics and Information Science.")
    (license license:asl2.0)))

(define-public r-modeldb
  (package
    (name "r-modeldb")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modeldb" version))
       (sha256
        (base32 "109ggy93ikcc5mv8jx8z8kx41pzzbz96mgqzwf8z7175ir6bql6z"))))
    (properties `((upstream-name . "modeldb")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidypredict
                             r-tibble
                             r-rlang
                             r-purrr
                             r-progress
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://modeldb.tidymodels.org")
    (synopsis "Fits Models Inside the Database")
    (description
     "Uses dplyr and tidyeval to fit statistical models inside the database.  It
currently supports KMeans and linear regression models.")
    (license license:expat)))

(define-public r-modeldatatoo
  (package
    (name "r-modeldatatoo")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modeldatatoo" version))
       (sha256
        (base32 "07wjrwgmg96gj8mxsx8g33l3609hrk4wvw31ml1sw3jgxpfziw7r"))))
    (properties `((upstream-name . "modeldatatoo")))
    (build-system r-build-system)
    (propagated-inputs (list r-pins))
    (home-page "https://github.com/tidymodels/modeldatatoo")
    (synopsis "More Data Sets Useful for Modeling Examples")
    (description
     "More data sets used for demonstrating or testing model-related packages are
contained in this package.  The data sets are downloaded and cached, allowing
for more and bigger data sets.")
    (license license:expat)))

(define-public r-modelcharts
  (package
    (name "r-modelcharts")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Modelcharts" version))
       (sha256
        (base32 "1jy78kyyh4r13px2d5bm7fra5bkcpimzfji0hmydspkfi3qla37x"))))
    (properties `((upstream-name . "Modelcharts")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly r-dplyr))
    (home-page "https://cran.r-project.org/package=Modelcharts")
    (synopsis "Classification Model Charts")
    (description
     "This package provides two important functions for producing Gain chart and Lift
chart for any classification model.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-modelc
  (package
    (name "r-modelc")
    (version "1.0.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelc" version))
       (sha256
        (base32 "0xz1zgv5yyvq0yfnhm1kvb4wvlyn4ccd0lkwji55vq8hndvjvk98"))))
    (properties `((upstream-name . "modelc")))
    (build-system r-build-system)
    (home-page "https://github.com/sparkfish/modelc")
    (synopsis "Linear Model to 'SQL' Compiler")
    (description
     "This is a cross-platform linear model to SQL compiler.  It generates SQL from
linear and generalized linear models.  Its interface consists of a single
function, modelc(), which takes the output of lm() or glm() functions (or any
object which has the same signature) and outputs a SQL character vector
representing the predictions on the scale of the response variable as described
in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder
& Wedderburn (1972) <doi:10.2307/2344614>.  The resultant SQL can be included in
a SELECT statement and returns output similar to that of the glm.predict() or
lm.predict() predictions, assuming numeric types are represented in the database
using sufficient precision.  Currently log and identity link functions are
supported.")
    (license license:expat)))

(define-public r-modelbpp
  (package
    (name "r-modelbpp")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelbpp" version))
       (sha256
        (base32 "02qy1y57dsgl9sl275ansbnliaafjijg49iakw32jycpf7zfh0rx"))))
    (properties `((upstream-name . "modelbpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-manymome r-lavaan r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/modelbpp/")
    (synopsis "Model BIC Posterior Probability")
    (description
     "Fits the neighboring models of a fitted structural equation model and assesses
the model uncertainty of the fitted model based on BIC posterior probabilities,
using the method presented in Wu, Cheung, and Leung (2020)
<doi:10.1080/00273171.2019.1574546>.")
    (license license:gpl3+)))

(define-public r-modelbased
  (package
    (name "r-modelbased")
    (version "0.8.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modelbased" version))
       (sha256
        (base32 "0fksigmpps5xrxqk2v7afm1zsg87mc11bfid160f84wixfk7hqw6"))))
    (properties `((upstream-name . "modelbased")))
    (build-system r-build-system)
    (propagated-inputs (list r-performance
                             r-parameters
                             r-insight
                             r-effectsize
                             r-datawizard
                             r-bayestestr))
    (native-inputs (list r-knitr))
    (home-page "https://easystats.github.io/modelbased/")
    (synopsis "Estimation of Model-Based Predictions, Contrasts and Means")
    (description
     "This package implements a general interface for model-based estimations for a
wide variety of models, used in the computation of marginal means, contrast
analysis and predictions.  For a list of supported models, see
insight::supported_models()'.")
    (license license:gpl3)))

(define-public r-modehunt
  (package
    (name "r-modehunt")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modehunt" version))
       (sha256
        (base32 "0qz9kmf1qfs2dr7kzm9l7ac0h5rvi3b9j9896p991sk4bcalsl0b"))))
    (properties `((upstream-name . "modehunt")))
    (build-system r-build-system)
    (home-page "http://www.kasparrufibach.ch")
    (synopsis "Multiscale Analysis for Density Functions")
    (description
     "Given independent and identically distributed observations X(1), ..., X(n) from
a density f, provides five methods to perform a multiscale analysis about f as
well as the necessary critical values.  The first method, introduced in Duembgen
and Walther (2008), provides simultaneous confidence statements for the
existence and location of local increases (or decreases) of f, based on all
intervals I(all) spanned by any two observations X(j), X(k).  The second method
approximates the latter approach by using only a subset of I(all) and is
therefore computationally much more efficient, but asymptotically equivalent.
Omitting the additive correction term Gamma in either method offers another two
approaches which are more powerful on small scales and less powerful on large
scales, however, not asymptotically minimax optimal anymore.  Finally, the block
procedure is a compromise between adding Gamma or not, having intermediate power
properties.  The latter is again asymptotically equivalent to the first and was
introduced in Rufibach and Walther (2010).")
    (license license:gpl2+)))

(define-public r-modalclust
  (package
    (name "r-modalclust")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Modalclust" version))
       (sha256
        (base32 "0ymzdxp6rh6baih1xm6kj8l8jxjclvnmq0gfp38cl8lpskspb26x"))))
    (properties `((upstream-name . "Modalclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-mvtnorm r-class))
    (home-page "https://cran.r-project.org/package=Modalclust")
    (synopsis "Hierarchical Modal Clustering")
    (description
     "This package performs Modal Clustering (MAC) including Hierarchical Modal
Clustering (HMAC) along with their parallel implementation (PHMAC) over several
processors.  These model-based non-parametric clustering techniques can extract
clusters in very high dimensions with arbitrary density shapes.  By default
clustering is performed over several resolutions and the results are summarised
as a hierarchical tree.  Associated plot functions are also provided.  There is
a package vignette that provides many examples.  This version adheres to CRAN
policy of not spanning more than two child processes by default.")
    (license license:gpl2)))

(define-public r-modacdc
  (package
    (name "r-modacdc")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "modACDC" version))
       (sha256
        (base32 "1djgkch4lyca46vy8djqfk3qwi8zx21j4hkwylkydlgl9fzk59j3"))))
    (properties `((upstream-name . "modACDC")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-partition
                             r-ggplot2
                             r-genio
                             r-genieclust
                             r-foreach
                             r-doparallel
                             r-data-table
                             r-ccp))
    (home-page "https://github.com/USCbiostats/ACDC")
    (synopsis
     "Association of Covariance for Detecting Differential Co-Expression")
    (description
     "This package provides a series of functions to implement association of
covariance for detecting differential co-expression (ACDC), a novel approach for
detection of differential co-expression that simultaneously accommodates
multiple phenotypes or exposures with binary, ordinal, or continuous data types.
 Users can use the default method which identifies modules by Partition or may
supply their own modules.  Also included are functions to choose an information
loss criterion (ILC) for Partition using @code{OmicS-data-based} Complex trait
Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA).  The manuscript
describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S,
Raby BA, Millstein J. \"ACDC: a general approach for detecting phenotype or
exposure associated co-expression\" (2023) <doi:10.3389/fmed.2023.1118824>.")
    (license license:expat)))

(define-public r-mod2rm
  (package
    (name "r-mod2rm")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mod2rm" version))
       (sha256
        (base32 "1adx8ndz299hzlljahvbv1px60dfchwh2ic6njr12k3zcm52rsby"))))
    (properties `((upstream-name . "mod2rm")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (home-page "https://cran.r-project.org/package=mod2rm")
    (synopsis "Moderation Analysis for Two-Instance Repeated Measures Designs")
    (description
     "Multiple moderation analysis for two-instance repeated measures designs, with up
to three simultaneous moderators (dichotomous and/or continuous) with additive
or multiplicative relationship.  Includes analyses of simple slopes and
conditional effects at (automatically determined or manually set) values of the
moderator(s), as well as an implementation of the Johnson-Neyman procedure for
determining regions of significance in single moderator models.  Based on
Montoya, A. K. (2018) \"Moderation analysis in two-instance repeated measures
designs: Probing methods and multiple moderator models\"
<doi:10.3758/s13428-018-1088-6> .")
    (license license:gpl2+)))

(define-public r-mod09nrt
  (package
    (name "r-mod09nrt")
    (version "0.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mod09nrt" version))
       (sha256
        (base32 "1wn1y33bj9r712l0f063j5gcl423anjzmvgfy0ddihcrbpz4l7a0"))))
    (properties `((upstream-name . "mod09nrt")))
    (build-system r-build-system)
    (inputs (list))
    (home-page "https://cran.r-project.org/package=mod09nrt")
    (synopsis
     "Extraction of Bands from MODIS Surface Reflectance Product MOD09 NRT")
    (description
     "Package for processing downloaded MODIS Surface reflectance Product HDF files.
Specifically, MOD09 surface reflectance product files, and the associated MOD03
geolocation files (for MODIS-TERRA).  The package will be most effective if the
user installs MRTSwath (MODIS Reprojection Tool for swath products;
<https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and adds the
directory with the MRTSwath executable to the default R PATH by editing
~/.Rprofile.")
    (license license:gpl2+)))

(define-public r-mod
  (package
    (name "r-mod")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mod" version))
       (sha256
        (base32 "0z5a6ps2m0cbw0n9kn44767m5jnbf1dl077i2p40fipv8xali9fb"))))
    (properties `((upstream-name . "mod")))
    (build-system r-build-system)
    (home-page "https://github.com/iqis/mod")
    (synopsis "Lightweight and Self-Contained Modules for Code Organization")
    (description
     "This package creates modules inline or from a file.  Modules can contain any R
object and be nested.  Each module have their own scope and package \"search
path\" that does not interfere with one another or the user's working
environment.")
    (license license:gpl3)))

(define-public r-mockthat
  (package
    (name "r-mockthat")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mockthat" version))
       (sha256
        (base32 "02j74gpppspf8ll3mvz879mnjm9wsj8dq5y3i0r8kzdizc6rxk3k"))))
    (properties `((upstream-name . "mockthat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang))
    (home-page "https://nbenn.github.io/mockthat/")
    (synopsis "Function Mocking for Unit Testing")
    (description
     "With the deprecation of mocking capabilities shipped with testthat as of edition
3 it is left to third-party packages to replace this functionality, which in
some test-scenarios is essential in order to run unit tests in limited
environments (such as no Internet connection).  Mocking in this setting means
temporarily substituting a function with a stub that acts in some sense like the
original function (for example by serving a HTTP response that has been cached
as a file).  The only exported function with_mock() is modeled after the
eponymous testthat function with the intention of providing a drop-in
replacement.")
    (license license:expat)))

(define-public r-mockr
  (package
    (name "r-mockr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mockr" version))
       (sha256
        (base32 "0n3syab3dq1am562vnjpkgmx3v06liafyy0fcyvj0spdxr0mkvr3"))))
    (properties `((upstream-name . "mockr")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-rlang))
    (native-inputs (list r-knitr))
    (home-page "https://krlmlr.github.io/mockr/")
    (synopsis "Mocking in R")
    (description
     "This package provides a means to mock a package function, i.e., temporarily
substitute it for testing.  Designed as a drop-in replacement for the now
deprecated testthat::with_mock() and testthat::local_mock()'.")
    (license license:gpl3)))

(define-public r-mocha
  (package
    (name "r-mocha")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOCHA" version))
       (sha256
        (base32 "1v0pvpzc635bz63ywzpb39h7f6qrk3yvxf21i13y5abhmyjppdmi"))))
    (properties `((upstream-name . "MOCHA")))
    (build-system r-build-system)
    (propagated-inputs (list r-wcorr
                             r-tidyselect
                             r-tidyr
                             r-summarizedexperiment
                             r-stringr
                             r-scales
                             r-s4vectors
                             r-rlang
                             r-raggedexperiment
                             r-qvalue
                             r-plyranges
                             r-pbapply
                             r-multiassayexperiment
                             r-matrixstats
                             r-magrittr
                             r-lifecycle
                             r-iranges
                             r-ggridges
                             r-ggrepel
                             r-ggplot2
                             r-ggbio
                             r-genomicranges
                             r-genomicfeatures
                             r-genomeinfodb
                             r-ensembldb
                             r-dplyr
                             r-data-table
                             r-bsgenome
                             r-biocgenerics
                             r-assertthat
                             r-annotationdbi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MOCHA")
    (synopsis "Modeling for Single-Cell Open Chromatin Analysis")
    (description
     "This package provides a statistical framework and analysis tool for open
chromatin analysis designed specifically for single cell ATAC-seq (Assay for
Transposase-Accessible Chromatin) data, after cell type/cluster identification.
These novel modules remove unwanted technical variation, identify open
chromatin, robustly models repeated measures in single cell data, implement
advanced statistical frameworks to model zero-inflation for differential and
co-accessibility analyses, and integrate with existing databases and modules for
downstream analyses to reveal biological insights.  MOCHA provides a statistical
foundation for complex downstream analysis to help advance the potential of
single cell ATAC-seq for applied studies.  Methods for zero-inflated statistics
are as described in: Ghazanfar, S., Lin, Y., Su, X. et al. (2020)
<doi:10.1038/s41592-020-0885-x>.  Pimentel, Ronald Silva, \"Kendall's Tau and
Spearman's Rho for Zero-Inflated Data\" (2009)
<https://scholarworks.wmich.edu/dissertations/721/>.")
    (license license:gpl3+)))

(define-public r-mocca
  (package
    (name "r-mocca")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MOCCA" version))
       (sha256
        (base32 "0g70s7hnmrp4sm8gx49vsijj54y4amlcy6axc4k37k4c9g4sdics"))))
    (properties `((upstream-name . "MOCCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-cluster r-clue r-class r-cclust))
    (home-page "https://cran.r-project.org/package=MOCCA")
    (synopsis
     "Multi-Objective Optimization for Collecting Cluster Alternatives")
    (description
     "This package provides methods to analyze cluster alternatives based on
multi-objective optimization of cluster validation indices.  For details see
Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.")
    (license (license:fsdg-compatible "Artistic License 2.0"))))

(define-public r-moc-gapbk
  (package
    (name "r-moc-gapbk")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "moc.gapbk" version))
       (sha256
        (base32 "1zjyaybrjxr2krx66spa93bf2q1zl5izfh6ir3r7w3n02sx9g25y"))))
    (properties `((upstream-name . "moc.gapbk")))
    (build-system r-build-system)
    (propagated-inputs (list r-nsga2r
                             r-foreach
                             r-dosnow
                             r-doparallel
                             r-dompi
                             r-amap))
    (home-page "https://cran.r-project.org/package=moc.gapbk")
    (synopsis
     "Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge")
    (description
     "This package implements the Multi-Objective Clustering Algorithm Guided by
a-Priori Biological Knowledge (MOC-@code{GaPBK}) which was proposed by
Parraga-Alava, J. et.  al. (2018) <doi:10.1186/s13040-018-0178-4>.")
    (license license:gpl2)))

(define-public r-mobsim
  (package
    (name "r-mobsim")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mobsim" version))
       (sha256
        (base32 "0ny924lsj12fqfagjwd2pq8prk0xgm89ray5b07dw7yfdcisx90i"))))
    (properties `((upstream-name . "mobsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-sads r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MoBiodiv/mobsim")
    (synopsis
     "Spatial Simulation and Scale-Dependent Analysis of Biodiversity Changes")
    (description
     "Simulation, analysis and sampling of spatial biodiversity data (May, Gerstner,
@code{McGlinn}, Xiao & Chase 2017) <doi:10.1111/2041-210x.12986>.  In the
simulation tools user define the numbers of species and individuals, the species
abundance distribution and species aggregation.  Functions for analysis include
species rarefaction and accumulation curves, species-area relationships and the
distance decay of similarity.")
    (license license:gpl3+)))

(define-public r-mobr
  (package
    (name "r-mobr")
    (version "3.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mobr" version))
       (sha256
        (base32 "02bxacq7m1ln4abd0kdzqndwj3384kfbfxcn13qqmix0i6q1i4y6"))))
    (properties `((upstream-name . "mobr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-sf
                             r-scam
                             r-scales
                             r-rlang
                             r-purrr
                             r-plotrix
                             r-pbapply
                             r-ggplot2
                             r-geosphere
                             r-egg
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MoBiodiv/mobr")
    (synopsis "Measurement of Biodiversity")
    (description
     "This package provides functions for calculating metrics for the measurement
biodiversity and its changes across scales, treatments, and gradients.  The
methods implemented in this package are described in: Chase, J.M., et al. (2018)
<doi:10.1111/ele.13151>, @code{McGlinn}, D.J., et al. (2019)
<doi:10.1111/2041-210X.13102>, @code{McGlinn}, D.J., et al. (2020)
<doi:10.1101/851717>, and @code{McGlinn}, D.J., et al. (2023)
<doi:10.1101/2023.09.19.558467>.")
    (license license:expat)))

(define-public r-mobps
  (package
    (name "r-mobps")
    (version "1.6.64")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MoBPS" version))
       (sha256
        (base32 "12czwx362pn0crj047rqb9ambl1fw73k474i7vcxhz53kbw21wj0"))))
    (properties `((upstream-name . "MoBPS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MoBPS")
    (synopsis "Modular Breeding Program Simulator")
    (description
     "Framework for the simulation framework for the simulation of complex breeding
programs and compare their economic and genetic impact.  The package is also
used as the background simulator for our a web-based interface
<http:www.mobps.de>.  Associated publication: Pook et al. (2020)
<doi:10.1534/g3.120.401193>.")
    (license license:gpl3+)))

(define-public r-mobilityindexr
  (package
    (name "r-mobilityindexr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mobilityIndexR" version))
       (sha256
        (base32 "0bs2wvbjcrfh82b483vz4lr16y2vaajz2fb1p5bw05qb78jqj9bd"))))
    (properties `((upstream-name . "mobilityIndexR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mobilityIndexR")
    (synopsis "Calculates Transition Matrices and Mobility Indices")
    (description
     "Measures mobility in a population through transition matrices and mobility
indices.  Relative, mixed, and absolute transition matrices are supported.  The
Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility
indices are supported.  Example income and grade data are included.")
    (license license:expat)))

(define-public r-mob
  (package
    (name "r-mob")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mob" version))
       (sha256
        (base32 "1mv0bsik3bdkmnggy2fv0482fqv2yck1ycw49wd45p2dyfzzwcsd"))))
    (properties `((upstream-name . "mob")))
    (build-system r-build-system)
    (propagated-inputs (list r-rborist r-gbm))
    (home-page "https://github.com/statcompute/mob")
    (synopsis "Monotonic Optimal Binning")
    (description
     "Generate the monotonic binning and perform the woe (weight of evidence)
transformation for the logistic regression used in the consumer credit scorecard
development.  The woe transformation is a piecewise transformation that is
linear to the log odds.  For a numeric variable, all of its monotonic functional
transformations will converge to the same woe transformation.")
    (license license:gpl2+)))

(define-public r-mnt
  (package
    (name "r-mnt")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnt" version))
       (sha256
        (base32 "1wf2mq4zdhznv589j7snw0gpnfrlvq6lhfihi2n20qlwm3lb9naa"))))
    (properties `((upstream-name . "mnt")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-mass))
    (home-page "https://cran.r-project.org/package=mnt")
    (synopsis "Affine Invariant Tests of Multivariate Normality")
    (description
     "Various affine invariant multivariate normality tests are provided.  It is
designed to accompany the survey article Ebner, B. and Henze, N. (2020)
<@code{arXiv:2004.07332>} titled \"Tests for multivariate normality -- a critical
review with emphasis on weighted L^2-statistics\".  We implement new and time
honoured L^2-type tests of multivariate normality, such as the
Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of
Henze-JimÃ©nes-Gamero, the test of Henze-JimÃ©nes-Gamero-Meintanis, the test of
Henze-Visage, the DÃ¶rr-Ebner-Henze test based on harmonic oscillator and the
DÃ¶rr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we
include the measures of multivariate skewness and kurtosis by Mardia, Koziol,
Malkovich and Afifi and MÃ³ri, Rohatgi and SzÃ©kely, as well as the associated
tests.  Thirdly, we include the tests of multivariate normality by Cox and
Small, the energy test of SzÃ©kely and Rizzo, the tests based on spherical
harmonics by Manzotti and Quiroz and the test of Pudelko.  All the functions and
tests need the data to be a n x d matrix where n is the samplesize (number of
rows) and d is the dimension (number of columns).")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-mns
  (package
    (name "r-mns")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MNS" version))
       (sha256
        (base32 "0if46a6rw0f2d72wnykkaa5z5b1p2c0r43il6cbwbcnnb3zd8acb"))))
    (properties `((upstream-name . "MNS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-igraph r-glmnet r-doparallel))
    (home-page "https://cran.r-project.org/package=MNS")
    (synopsis "Mixed Neighbourhood Selection")
    (description
     "An implementation of the mixed neighbourhood selection (MNS) algorithm.  The MNS
algorithm can be used to estimate multiple related precision matrices.  In
particular, the motivation behind this work was driven by the need to understand
functional connectivity networks across multiple subjects.  This package also
contains an implementation of a novel algorithm through which to simulate
multiple related precision matrices which exhibit properties frequently reported
in neuroimaging analysis.")
    (license license:gpl2)))

(define-public r-mnreadr
  (package
    (name "r-mnreadr")
    (version "2.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnreadR" version))
       (sha256
        (base32 "0dr1xvvz16g7zvq9kqz9701mxdp0wh2lpkimcad43k395ngw1g2g"))))
    (properties `((upstream-name . "mnreadR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-tibble r-nlme r-ggplot2 r-dplyr))
    (home-page "https://legge.psych.umn.edu/mnread-acuity-charts")
    (synopsis "MNREAD Parameters Estimation and Curve Plotting")
    (description
     "Allows to analyze the reading data obtained with the MNREAD Acuity Chart, a
continuous-text reading acuity chart for normal and low vision.  Provides the
necessary functions to plot the MNREAD curve and estimate automatically the four
MNREAD parameters: Maximum Reading Speed, Critical Print Size, Reading Acuity
and Reading Accessibility Index.  Parameters can be estimated either with the
standard method or with a nonlinear mixed-effects (NLME) modeling.  See
Calabrese et al.  2018 for more details <doi:10.1167/18.1.8>.")
    (license license:gpl2)))

(define-public r-mnp
  (package
    (name "r-mnp")
    (version "3.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MNP" version))
       (sha256
        (base32 "1rvkhx90hac6spcgzmrs62wvcci08accjlngw9jj4snawc7zyxvs"))))
    (properties `((upstream-name . "MNP")))
    (build-system r-build-system)
    (home-page "https://github.com/kosukeimai/MNP")
    (synopsis "Fitting the Multinomial Probit Model")
    (description
     "Fits the Bayesian multinomial probit model via Markov chain Monte Carlo.  The
multinomial probit model is often used to analyze the discrete choices made by
individuals recorded in survey data.  Examples where the multinomial probit
model may be useful include the analysis of product choice by consumers in
market research and the analysis of candidate or party choice by voters in
electoral studies.  The MNP package can also fit the model with different choice
sets for each individual, and complete or partial individual choice orderings of
the available alternatives from the choice set.  The estimation is based on the
efficient marginal data augmentation algorithm that is developed by Imai and van
Dyk (2005). \"A Bayesian Analysis of the Multinomial Probit Model Using the Data
Augmentation.\" Journal of Econometrics, Vol.  124, No.  2 (February), pp.
311-334. <doi:10.1016/j.jeconom.2004.02.002> Detailed examples are given in Imai
and van Dyk (2005). \"MNP: R Package for Fitting the Multinomial Probit Model.\"
Journal of Statistical Software, Vol.  14, No.  3 (May), pp.  1-32.
<doi:10.18637/jss.v014.i03>.")
    (license license:gpl2+)))

(define-public r-mnorm
  (package
    (name "r-mnorm")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnorm" version))
       (sha256
        (base32 "1ivd40cmcp66h4yccbll90fgqmq03pgy3hgklz8wxj0j479r21z0"))))
    (properties `((upstream-name . "mnorm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-hpa))
    (home-page "https://cran.r-project.org/package=mnorm")
    (synopsis "Multivariate Normal Distribution")
    (description
     "Calculates and differentiates probabilities and density of (conditional)
multivariate normal distribution and Gaussian copula (with various marginal
distributions) using methods described in A. Genz (2004)
<doi:10.1023/B:STCO.0000035304.20635.31>, A. Genz, F. Bretz (2009)
<doi:10.1007/978-3-642-01689-9>, H. I. Gassmann (2003)
<doi:10.1198/1061860032283> and E. Kossova, B. Potanin (2018)
<https://ideas.repec.org/a/ris/apltrx/0346.html>.")
    (license license:gpl2+)))

(define-public r-mnonr
  (package
    (name "r-mnonr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnonr" version))
       (sha256
        (base32 "143qkn3bx37a6r7kgmspqh55l2r8k9f93jgxr6qpg51w411k5ilb"))))
    (properties `((upstream-name . "mnonr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mnonr")
    (synopsis "Generator of Multivariate Non-Normal Random Numbers")
    (description
     "This package provides a data generator of multivariate non-normal data in R. It
combines two different methods to generate non-normal data, one with
user-specified multivariate skewness and kurtosis (more details can be found in
the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the
other with the given marginal skewness and kurtosis.  The latter one is the
widely-used Vale and Maurelli's method.  It also contains a function to
calculate univariate and multivariate (Mardia's Test) skew and kurtosis.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mnm
  (package
    (name "r-mnm")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MNM" version))
       (sha256
        (base32 "1s5fn1h10swl003pq0sb08i38zh951n4hpw2szf8akw1lawrzxak"))))
    (properties `((upstream-name . "MNM")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatialnp r-icsnp r-ics r-ellipse))
    (home-page "https://cran.r-project.org/package=MNM")
    (synopsis
     "Multivariate Nonparametric Methods. An Approach Based on Spatial Signs and Ranks")
    (description
     "Multivariate tests, estimates and methods based on the identity score, spatial
sign score and spatial rank score are provided.  The methods include one and
c-sample problems, shape estimation and testing, linear regression and principal
components.  The methodology is described in Oja (2010)
<doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011)
<doi:10.18637/jss.v043.i05>.")
    (license license:gpl2+)))

(define-public r-mnlr
  (package
    (name "r-mnlr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MNLR" version))
       (sha256
        (base32 "0q80232ndp7pkklzqdbga1vf51dmsxaivm4ia4wah32rc2c87fqp"))))
    (properties `((upstream-name . "MNLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rmarkdown r-nnet r-e1071 r-caret))
    (home-page "https://cran.r-project.org/package=MNLR")
    (synopsis
     "Interactive Shiny Presentation for Working with Multinomial Logistic Regression")
    (description
     "An interactive presentation on the topic of Multinomial Logistic Regression.  It
is helpful to those who want to learn Multinomial Logistic Regression quickly
and get a hands on experience.  The presentation has a template for solving
problems on Multinomial Logistic Regression.  Runtime examples are provided in
the package function as well as at
<https://jarvisatharva.shinyapps.io/@code{MultinomPresentation>}.")
    (license license:gpl2)))

(define-public r-mnlpred
  (package
    (name "r-mnlpred")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MNLpred" version))
       (sha256
        (base32 "0wb1a3d5s9hrv3sw8g1vyr9w9gmig4sdj3l339cv5cjfca75swns"))))
    (properties `((upstream-name . "MNLpred")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MNLpred")
    (synopsis "Simulated Predicted Probabilities for Multinomial Logit Models")
    (description
     "This package provides functions to easily return simulated predicted
probabilities and first differences for multinomial logit models.  It takes a
specified scenario and a multinomial model to predict probabilities with a set
of coefficients, drawn from a simulated sampling distribution.  The simulated
predictions allow for meaningful plots with means and confidence intervals.  The
methodological approach is based on the principles laid out by King, Tomz, and
Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan (2016)
<doi:10.1111/j.1540-5907.2012.00602.x>.")
    (license license:gpl3)))

(define-public r-mnlfa
  (package
    (name "r-mnlfa")
    (version "0.3-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnlfa" version))
       (sha256
        (base32 "1bcs4v02n5vwcmvij4isia1vv2mcx1gllkzzx2r29p24cmi21fgb"))))
    (properties `((upstream-name . "mnlfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-cdm))
    (home-page "https://github.com/alexanderrobitzsch/mnlfa")
    (synopsis "Moderated Nonlinear Factor Analysis")
    (description
     "Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014,
<doi:10.1080/00273171.2014.889594>).  Regularization methods are implemented for
assessing non-invariant items.  Currently, the package includes dichotomous
items and unidimensional item response models.  Extensions will be included in
future package versions.")
    (license license:gpl2+)))

(define-public r-mniw
  (package
    (name "r-mniw")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mniw" version))
       (sha256
        (base32 "10l4nrnjx8hrn2iqkh57ncqjma9mqbfbnkxzb7nw24i611pvxhcx"))))
    (properties `((upstream-name . "mniw")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mlysy/mniw/")
    (synopsis "The Matrix-Normal Inverse-Wishart Distribution")
    (description
     "Density evaluation and random number generation for the Matrix-Normal
Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T,
Wishart, and Inverse-Wishart distributions.  Core calculations are implemented
in a portable (header-only) C++ library, with matrix manipulations using the
Eigen library for linear algebra.  Also provided is a Gibbs sampler for Bayesian
inference on a random-effects model with multivariate normal observations.")
    (license license:gpl3)))

(define-public r-mnis
  (package
    (name "r-mnis")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnis" version))
       (sha256
        (base32 "03ss2sp4gjjxlfip95p2vcc0nz8q7khm3iiyh1362n2xb6yp78k0"))))
    (properties `((upstream-name . "mnis")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringi
                             r-rcpp
                             r-purrr
                             r-jsonlite
                             r-janitor
                             r-httr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.evanodell.com/mnis")
    (synopsis
     "Easy Downloading Capabilities for the Members' Name Information Service")
    (description
     "An API package for the Members Name Information Service operated by the UK
parliament.  Documentation for the API itself can be found here:
<http://data.parliament.uk/membersdataplatform/default.aspx>.")
    (license license:expat)))

(define-public r-mnet
  (package
    (name "r-mnet")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnet" version))
       (sha256
        (base32 "02chp8g5303nbcbvfmm10lh63170290fyg9kbfxgr2krg08gbr1v"))))
    (properties `((upstream-name . "mnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-mlvar r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mnet")
    (synopsis
     "Modeling Group Differences and Moderation Effects in Statistical Network Models")
    (description
     "This package provides a toolbox for modeling manifest and latent group
differences and moderation effects in various statistical network models.")
    (license license:gpl2)))

(define-public r-mnda
  (package
    (name "r-mnda")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mnda" version))
       (sha256
        (base32 "1lmkxdfym1jfq05vpjky1b6nz1xwzsdh9mz3017v23qyg7dmbzyi"))))
    (properties `((upstream-name . "mnda")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-tensorflow
                             r-reticulate
                             r-matrix
                             r-mass
                             r-magrittr
                             r-keras
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-assertthat
                             r-aggregation))
    (home-page "https://cran.r-project.org/package=mnda")
    (synopsis "Multiplex Network Differential Analysis (MNDA)")
    (description
     "Interactions between different biological entities are crucial for the function
of biological systems.  In such networks, nodes represent biological elements,
such as genes, proteins and microbes, and their interactions can be defined by
edges, which can be either binary or weighted.  The dysregulation of these
networks can be associated with different clinical conditions such as diseases
and response to treatments.  However, such variations often occur locally and do
not concern the whole network.  To capture local variations of such networks, we
propose multiplex network differential analysis (MNDA).  MNDA allows to quantify
the variations in the local neighborhood of each node (e.g. gene) between the
two given clinical states, and to test for statistical significance of such
variation.  Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.")
    (license license:gpl3+)))

(define-public r-mnb
  (package
    (name "r-mnb")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MNB" version))
       (sha256
        (base32 "01rq6bczqdbqj7w7aacvwb4zvfzy53pkxfawx5faxk60iwba8bj1"))))
    (properties `((upstream-name . "MNB")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-flexsurv))
    (home-page "https://cran.r-project.org/package=MNB")
    (synopsis "Diagnostic Tools for a Multivariate Negative Binomial Model")
    (description
     "Diagnostic tools as residual analysis, global, local and total-local influence
for the multivariate model from the random intercept Poisson generalized log
gamma model are available in this package.  Including also, the estimation
process by maximum likelihood method, for details see Fabio, L. F; Villegas, C.
L.; Carrasco, J.M.F and de Castro, M. (2021)
<doi:10.1080/03610926.2021.1939380>.")
    (license license:gpl2+)))

(define-public r-mnarclust
  (package
    (name "r-mnarclust")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MNARclust" version))
       (sha256
        (base32 "0r4d066nd5xzry3zn463zqzy3x45pqhix7jlg9ib3sjdfrqqimnn"))))
    (properties `((upstream-name . "MNARclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-rmutil r-rcpparmadillo r-rcpp))
    (home-page "https://arxiv.org/abs/2009.07662")
    (synopsis
     "Clustering Data with Non-Ignorable Missingness using Semi-Parametric Mixture Models")
    (description
     "Clustering of data under a non-ignorable missingness mechanism.  Clustering is
achieved by a semi-parametric mixture model and missingness is managed by using
the pattern-mixture approach.  More details of the approach are available in Du
Roy de Chaumaray et al. (2020) <@code{arXiv:2009.07662>}.")
    (license license:gpl2+)))

(define-public r-mn
  (package
    (name "r-mn")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MN" version))
       (sha256
        (base32 "1cq1yi5y014hfiz9k1l0pif4n1bs0zf8nzynichv6l9r8mr63j2b"))))
    (properties `((upstream-name . "MN")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast))
    (home-page "https://cran.r-project.org/package=MN")
    (synopsis "Matrix Normal Distribution")
    (description
     "Density computation, random matrix generation and maximum likelihood estimation
of the matrix normal distribution.  References: Pocuca N., Gallaugher M. P.,
Clark K. M. & @code{McNicholas} P. D. (2019).  Assessing and Visualizing Matrix
Variate Normality. <doi:10.48550/@code{arXiv.1910.02859>} and the relevant
wikipedia page.")
    (license license:gpl2+)))

(define-public r-mmvbvs
  (package
    (name "r-mmvbvs")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMVBVS" version))
       (sha256
        (base32 "08nkhrf62yywpm0nw22pqzlcm72sf4zq44640a7pf57bbzm38ccy"))))
    (properties `((upstream-name . "MMVBVS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-reshape2
                             r-reshape
                             r-rcpparmadillo
                             r-rcpp
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=MMVBVS")
    (synopsis "Missing Multivariate Bayesian Variable Selection")
    (description
     "This package provides a variable selection tool for multivariate normal
variables with missing-at-random values using Bayesian Hierarchical Model.
Visualization functions show the posterior distribution of gamma (inclusion
variables) and beta (coefficients).  Users can also visualize the heatmap of the
posterior mean of covariance matrix.  Kim, T. Nicolae, D. (2019)
<https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>.  Guan, Y.
Stephens, M. (2011) <doi:10.1214/11-AOAS455>.")
    (license license:gpl2+)))

(define-public r-mmtsne
  (package
    (name "r-mmtsne")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmtsne" version))
       (sha256
        (base32 "1by0hrggla6idc1isnq072i1wfpw4x7id3hkg8l65gy8iycccd12"))))
    (properties `((upstream-name . "mmtsne")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mmtsne")
    (synopsis "Multiple Maps t-SNE")
    (description
     "An implementation of multiple maps t-distributed stochastic neighbor embedding
(t-SNE).  Multiple maps t-SNE is a method for projecting high-dimensional data
into several low-dimensional maps such that non-metric space properties are
better preserved than they would be by a single map.  Multiple maps t-SNE with
only one map is equivalent to standard t-SNE. When projecting onto more than one
map, multiple maps t-SNE estimates a set of latent weights that allow each point
to contribute to one or more maps depending on similarity relationships in the
original data.  This implementation is a port of the original Matlab library by
Laurens van der Maaten.  See Van der Maaten and Hinton (2012)
<doi:10.1007/s10994-011-5273-4>.  This material is based upon work supported by
the United States Air Force and Defense Advanced Research Project Agency (DARPA)
under Contract No.  FA8750-17-C-0020.  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 United States Air Force and Defense
Advanced Research Projects Agency.  Distribution Statement A: Approved for
Public Release; Distribution Unlimited.")
    (license (list (license:fsdg-compatible "FreeBSD")
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mmstat4
  (package
    (name "r-mmstat4")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmstat4" version))
       (sha256
        (base32 "0n6vj5s5jlj09nmzhyv64ll07s4q7ci0q80xvl9zpa3idmbqjw24"))))
    (properties `((upstream-name . "mmstat4")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringdist
                             r-shiny
                             r-rstudioapi
                             r-rio
                             r-reticulate
                             r-rappdirs
                             r-knitr
                             r-httr
                             r-digest
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mmstat4")
    (synopsis "Access to Teaching Materials from a ZIP File or GitHub")
    (description
     "This package provides access to teaching materials for various statistics
courses, including R and Python programs, Shiny apps, data, and PDF/HTML
documents.  These materials are stored on the Internet as a ZIP file (e.g., in a
@code{GitHub} repository) and can be downloaded and displayed or run locally.
The content of the ZIP file is temporarily or permanently stored.  By default,
the package uses the @code{GitHub} repository sigbertklinke/mmstat4.data.
Additionally, the package includes association_measures.R from the archived
package ryouready by Mark Heckman and some auxiliary functions.")
    (license license:gpl3)))

(define-public r-mmsample
  (package
    (name "r-mmsample")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmsample" version))
       (sha256
        (base32 "0mdd2zryadd31a5pls5fvlxx52mr54jsi1i6mwrs4y7rl3i9v95z"))))
    (properties `((upstream-name . "mmsample")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://cran.r-project.org/package=mmsample")
    (synopsis "Multivariate Matched Sampling")
    (description
     "Subset a control group to match an intervention group on a set of features using
multivariate matching and propensity score calipers.  Based on methods in
Rosenbaum and Rubin (1985).")
    (license license:gpl3)))

(define-public r-mmrm
  (package
    (name "r-mmrm")
    (version "0.3.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmrm" version))
       (sha256
        (base32 "04rzb1bww8rdnpawwcx16kp5x0zfdhi23q4v7avb3c6ilw641s65"))))
    (properties `((upstream-name . "mmrm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-tibble
                             r-testthat
                             r-stringr
                             r-rdpack
                             r-rcppeigen
                             r-rcpp
                             r-nlme
                             r-matrix
                             r-lifecycle
                             r-generics
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://openpharma.github.io/mmrm/")
    (synopsis "Mixed Models for Repeated Measures")
    (description
     "Mixed models for repeated measures (MMRM) are a popular choice for analyzing
longitudinal continuous outcomes in randomized clinical trials and beyond; see
Cnaan, Laird and Slasor (1997)
<doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for
a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008)
<doi:10.1177/009286150804200402> for a review.  This package implements MMRM
based on the marginal linear model without random effects using Template Model
Builder ('TMB') which enables fast and robust model fitting.  Users can specify
a variety of covariance matrices, weight observations, fit models with
restricted or standard maximum likelihood inference, perform hypothesis testing
with Satterthwaite or Kenward-Roger adjustment, and extract least square means
estimates by using emmeans'.")
    (license license:asl2.0)))

(define-public r-mmrcaseselection
  (package
    (name "r-mmrcaseselection")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMRcaseselection" version))
       (sha256
        (base32 "093bp2c988p82wqz0cq9ra4bd06647v12n6yk1rgr2bvvygg1i8f"))))
    (properties `((upstream-name . "MMRcaseselection")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ingorohlfing/MMRcaseselection")
    (synopsis "Case Classification and Selection Based on Regression Results")
    (description
     "Researchers doing a mixed-methods analysis (nested analysis as developed by
Lieberman (2005) <doi:10.1017/S0003055405051762>) can use the package for the
classification of cases and case selection using results of a linear regression.
 One can designate cases as typical, deviant, extreme and pathway case and use
different case selection strategies for the choice of a case belonging to one of
these types.")
    (license license:gpl3)))

(define-public r-mmr
  (package
    (name "r-mmr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmr" version))
       (sha256
        (base32 "0by2v47b4kpjhfxzzl6kc1shw7g46nwnww4rq333r2cywvrbkk4w"))))
    (properties `((upstream-name . "mmr")))
    (build-system r-build-system)
    (home-page "https://github.com/stevecondylios/mmr")
    (synopsis "Matrix Multiplication on Data.frames")
    (description
     "Simple helpers for matrix multiplication on data.frames.  These allow for more
concise code during low level mathematical operations, and help ensure code is
more easily read, understood, and serviced.")
    (license license:expat)))

(define-public r-mmpp
  (package
    (name "r-mmpp")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmpp" version))
       (sha256
        (base32 "16aypjf4i0ya3qgxqrrg8y0xbnq1pnqky1dpkgln5q494hphyg91"))))
    (properties `((upstream-name . "mmpp")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mmpp")
    (synopsis
     "Various Similarity and Distance Metrics for Marked Point Processes")
    (description
     "Compute similarities and distances between marked point processes.")
    (license license:gpl2)))

(define-public r-mmpca
  (package
    (name "r-mmpca")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmpca" version))
       (sha256
        (base32 "1kzjwl0sn8cdgw3jw95lg90vgh47iw293ffn7c0fb9kgxisd6dma"))))
    (properties `((upstream-name . "mmpca")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppgsl r-rcppeigen r-rcpp r-digest))
    (home-page "https://github.com/cyianor/mmpca")
    (synopsis "Integrative Analysis of Several Related Data Matrices")
    (description
     "This package provides a generalization of principal component analysis for
integrative analysis.  The method finds principal components that describe
single matrices or that are common to several matrices.  The solutions are
sparse.  Rank of solutions is automatically selected using cross validation.
The method is described in Kallus et al. (2019) <@code{arXiv:1911.04927>}.")
    (license license:gpl3+)))

(define-public r-mmpa
  (package
    (name "r-mmpa")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mMPA" version))
       (sha256
        (base32 "0g4zjknz52fpk7f436j95aw93aa8q2jwcrb1pqkm294kr276nnd9"))))
    (properties `((upstream-name . "mMPA")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mMPA")
    (synopsis "Implementation of Marker-Assisted Mini-Pooling with Algorithm")
    (description
     "To determine the number of quantitative assays needed for a sample of data using
pooled testing methods, which include mini-pooling (MP), MP with algorithm
(MPA), and marker-assisted MPA (@code{mMPA}).  To estimate the number of assays
needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate
different orders in which the sample would be collected to form pools.  Using MC
avoids the dependence of the estimated number of assays on any specific ordering
of the samples to form pools.")
    (license license:expat)))

(define-public r-mmodely
  (package
    (name "r-mmodely")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmodely" version))
       (sha256
        (base32 "0fw2ncva8y5jxybi51bbqygkag5zaswni8bfg4gf78yjvjw3iyj5"))))
    (properties `((upstream-name . "mmodely")))
    (build-system r-build-system)
    (propagated-inputs (list r-caroline r-caper r-ape))
    (home-page "https://cran.r-project.org/package=mmodely")
    (synopsis
     "Modeling Multivariate Origins Determinants - Evolutionary Lineages in Ecology")
    (description
     "Perform multivariate modeling of evolved traits, with special attention to
understanding the interplay of the multi-factorial determinants of their origins
in complex ecological settings (Stephens, 2007
<doi:10.1016/j.tree.2006.12.003>).  This software primarily concentrates on
phylogenetic regression analysis, enabling implementation of tree transformation
averaging and visualization functionality.  Functions additionally support
information theoretic approaches (Grueber, 2011
<doi:10.1111/j.1420-9101.2010.02210.x>; Garamszegi, 2011
<doi:10.1007/s00265-010-1028-7>) such as model averaging and selection of
phylogenetic models.  Accessory functions are also implemented for coef
standardization (Cade 2015), selection uncertainty, and variable importance
(Burnham & Anderson 2000).  There are other numerous functions for visualizing
confounded variables, plotting phylogenetic trees, as well as reporting and
exporting modeling results.  Lastly, as challenges to ecology are inherently
multifarious, and therefore often multi-dataset, this package features several
functions to support the identification, interpolation, merging, and updating of
missing data and outdated nomenclature.")
    (license (license:fsdg-compatible "Apache License"))))

(define-public r-mmod
  (package
    (name "r-mmod")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmod" version))
       (sha256
        (base32 "1dz6887mlqbagjj98wcabmxj9hvsz8pgsizy1fqkwjhhs44c62y4"))))
    (properties `((upstream-name . "mmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-pegas r-adegenet))
    (home-page "https://github.com/dwinter/mmod")
    (synopsis "Modern Measures of Population Differentiation")
    (description
     "This package provides functions for measuring population divergence from
genotypic data.")
    (license license:expat)))

(define-public r-mmoc
  (package
    (name "r-mmoc")
    (version "0.1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMOC" version))
       (sha256
        (base32 "1vk2rrngv891g8gcqqn8yyca8rkqfz786amx6g900ph410smyy1s"))))
    (properties `((upstream-name . "MMOC")))
    (build-system r-build-system)
    (propagated-inputs (list r-spectrum r-mass r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MMOC")
    (synopsis "Multi-Omic Spectral Clustering using the Flag Manifold")
    (description
     "Multi-omic (or any multi-view) spectral clustering methods often assume the same
number of clusters across all datasets.  We supply methods for multi-omic
spectral clustering when the number of distinct clusters differs among the omics
profiles (views).")
    (license license:expat)))

(define-public r-mmmgee
  (package
    (name "r-mmmgee")
    (version "1.20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmmgee" version))
       (sha256
        (base32 "0y3pwnc9h7y2l9cvgfl62nl1vnnkla2y8082jhdvxsb3mk88rgad"))))
    (properties `((upstream-name . "mmmgee")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix))
    (home-page "https://cran.r-project.org/package=mmmgee")
    (synopsis
     "Simultaneous Inference for Multiple Linear Contrasts in GEE Models")
    (description
     "This package provides global hypothesis tests, multiple testing procedures and
simultaneous confidence intervals for multiple linear contrasts of regression
coefficients in a single generalized estimating equation (GEE) model or across
multiple GEE models.  GEE models are fit by a modified version of the
@code{geeM} package.")
    (license license:gpl3)))

(define-public r-mmlr
  (package
    (name "r-mmlr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMLR" version))
       (sha256
        (base32 "1jc33ani6w54j38kxzk0kfj1hjfk80cil2hdja2hcsk6kqwd431d"))))
    (properties `((upstream-name . "MMLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://cran.r-project.org/package=MMLR")
    (synopsis "Fitting Markov-Modulated Linear Regression Models")
    (description
     "This package provides a set of tools for fitting Markov-modulated linear
regression, where responses Y(t) are time-additive, and model operates in the
external environment, which is described as a continuous time Markov chain with
finite state space.  Model is proposed by Alexander Andronov (2012)
<@code{arXiv:1901.09600v1>} and algorithm of parameters estimation is based on
eigenvalues and eigenvectors decomposition.  Markov-switching regression models
have the same idea of varying the regression parameters randomly in accordance
with external environment.  The difference is that for Markov-modulated linear
regression model the external environment is described as a continuous-time
homogeneous irreducible Markov chain with known parameters while switching
models consider Markov chain as unobserved and estimation procedure involves
estimation of transition matrix.  These models have significant differences in
terms of the analytical approach.  Also, package provides a set of data
simulation tools for Markov-modulated linear regression (for academical/research
purposes).  Research project No.  1.1.1.2/VIAA/1/16/075.")
    (license license:gpl2+)))

(define-public r-mmirestriktor
  (package
    (name "r-mmirestriktor")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmirestriktor" version))
       (sha256
        (base32 "008x9bypp34aq59v6dqns5qjnh94lrng6yjiiaybi5acdyrvipbr"))))
    (properties `((upstream-name . "mmirestriktor")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes
                             r-shiny
                             r-rpostgres
                             r-restriktor
                             r-pool
                             r-mmcards
                             r-mass
                             r-dt))
    (home-page "https://github.com/mightymetrika/mmirestriktor")
    (synopsis "Informative Hypothesis Testing Web Applications")
    (description
     "Offering enhanced statistical power compared to traditional hypothesis testing
methods, informative hypothesis testing allows researchers to explicitly model
their expectations regarding the relationships among parameters.  An important
software tool for this framework is restriktor'.  The mmirestriktor package
provides shiny web applications to implement some of the basic functionality of
restriktor'.  The mmirestriktor() function launches a shiny application for
fitting and analyzing models with constraints.  The @code{FbarCards}() function
launches a card game application which can help build intuition about
informative hypothesis testing.  The iht_interpreter() helps interpret
informative hypothesis testing results based on guidelines in Vanbrabant and
Rosseel (2020) <doi:10.4324/9780429273872-14>.")
    (license license:expat)))

(define-public r-mmints
  (package
    (name "r-mmints")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmints" version))
       (sha256
        (base32 "0ddqbnj466sd56470sc8m1irzgch7nqidrbb01axwmyqjnfy56jv"))))
    (properties `((upstream-name . "mmints")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rpostgres r-pool r-dt))
    (home-page "https://github.com/mightymetrika/mmints")
    (synopsis "Workflows for Building Web Applications")
    (description
     "Sharing statistical methods or simulation frameworks through shiny applications
often requires workflows for handling data.  To help save and display simulation
results, the @code{postgresUI}() and @code{postgresServer}() functions in mmints
help with persistent data storage using a @code{PostgreSQL} database.  The
mmints package also offers data upload functionality through the
@code{csvUploadUI}() and @code{csvUploadServer}() functions which allow users to
upload data, view variables and their types, and edit variable types before
fitting statistical models within the shiny application.  These tools aim to
enhance efficiency and user interaction in shiny based statistical and
simulation applications.")
    (license license:expat)))

(define-public r-mminp
  (package
    (name "r-mminp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMINP" version))
       (sha256
        (base32 "0qf5b59b878k7kf5iz6zxg1291pbbq8a1jrsax5bxfrdpq7ili0l"))))
    (properties `((upstream-name . "MMINP")))
    (build-system r-build-system)
    (propagated-inputs (list r-omicspls r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/YuLab-SMU/MMINP")
    (synopsis
     "Microbe-Metabolite Interactions-Based Metabolic Profiles Predictor")
    (description
     "This package implements a computational framework to predict microbial
community-based metabolic profiles with O2PLS model.  It provides procedures of
model training and prediction.  Paired microbiome and metabolome data are needed
for modeling, and the trained model can be applied to predict metabolites of
analogous environments using new microbial feature abundances.")
    (license license:gpl3+)))

(define-public r-mmicats
  (package
    (name "r-mmicats")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmiCATs" version))
       (sha256
        (base32 "0g4w0h15dglyk28dibz2vp780s7zygwpvm87hc1gl2a3lb9kzy2v"))))
    (properties `((upstream-name . "mmiCATs")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes
                             r-shiny
                             r-rpostgres
                             r-robustbase
                             r-robust
                             r-pool
                             r-mmcards
                             r-mass
                             r-lmertest
                             r-dt
                             r-clusterses
                             r-broom-mixed
                             r-broom))
    (home-page "https://github.com/mightymetrika/mmiCATs")
    (synopsis "Cluster Adjusted t Statistic Applications")
    (description
     "Simulation results detailed in Esarey and Menger (2019)
<doi:10.1017/psrm.2017.42> demonstrate that cluster adjusted t statistics (CATs)
are an effective method for correcting standard errors in scenarios with a small
number of clusters.  The @code{mmiCATs} package offers a suite of tools for
working with CATs.  The @code{mmiCATs}() function initiates a shiny web
application, facilitating the analysis of data utilizing CATs, as implemented in
the cluster.im.glm() function from the @code{clusterSEs} package.  Additionally,
the pwr_func_lmer() function is designed to simplify the process of conducting
simulations to compare mixed effects models with CATs models.  For educational
purposes, the @code{CloseCATs}() function launches a shiny application card
game, aimed at enhancing users understanding of the conditions under which CATs
should be preferred over random intercept models.")
    (license license:expat)))

(define-public r-mmibain
  (package
    (name "r-mmibain")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmibain" version))
       (sha256
        (base32 "1ii2lfsbmp0n85a3y76zp87fpj5nm5ndigjyl09sl0cl2g8q7ql7"))))
    (properties `((upstream-name . "mmibain")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes
                             r-shiny
                             r-psych
                             r-mmcards
                             r-lavaan
                             r-igraph
                             r-ggplot2
                             r-e1071
                             r-dt
                             r-car
                             r-broom
                             r-bain))
    (home-page "https://github.com/mightymetrika/mmibain")
    (synopsis "Bayesian Informative Hypotheses Evaluation Web Applications")
    (description
     "Researchers often have expectations about the relations between means of
different groups or standardized regression coefficients; using informative
hypothesis testing to incorporate these expectations into the analysis through
order constraints increases statistical power Vanbrabant and Rosseel (2020)
<doi:10.4324/9780429273872-14>.  Another valuable tool, the Bayes factor, can
evaluate evidence for multiple hypotheses without concerns about multiple
testing, and can be used in Bayesian updating Hoijtink, Mulder, van Lissa & Gu
(2019) <doi:10.1037/met0000201>.  The bain R package enables informative
hypothesis testing using the Bayes factor.  The mmibain package provides shiny
web applications based on bain'.  The @code{RepliCrisis}() function launches a
shiny card game to simulate the evaluation of replication studies while the
mmibain() function launches a shiny application to fit Bayesian informative
hypotheses evaluation models from bain'.")
    (license license:expat)))

(define-public r-mmgfm
  (package
    (name "r-mmgfm")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMGFM" version))
       (sha256
        (base32 "1alsgv7wjb6mcg0zgcxkpfr89hr3yclgc68yv88wzq8ad9py9wzg"))))
    (properties `((upstream-name . "MMGFM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-multicoap
                             r-mass
                             r-irlba
                             r-gfm))
    (home-page "https://cran.r-project.org/package=MMGFM")
    (synopsis "Multi-Study Multi-Modality Generalized Factor Model")
    (description
     "We introduce a generalized factor model designed to jointly analyze
high-dimensional multi-modality data from multiple studies by extracting
study-shared and specified factors.  Our factor models account for heterogeneous
noises and overdispersion among modality variables with augmented covariates.
We propose an efficient and speedy variational estimation procedure for
estimating model parameters, along with a novel criterion for selecting the
optimal number of factors.  More details can be referred to Liu et al. (2024)
<doi:10.48550/@code{arXiv.2408.10542>}.")
    (license license:gpl3)))

(define-public r-mmem
  (package
    (name "r-mmem")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMeM" version))
       (sha256
        (base32 "1cig085h2jvrpb904wglwvhvds3sn58wpbl081wcwvzipfa8y37s"))))
    (properties `((upstream-name . "MMeM")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-psych
                             r-matrixcalc
                             r-matrix
                             r-mass
                             r-lme4
                             r-jointdiag))
    (home-page "https://cran.r-project.org/package=MMeM")
    (synopsis "Multivariate Mixed Effects Model")
    (description
     "Analyzing data under multivariate mixed effects model using multivariate REML
and multivariate Henderson3 methods.  See Meyer (1985) <doi:10.2307/2530651> and
Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.")
    (license license:gpl3)))

(define-public r-mmeln
  (package
    (name "r-mmeln")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmeln" version))
       (sha256
        (base32 "06knagiyhm24rk4m2p7yz61sscxplwpwbwf1356jazxp7y0v61xp"))))
    (properties `((upstream-name . "mmeln")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mmeln")
    (synopsis "Estimation of Multinormal Mixture Distribution")
    (description
     "Fit multivariate mixture of normal distribution using covariance structure.")
    (license license:gpl3)))

(define-public r-mme
  (package
    (name "r-mme")
    (version "0.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mme" version))
       (sha256
        (base32 "1k60y1yrf7fv939v32kf3il7r3cws0kih4bpm0ap28fvbx3vr3yb"))))
    (properties `((upstream-name . "mme")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=mme")
    (synopsis "Multinomial Mixed Effects Models")
    (description
     "Fit Gaussian Multinomial mixed-effects models for small area estimation: Model
1, with one random effect in each category of the response variable
(Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2,
introducing independent time effect; Model 3, introducing correlated time
effect.  mme calculates direct and parametric bootstrap MSE estimators
(Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.")
    (license license:gpl2+)))

(define-public r-mmdvariance
  (package
    (name "r-mmdvariance")
    (version "0.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMDvariance" version))
       (sha256
        (base32 "1yiqabifym7gdknqrhfnql6qc8p163npzrrckbyqjry3yvkqr5wi"))))
    (properties `((upstream-name . "MMDvariance")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lawstat r-biobase))
    (home-page "https://cran.r-project.org/package=MMDvariance")
    (synopsis
     "Detecting Differentially Variable Genes Using the Mixture of Marginal Distributions")
    (description
     "Gene selection based on variance using the marginal distributions of gene
profiles that characterized by a mixture of three-component multivariate
distributions.  Please see the reference: Li X, Fu Y, Wang X, @code{DeMeo} DL,
Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.")
    (license license:gpl2+)))

(define-public r-mmdcopula
  (package
    (name "r-mmdcopula")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMDCopula" version))
       (sha256
        (base32 "10326dnhmscb0dvgyrgz5l1z1wdf9g5xb6s4rlivy7b3zkgq3ii1"))))
    (properties `((upstream-name . "MMDCopula")))
    (build-system r-build-system)
    (propagated-inputs (list r-wdm r-vinecopula r-randtoolbox r-pbapply
                             r-cubature))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MMDCopula")
    (synopsis "Robust Estimation of Copulas by Maximum Mean Discrepancy")
    (description
     "This package provides functions for the robust estimation of parametric families
of copulas using minimization of the Maximum Mean Discrepancy, following the
article Alquier, ChÃ©rief-Abdellatif, Derumigny and Fermanian (2022)
<doi:10.1080/01621459.2021.2024836>.")
    (license license:gpl3)))

(define-public r-mmdai
  (package
    (name "r-mmdai")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMDai" version))
       (sha256
        (base32 "0b9lksql266sqhxy4k3chz2pcaabgijkp7g0v4pm7ci5v50d4vag"))))
    (properties `((upstream-name . "MMDai")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MMDai")
    (synopsis
     "Multivariate Multinomial Distribution Approximation and Imputation for Incomplete Categorical Data")
    (description
     "This package provides a method to impute the missingness in categorical data.
Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.")
    (license license:gpl2+)))

(define-public r-mmd
  (package
    (name "r-mmd")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMD" version))
       (sha256
        (base32 "0cz009ndsvlk4hvvyl49l0d68z3b7z083r26z52lizbmw7zaxvbv"))))
    (properties `((upstream-name . "MMD")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-e1071 r-bigmemory))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MMD")
    (synopsis
     "Minimal Multilocus Distance (MMD) for Source Attribution and Loci Selection")
    (description
     "The aim of the package is two-fold: (i) To implement the MMD method for
attribution of individuals to sources using the Hamming distance between
multilocus genotypes. (ii) To select informative genetic markers based on
information theory concepts (entropy, mutual information and redundancy).  The
package implements the functions introduced by Perez-Reche, F. J., Rotariu, O.,
Lopes, B. S., Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence
data to efficiently attribute individuals to source populations.  Scientific
Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>.  See more details and
examples in the README file.")
    (license license:gpl3)))

(define-public r-mmcsd
  (package
    (name "r-mmcsd")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mmcsd" version))
       (sha256
        (base32 "1vj9qissby967a4z21772vj49hxhjppsn4w6l6s998pll8500np5"))))
    (properties `((upstream-name . "Mmcsd")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlist
                             r-purrr
                             r-magrittr
                             r-knitr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Mmcsd")
    (synopsis "Modeling Complex Longitudinal Data in a Quick and Easy Way")
    (description
     "Matching longitudinal methodology models with complex sampling design.  It fits
fixed and random effects models and covariance structured models so far.  It
also provides tools to perform statistical tests considering these
specifications as described in : Pacheco, P. H. (2021). \"Modeling complex
longitudinal data in R: development of a statistical package.\"
<https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.")
    (license license:gpl3+)))

(define-public r-mmconvert
  (package
    (name "r-mmconvert")
    (version "0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmconvert" version))
       (sha256
        (base32 "1bmvcvkisqmfnpwwwb8fvnqq5fbdd6ppsi6iw9parbmzrnir8zmj"))))
    (properties `((upstream-name . "mmconvert")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/rqtl/mmconvert")
    (synopsis "Mouse Map Converter")
    (description
     "Convert mouse genome positions between the build 39 physical map and the genetic
map of Cox et al. (2009) <doi:10.1534/genetics.109.105486>.")
    (license license:gpl3)))

(define-public r-mmcm
  (package
    (name "r-mmcm")
    (version "1.2-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmcm" version))
       (sha256
        (base32 "09xcvgzgg5x890jibmmbcd3rr8q8vmwxwk6s8zgn9a8hfdwqny9a"))))
    (properties `((upstream-name . "mmcm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=mmcm")
    (synopsis "Modified Maximum Contrast Method")
    (description
     "An implementation of modified maximum contrast methods (Sato et al. (2009)
<doi:10.1038/tpj.2008.17>; Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>)
and the maximum contrast method (Yoshimura et al. (1997)
<doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give
P-value by using randomized quasi-Monte Carlo method with pmvt() function of
package mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.")
    (license license:gpl3)))

(define-public r-mmcif
  (package
    (name "r-mmcif")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmcif" version))
       (sha256
        (base32 "14kp0n24nxcfnfq8drxfhsvd4apg4f7iwdamzw005rlhi5srmw3r"))))
    (properties `((upstream-name . "mmcif")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-rcpparmadillo r-rcpp r-psqn
                             r-alabama))
    (native-inputs (list r-r-rsp gfortran))
    (home-page "https://github.com/boennecd/mmcif")
    (synopsis "Mixed Multivariate Cumulative Incidence Functions")
    (description
     "Fits the mixed cumulative incidence functions model suggested by
<doi:10.1093/biostatistics/kxx072> which decomposes within cluster dependence of
risk and timing.  The estimation method supports computation in parallel using a
shared memory C++ implementation.  A sandwich estimator of the covariance matrix
is available.  Natural cubic splines are used to provide a flexible model for
the cumulative incidence functions.")
    (license license:gpl3+)))

(define-public r-mmcards
  (package
    (name "r-mmcards")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmcards" version))
       (sha256
        (base32 "17icmsk37lfr850ilcx7q97bjzimv2zj0c27m9gpmb00bnkrkv21"))))
    (properties `((upstream-name . "mmcards")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mightymetrika/mmcards")
    (synopsis "Playing Cards Utility Functions")
    (description
     "Early insights in probability theory were largely influenced by questions about
gambling and games of chance, as noted by Blitzstein and Hwang (2019,
ISBN:978-1138369917).  In modern times, playing cards continue to serve as an
effective teaching tool for probability, statistics, and even R programming, as
demonstrated by Grolemund (2014, ISBN:978-1449359010).  The mmcards package
offers a collection of utility functions designed to aid in the creation,
manipulation, and utilization of playing card decks in multiple formats.  These
include a standard 52-card deck, as well as alternative decks such as decks
defined by custom anonymous functions and custom interleaved decks.  Optimized
for the development of educational shiny applications, the package is
particularly useful for teaching statistics and probability through card-based
games.  Functions include shuffle_deck(), which creates either a shuffled
standard deck or a shuffled custom alternative deck; deal_card(), which takes a
deck and returns a list object containing both the dealt card and the updated
deck; and i_deck(), which adds image paths to card objects, further enriching
the package's utility in the development of interactive shiny application card
games.")
    (license license:expat)))

(define-public r-mmc
  (package
    (name "r-mmc")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmc" version))
       (sha256
        (base32 "03nhfhiiadga8mcp33kj20g33v9n5i62fdqgi20h5p80g849k719"))))
    (properties `((upstream-name . "mmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass))
    (home-page "https://cran.r-project.org/package=mmc")
    (synopsis "Multivariate Measurement Error Correction")
    (description
     "This package provides routines for multivariate measurement error correction.
Includes procedures for linear, logistic and Cox regression models.
Bootstrapped standard errors and confidence intervals can be obtained for
corrected estimates.")
    (license license:gpl3+)))

(define-public r-mmb
  (package
    (name "r-mmb")
    (version "0.13.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmb" version))
       (sha256
        (base32 "07nni04fb0gprma368axzz303db12hp22fz1a0xwh3bbh6n7zb9g"))))
    (properties `((upstream-name . "mmb")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MrShoenel/R-mmb")
    (synopsis "Arbitrary Dependency Mixed Multivariate Bayesian Models")
    (description
     "Supports Bayesian models with full and partial (hence arbitrary) dependencies
between random variables.  Discrete and continuous variables are supported, and
conditional joint probabilities and probability densities are estimated using
Kernel Density Estimation (KDE).  The full general form, which implements an
extension to Bayes theorem, as well as the simple form, which is just a Bayesian
network, both support regression through segmentation and KDE and estimation of
probability or relative likelihood of discrete or continuous target random
variables.  This package also provides true statistical distance measures based
on Bayesian models.  Furthermore, these measures can be facilitated on
neighborhood searches, and to estimate the similarity and distance between data
points.  Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053> and by
Scutari (2010) <doi:10.18637/jss.v035.i03>.")
    (license license:gpl3)))

(define-public r-mmarch-ac
  (package
    (name "r-mmarch-ac")
    (version "2.9.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mMARCH.AC" version))
       (sha256
        (base32 "0mrhr1f5qvqdv1mwzd8iqgfjwq9n4ny1s8a5sppkdp9zyvvmwv81"))))
    (properties `((upstream-name . "mMARCH.AC")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xlsx
                             r-tidyr
                             r-survival
                             r-refund
                             r-minpack-lm
                             r-kableextra
                             r-ineq
                             r-ggir
                             r-dplyr
                             r-denseflmm
                             r-cosinor2
                             r-cosinor
                             r-actfrag
                             r-actcr
                             r-accelerometry
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/WeiGuoNIMH/mMARCH.AC")
    (synopsis "Processing of Accelerometry Data with 'GGIR' in mMARCH")
    (description
     "Mobile Motor Activity Research Consortium for Health (@code{mMARCH}) is a
collaborative network of studies of clinical and community samples that employ
common clinical, biological, and digital mobile measures across involved
studies.  One of the main scientific goals of @code{mMARCH} sites is developing
a better understanding of the inter-relationships between accelerometry-measured
physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental
and physical health in children, adolescents, and adults.  Currently, there is
no consensus on a standard procedure for a data processing pipeline of raw
accelerometry data, and few open-source tools to facilitate their development.
The R package GGIR is the most prominent open-source software package that
offers great functionality and tremendous user flexibility to process raw
accelerometry data.  However, even with GGIR', processing done in a harmonized
and reproducible fashion requires a non-trivial amount of expertise combined
with a careful implementation.  In addition, novel accelerometry-derived
features of PA/SL/CR capturing multiscale, time-series, functional,
distributional and other complimentary aspects of accelerometry data being
constantly proposed and become available via non-GGIR R implementations.  To
address these issues, @code{mMARCH} developed a streamlined harmonized and
reproducible pipeline for loading and cleaning raw accelerometry data,
extracting features available through GGIR as well as through non-GGIR R
packages, implementing several data and feature quality checks, merging all
features of PA/SL/CR together, and performing multiple analyses including Joint
Individual Variation Explained (JIVE), an unsupervised machine learning
dimension reduction technique that identifies latent factors capturing joint
across and individual to each of three domains of PA/SL/CR. In detail, the
pipeline generates all necessary R/Rmd/shell files for data processing after
running GGIR for accelerometer data.  In module 1, all csv files in the GGIR
output directory were read, transformed and then merged.  In module 2, the GGIR
output files were checked and summarized in one excel sheet.  In module 3, the
merged data was cleaned according to the number of valid hours on each night and
the number of valid days for each subject.  In module 4, the cleaned activity
data was imputed by the average Euclidean norm minus one (ENMO) over all the
valid days for each subject.  Finally, a comprehensive report of data processing
was created using Rmarkdown, and the report includes few exploratory plots and
multiple commonly used features extracted from minute level actigraphy data.
Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M,
Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in
Motor Activity Research Consortium for Health (@code{mMARCH}) Journal for the
Measurement of Physical Behaviour, 6(1): 37-44.")
    (license license:gpl3)))

(define-public r-mmaqshiny
  (package
    (name "r-mmaqshiny")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmaqshiny" version))
       (sha256
        (base32 "0v44hd7h9d8hdd2avwwwwc0a8rzm5ij3yhryxkrw7bwvr12zsp17"))))
    (properties `((upstream-name . "mmaqshiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-xml
                             r-stringr
                             r-shinyjs
                             r-shiny
                             r-plotly
                             r-lubridate
                             r-leaflet
                             r-htmltools
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-data-table
                             r-catools
                             r-cairo))
    (home-page "https://github.com/meenakshi-kushwaha/mmaqshiny")
    (synopsis "Explore Air-Quality Mobile-Monitoring Data")
    (description
     "Mobile-monitoring or \"sensors on a mobile platform\", is an increasingly popular
approach to measure high-resolution pollution data at the street level.  Coupled
with location data, spatial visualisation of air-quality parameters helps detect
localized areas of high air-pollution, also called hotspots.  In this approach,
portable sensors are mounted on a vehicle and driven on predetermined routes to
collect high frequency data (1 Hz).  mmaqshiny is for analysing, visualising and
spatial mapping of high-resolution air-quality data collected by specific
devices installed on a moving platform.  1 Hz data of PM2.5 (mass concentrations
of particulate matter with size less than 2.5 microns), Black carbon mass
concentrations (BC), ultra-fine particle number concentrations, carbon dioxide
along with GPS coordinates and relative humidity (RH) data collected by popular
portable instruments (TSI @code{DustTrak-8530}, Aethlabs @code{microAeth-AE51},
TSI CPC3007, LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively).
It incorporates device specific cleaning and correction algorithms.  RH
correction is applied to @code{DustTrak} PM2.5 following the Chakrabarti et al.,
(2004) <doi:10.1016/j.atmosenv.2004.03.007>.  Provision is given to add linear
regression coefficients for correcting the PM2.5 data (if required).  BC data
will be cleaned for the vibration generated noise, by adopting the statistical
procedure as explained in Apte et al., (2011)
<doi:10.1016/j.atmosenv.2011.05.028>, followed by a loading correction as
suggested by Ban-Weiss et al., (2009) <doi:10.1021/es8021039>.  For the number
concentration data, provision is given for dilution correction factor (if a
diluter is used with CPC3007; default value is 1).  The package joins the raw,
cleaned and corrected data from the above said instruments and outputs as a
downloadable csv file.")
    (license license:expat)))

(define-public r-mmapcharr
  (package
    (name "r-mmapcharr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmapcharr" version))
       (sha256
        (base32 "175qb6ay6zlcdcxzc86lmlddfzjc8j8i1b8cvi2q0j6nbl2kl50r"))))
    (properties `((upstream-name . "mmapcharr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmio r-rcpp))
    (home-page "https://github.com/privefl/mmapcharr")
    (synopsis "Memory-Map Character Files")
    (description
     "Uses memory-mapping to enable the random access of elements of a text file of
characters separated by characters as if it were a simple R(cpp) matrix.")
    (license license:gpl3)))

(define-public r-mmap
  (package
    (name "r-mmap")
    (version "0.6-22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmap" version))
       (sha256
        (base32 "0v1qniz0jdp6fay93plv5j200s1pybyyxpfjhgcdjzm7rag8d1jd"))))
    (properties `((upstream-name . "mmap")))
    (build-system r-build-system)
    (native-inputs (list))
    (home-page "https://cran.r-project.org/package=mmap")
    (synopsis "Map Pages of Memory")
    (description
     "R interface to POSIX mmap and Window's @code{MapViewOfFile}.")
    (license license:gpl3)))

(define-public r-mmand
  (package
    (name "r-mmand")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmand" version))
       (sha256
        (base32 "18bl3s0x0rm3yg7zjamhl6rn2bknxg75ilqgi28ikzbrp2k5sdzw"))))
    (properties `((upstream-name . "mmand")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/jonclayden/mmand")
    (synopsis "Mathematical Morphology in Any Number of Dimensions")
    (description
     "This package provides tools for performing mathematical morphology operations,
such as erosion and dilation, on data of arbitrary dimensionality.  Can also be
used for finding connected components, resampling, filtering, smoothing and
other image processing-style operations.")
    (license license:gpl2)))

(define-public r-mmad
  (package
    (name "r-mmad")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMAD" version))
       (sha256
        (base32 "0587fribpy44xz8dflc3rhp861lh394nmdb25nbk826hb0azzqxx"))))
    (properties `((upstream-name . "MMAD")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=MMAD")
    (synopsis "MM Algorithm Based on the Assembly-Decomposition Technology")
    (description
     "The Minorize-Maximization(MM) algorithm based on Assembly-Decomposition(AD)
technology can be used for model estimation of parametric models,
semi-parametric models and non-parametric models.  We selected parametric models
including left truncated normal distribution, type I multivariate zero-inflated
generalized poisson distribution and multivariate compound zero-inflated
generalized poisson distribution; semiparametric models include Cox model and
gamma frailty model; nonparametric model is estimated for type II
interval-censored data.  These general methods are proposed based on the
following papers, Tian, Huang and Xu (2019) <doi:10.5705/SS.202016.0488>, Huang,
Xu and Tian (2019) <doi:10.5705/ss.202016.0516>, Zhang and Huang (2022)
<doi:10.1117/12.2642737>.")
    (license license:gpl3+)))

(define-public r-mmac
  (package
    (name "r-mmac")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MMAC" version))
       (sha256
        (base32 "1xwz48iizf0lvmhc7krsraabc49ky85qvgr23w4ip6jzfv4sn1jr"))))
    (properties `((upstream-name . "MMAC")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MMAC")
    (synopsis "Data for Mathematical Modeling and Applied Calculus")
    (description
     "This package contains the data sets for the textbook \"Mathematical Modeling and
Applied Calculus\" by Joel Kilty and Alex M. @code{McAllister}.  The book will be
published by Oxford University Press in 2018 with ISBN-13: 978-019882472.")
    (license license:gpl2+)))

(define-public r-mmabig
  (package
    (name "r-mmabig")
    (version "3.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mmabig" version))
       (sha256
        (base32 "1ji832vs9gfzpl1ij85dgmkpqr3qy4p7r6kvrk0y3q3agjxw1gky"))))
    (properties `((upstream-name . "mmabig")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mma r-gplots r-glmnet r-car))
    (native-inputs (list r-knitr))
    (home-page "https://www.r-project.org")
    (synopsis "Multiple Mediation Analysis for Big Data Sets")
    (description
     "Used for general multiple mediation analysis with big data sets.")
    (license license:gpl2+)))

(define-public r-mma
  (package
    (name "r-mma")
    (version "10.7-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mma" version))
       (sha256
        (base32 "0zqkqzi27qg4q6mirhw5gzjbsd9i06rxzr61zmmsw33wg5a6lgd3"))))
    (properties `((upstream-name . "mma")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-lattice r-gplots r-gbm r-car))
    (native-inputs (list r-knitr))
    (home-page "https://www.r-project.org")
    (synopsis "Multiple Mediation Analysis")
    (description
     "Used for general multiple mediation analysis.  The analysis method is described
in Yu and Li (2022) (ISBN: 9780367365479) \"Statistical Methods for Mediation,
Confounding and Moderation Analysis Using R and SAS\", published by Chapman and
Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> \"Exploring racial
disparity in obesity: a mediation analysis considering geo-coded environmental
factors\", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.")
    (license license:gpl2+)))

(define-public r-mm4lmm
  (package
    (name "r-mm4lmm")
    (version "3.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MM4LMM" version))
       (sha256
        (base32 "02a553pncv7q0zczy6rvj4avjpryv01n2mhr9cr0ir7607k1jllx"))))
    (properties `((upstream-name . "MM4LMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-matrix
                             r-mass
                             r-dplyr
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=MM4LMM")
    (synopsis "Inference of Linear Mixed Models Through MM Algorithm")
    (description
     "The main function MMEst() performs (Restricted) Maximum Likelihood in a variance
component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset,
A. & Mary-Huard, T. (2022) <doi:10.1371/journal.pcbi.1009659>).")
    (license license:gpl2+)))

(define-public r-mm2sdata
  (package
    (name "r-mm2sdata")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MM2Sdata" version))
       (sha256
        (base32 "0hlm6jp8jq38vkszwdgpf30w11l1fhpblg26zv05j50a6whpmhqd"))))
    (properties `((upstream-name . "MM2Sdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-biobase))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MM2Sdata")
    (synopsis "Gene Expression Datasets for the 'MM2S' Package")
    (description
     "Gene Expression datasets for the MM2S package.  Contains normalized expression
data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma
models ('GSE36594').  Deena Gendoo et al. (2015)
<doi:10.1016/j.ygeno.2015.05.002>.")
    (license license:gpl3)))

(define-public r-mm
  (package
    (name "r-mm")
    (version "1.6-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MM" version))
       (sha256
        (base32 "04fr8vxjc7kgpxrlk7q5ibad00ff8fafl14l36snhbszv0zv4n8k"))))
    (properties `((upstream-name . "MM")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadform
                             r-partitions
                             r-oarray
                             r-mathjaxr
                             r-magic
                             r-abind))
    (home-page "https://github.com/RobinHankin/MM")
    (synopsis "The Multiplicative Multinomial Distribution")
    (description
     "Various utilities for the Multiplicative Multinomial distribution.")
    (license license:gpl2)))

(define-public r-mlz
  (package
    (name "r-mlz")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLZ" version))
       (sha256
        (base32 "1kaax2qz6kiqh6c9qmsf6n431ippdpvg720vlfywxyhw2v1qpgdy"))))
    (properties `((upstream-name . "MLZ")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-reshape2
                             r-rcppeigen
                             r-gplots
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MLZ")
    (synopsis "Mean Length-Based Estimators of Mortality using TMB")
    (description
     "Estimation functions and diagnostic tools for mean length-based total mortality
estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.")
    (license license:gpl2)))

(define-public r-mlxr
  (package
    (name "r-mlxr")
    (version "4.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlxR" version))
       (sha256
        (base32 "0pr564376q7pn0wbbw5q9mjfzdc0bpq1g4xj0bcdmb64binqllvb"))))
    (properties `((upstream-name . "mlxR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "http://simulx.webpopix.org")
    (synopsis "Simulation of Longitudinal Data")
    (description
     "Simulation and visualization of complex models for longitudinal data.  The
models are encoded using the model coding language Mlxtran and automatically
converted into C++ codes.  That allows one to implement very easily complex
ODE-based models and complex statistical models, including mixed effects models,
for continuous, count, categorical, and time-to-event data.")
    (license license:bsd-2)))

(define-public r-mlvsbm
  (package
    (name "r-mlvsbm")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLVSBM" version))
       (sha256
        (base32 "1jsaas7crdc3df790c279a5qr8iw63whmkijs86s1y6gyz4qk9vf"))))
    (properties `((upstream-name . "MLVSBM")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-magrittr r-cluster r-blockmodels r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Chabert-Liddell/MLVSBM")
    (synopsis "Stochastic Block Model for Multilevel Networks")
    (description
     "Simulation, inference and clustering of multilevel networks using a Stochastic
Block Model framework as described in Chabert-Liddell, Barbillon, Donnet and
Lazega (2021) <doi:10.1016/j.csda.2021.107179>.  A multilevel network is defined
as the junction of two interaction networks, the upper level or
inter-organizational level and the lower level or inter-individual level.  The
inter-level represents an affiliation relationship.")
    (license license:gpl3)))

(define-public r-mlvar
  (package
    (name "r-mlvar")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlVAR" version))
       (sha256
        (base32 "0xvkhbjkzrvg9x5rd1yn9kzk27f4khwil1837l83jyd33hlyfsj3"))))
    (properties `((upstream-name . "mlVAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-qgraph
                             r-plyr
                             r-mvtnorm
                             r-mplusautomation
                             r-lme4
                             r-graphicalvar
                             r-dplyr
                             r-corpcor
                             r-clustergeneration
                             r-arm
                             r-abind))
    (home-page "https://cran.r-project.org/package=mlVAR")
    (synopsis "Multi-Level Vector Autoregression")
    (description
     "Estimates the multi-level vector autoregression model on time-series data.
Three network structures are obtained: temporal networks, contemporaneous
networks and between-subjects networks.")
    (license license:gpl2)))

(define-public r-mlts
  (package
    (name "r-mlts")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlts" version))
       (sha256
        (base32 "017kl04f70iglrpzxc24kl4v65rf3lqi3fy0wdhnaffr0bf7qyri"))))
    (properties `((upstream-name . "mlts")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rmarkdown
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-pdftools
                             r-mvtnorm
                             r-ggplot2
                             r-dplyr
                             r-cowplot
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/munchfab/mlts")
    (synopsis "Multilevel Latent Time Series Models with 'R' and 'Stan'")
    (description
     "Fit multilevel manifest or latent time-series models, including popular Dynamic
Structural Equation Models (DSEM).  The models can be set up and modified with
user-friendly functions and are fit to the data using Stan for Bayesian
inference.  Path models and formulas for user-defined models can be easily
created with functions using knitr'.  Asparouhov, Hamaker, & Muthen (2018)
<doi:10.1080/10705511.2017.1406803>.")
    (license license:gpl3+)))

(define-public r-mltools
  (package
    (name "r-mltools")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mltools" version))
       (sha256
        (base32 "045v28w7vz1zjxim8vfc6ncvg2mavr1q332x0hzlqpfzrk7gz4vh"))))
    (properties `((upstream-name . "mltools")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-data-table))
    (home-page "https://github.com/ben519/mltools")
    (synopsis "Machine Learning Tools")
    (description
     "This package provides a collection of machine learning helper functions,
particularly assisting in the Exploratory Data Analysis phase.  Makes heavy use
of the data.table package for optimal speed and memory efficiency.  Highlights
include a versatile bin_data() function, sparsify() for converting a data.table
to sparse matrix format with one-hot encoding, fast evaluation metrics, and
empirical_cdf() for calculating empirical Multivariate Cumulative Distribution
Functions.")
    (license license:expat)))

(define-public r-mltest
  (package
    (name "r-mltest")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mltest" version))
       (sha256
        (base32 "14gyssfph088v936mpywmd4y8z9vdv0zk8638vlg23j8cf5j4al2"))))
    (properties `((upstream-name . "mltest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mltest")
    (synopsis "Classification Evaluation Metrics")
    (description
     "This package provides a fast, robust and easy-to-use calculation of multi-class
classification evaluation metrics based on confusion matrix.")
    (license license:gpl2)))

(define-public r-mlt-docreg
  (package
    (name "r-mlt-docreg")
    (version "1.1-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlt.docreg" version))
       (sha256
        (base32 "0sgrzaqadi9nld3dap8rf3ykjhlc7jrcvqvsihr3pn5x68vn06ig"))))
    (properties `((upstream-name . "mlt.docreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncreg
                             r-survival
                             r-numderiv
                             r-multcomp
                             r-mlt
                             r-lattice
                             r-flexsurv
                             r-eha))
    (native-inputs (list r-knitr))
    (home-page "http://ctm.R-forge.R-project.org")
    (synopsis
     "Most Likely Transformations: Documentation and Regression Tests")
    (description
     "Additional documentation, a package vignette and regression tests for package
mlt.")
    (license license:gpl2)))

(define-public r-mlt
  (package
    (name "r-mlt")
    (version "1.6-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlt" version))
       (sha256
        (base32 "0d0ca5ap0lh69fgqri9cjlw52ca9z582bvmq843fi686cplrkv9w"))))
    (properties `((upstream-name . "mlt")))
    (build-system r-build-system)
    (propagated-inputs (list r-variables
                             r-survival
                             r-sandwich
                             r-numderiv
                             r-nloptr
                             r-mvtnorm
                             r-matrix
                             r-coneproj
                             r-bb
                             r-basefun
                             r-alabama))
    (home-page "http://ctm.R-forge.R-project.org")
    (synopsis "Most Likely Transformations")
    (description
     "Likelihood-based estimation of conditional transformation models via the most
likely transformation approach described in Hothorn et al. (2018)
<DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>.
Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and
multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models
are part of this package.")
    (license license:gpl2)))

(define-public r-mlsurvlrnrs
  (package
    (name "r-mlsurvlrnrs")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlsurvlrnrs" version))
       (sha256
        (base32 "11yp41fvmr2ij3sqky5hjzblqsdwsywyn3r62cdld5b5fiv487hg"))))
    (properties `((upstream-name . "mlsurvlrnrs")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-mllrnrs r-mlexperiments r-kdry
                             r-data-table))
    (native-inputs (list r-quarto))
    (home-page "https://github.com/kapsner/mlsurvlrnrs")
    (synopsis "R6-Based ML Survival Learners for 'mlexperiments'")
    (description
     "Enhances mlexperiments <https://CRAN.R-project.org/package=mlexperiments> with
additional machine learning ('ML') learners for survival analysis.  The package
provides R6-based survival learners for the following algorithms: glmnet
<https://CRAN.R-project.org/package=glmnet>, ranger
<https://CRAN.R-project.org/package=ranger>, xgboost
<https://CRAN.R-project.org/package=xgboost>, and rpart
<https://CRAN.R-project.org/package=rpart>.  These can be used directly with the
mlexperiments R package.")
    (license license:gpl3+)))

(define-public r-mlstropalr
  (package
    (name "r-mlstropalr")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlstrOpalr" version))
       (sha256
        (base32 "11h9i5yljd7k68rz1gj131yzv6idj7ji3fya7kc7rgpinivqxnxa"))))
    (properties `((upstream-name . "mlstrOpalr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-opalr
                             r-madshapr
                             r-fabr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/maelstrom-research/mlstrOpalr")
    (synopsis
     "Support Compatibility Between 'Maelstrom' R Packages and 'Opal' Environment")
    (description
     "This package provides functions to support compatibility between Maelstrom R
packages and Opal environment.  Opal is the O@code{BiBa} core database
application for biobanks.  It is used to build data repositories that integrates
data collected from multiple sources.  Opal Maelstrom is a specific
implementation of this software.  This Opal client is specifically designed to
interact with Opal Maelstrom distributions to perform operations on the R server
side.  The user must have adequate credentials.  Please see
<https://opaldoc.obiba.org/> for complete documentation.")
    (license license:gpl3)))

(define-public r-mlsjunkgen
  (package
    (name "r-mlsjunkgen")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlsjunkgen" version))
       (sha256
        (base32 "1icsssgi5xf420ajagr9nw0wgbxpfm7dsd5b7s77vyh5hsfzm1z3"))))
    (properties `((upstream-name . "mlsjunkgen")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://stevemyles.site/mlsjunkgen/")
    (synopsis
     "Use the MLS Junk Generator Algorithm to Generate a Stream of Pseudo-Random Numbers")
    (description
     "Generate a stream of pseudo-random numbers generated using the MLS Junk
Generator algorithm.  Functions exist to generate single pseudo-random numbers
as well as a vector, data frame, or matrix of pseudo-random numbers.")
    (license license:expat)))

(define-public r-mlsbm
  (package
    (name "r-mlsbm")
    (version "0.99.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlsbm" version))
       (sha256
        (base32 "1p6fflhjbbyzx51hb77aak1kdabjrmzd7b47vdcanhb0vd263aby"))))
    (properties `((upstream-name . "mlsbm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=mlsbm")
    (synopsis "Efficient Estimation of Bayesian SBMs & MLSBMs")
    (description
     "Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block
models (MLSBMs) using efficient Gibbs sampling implemented in Rcpp'.  The models
assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary
edges.  Data are input as a symmetric binary adjacency matrix (SBMs), or list of
such matrices (MLSBMs).")
    (license license:gpl2+)))

(define-public r-mlrv
  (package
    (name "r-mlrv")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlrv" version))
       (sha256
        (base32 "1q3xjbdl729q5l2iljyj4m7k04lg1i0vg0vm8s3qlkpjidazxd5r"))))
    (properties `((upstream-name . "mlrv")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-mathjaxr
                             r-magrittr
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mlrv")
    (synopsis "Long-Run Variance Estimation in Time Series Regression")
    (description
     "Plug-in and difference-based long-run covariance matrix estimation for time
series regression.  Two applications of hypothesis testing are also provided.
The first one is for testing for structural stability in coefficient functions.
The second one is aimed at detecting long memory in time series regression.
Lujia Bai and Weichi Wu (2024)<doi:10.3150/23-BEJ1680> Zhou Zhou and Wei Biao
Wu(2010)<doi:10.1111/j.1467-9868.2010.00743.x> Jianqing Fan and Wenyang
Zhang<doi:10.1214/aos/1017939139> Lujia Bai and Weichi
Wu(2024)<doi:10.1093/biomet/asae013> Dimitris N. Politis, Joseph P. Romano,
Michael Wolf(1999)<doi:10.1007/978-1-4612-1554-7> Weichi Wu and Zhou
Zhou(2018)<doi:10.1214/17-AOS1582>.")
    (license license:expat)))

(define-public r-mlrpro
  (package
    (name "r-mlrpro")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlrpro" version))
       (sha256
        (base32 "1j8w8ji1n6cahcbyi1q4zq8gmhpcl15pb97f7bl1km29f4x4f272"))))
    (properties `((upstream-name . "mlrpro")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-dplyr r-car))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mlrpro")
    (synopsis "Stepwise Regression with Assumptions Checking")
    (description
     "The stepwise regression with assumptions checking and the possible Box-Cox
transformation.")
    (license license:gpl3)))

(define-public r-mlrintermbo
  (package
    (name "r-mlrintermbo")
    (version "0.5.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlrintermbo" version))
       (sha256
        (base32 "0hghnpsh6jm3vghn03v483cmqin2wxsvcahhzjg6x0wdszhmskhv"))))
    (properties `((upstream-name . "mlrintermbo")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-paradox
                             r-mlr3tuning
                             r-mlr3misc
                             r-lhs
                             r-data-table
                             r-checkmate
                             r-callr
                             r-bbotk
                             r-backports))
    (home-page "https://github.com/mb706/mlrintermbo")
    (synopsis "Model-Based Optimization for 'mlr3' Through 'mlrMBO'")
    (description
     "The @code{mlrMBO} package can ordinarily not be used for optimization within
mlr3', because of incompatibilities of their respective class systems.
mlrintermbo offers a compatibility interface that provides @code{mlrMBO} as an
mlr3tuning Tuner object, for tuning of machine learning algorithms within mlr3',
as well as a bbotk Optimizer object for optimization of general objective
functions using the bbotk black box optimization framework.  The control
parameters of @code{mlrMBO} are faithfully reproduced as a paradox
@code{ParamSet}'.")
    (license license:lgpl3)))

(define-public r-mlrcpo
  (package
    (name "r-mlrcpo")
    (version "0.3.7-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlrCPO" version))
       (sha256
        (base32 "0nia2f8j5vgiradlcqi618mlv7146ml2pvbzh4p89p1pk75p6v0l"))))
    (properties `((upstream-name . "mlrCPO")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-paramhelpers
                             r-mlr
                             r-checkmate
                             r-bbmisc
                             r-backports))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mlr-org/mlrCPO")
    (synopsis
     "Composable Preprocessing Operators and Pipelines for Machine Learning")
    (description
     "Toolset that enriches mlr with a diverse set of preprocessing operators.
Composable Preprocessing Operators (\"CPO\"s) are first-class R objects that can
be applied to data.frames and mlr \"Task\"s to modify data, can be attached to mlr
\"Learner\"s to add preprocessing to machine learning algorithms, and can be
composed to form preprocessing pipelines.")
    (license license:bsd-2)))

(define-public r-mlr3torch
  (package
    (name "r-mlr3torch")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3torch" version))
       (sha256
        (base32 "1q6jdyy0jyvjg3hab0i77p53iznx4z2hhxzx1rdj82n3q61wi8v2"))))
    (properties `((upstream-name . "mlr3torch")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-torch
                             r-r6
                             r-paradox
                             r-mlr3pipelines
                             r-mlr3misc
                             r-mlr3
                             r-lgr
                             r-data-table
                             r-checkmate
                             r-backports))
    (home-page "https://mlr3torch.mlr-org.com/")
    (synopsis "Deep Learning with 'mlr3'")
    (description
     "Deep Learning library that extends the mlr3 framework by building upon the torch
package.  It allows to conveniently build, train, and evaluate deep learning
models without having to worry about low level details.  Custom architectures
can be created using the graph language defined in mlr3pipelines'.")
    (license license:lgpl3+)))

(define-public r-mlr3superlearner
  (package
    (name "r-mlr3superlearner")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3superlearner" version))
       (sha256
        (base32 "1dvqkfijl3r87smx46x4ycjmqzprk45pmkd5gf538jmkrjg3rid0"))))
    (properties `((upstream-name . "mlr3superlearner")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr
                             r-mlr3learners
                             r-mlr3
                             r-lgr
                             r-glmnet
                             r-data-table
                             r-cli
                             r-checkmate))
    (home-page "https://cran.r-project.org/package=mlr3superlearner")
    (synopsis "Super Learner Fitting and Prediction")
    (description
     "An implementation of the Super Learner prediction algorithm from van der Laan,
Polley, and Hubbard (2007) <doi:10.2202/1544-6115.1309 using the mlr3 framework.")
    (license license:gpl3+)))

(define-public r-mlr3summary
  (package
    (name "r-mlr3summary")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3summary" version))
       (sha256
        (base32 "16r9sjjjy3f3aqjw1npibqk0pwhdajh6r3rkrypjfl80mhk9fpi4"))))
    (properties `((upstream-name . "mlr3summary")))
    (build-system r-build-system)
    (propagated-inputs (list r-mlr3misc
                             r-mlr3
                             r-future-apply
                             r-data-table
                             r-cli
                             r-checkmate
                             r-backports))
    (home-page "https://cran.r-project.org/package=mlr3summary")
    (synopsis "Model and Learner Summaries for 'mlr3'")
    (description
     "Concise and interpretable summaries for machine learning models and learners of
the mlr3 ecosystem.  The package takes inspiration from the summary function for
(generalized) linear models but extends it to non-parametric machine learning
models, based on generalization performance, model complexity, feature
importances and effects, and fairness metrics.")
    (license license:lgpl3)))

(define-public r-mlr3spatiotempcv
  (package
    (name "r-mlr3spatiotempcv")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3spatiotempcv" version))
       (sha256
        (base32 "04n5bzppkj9fmdhd54qk8738id0sfd3ij7p31l3hxmjbak5inh83"))))
    (properties `((upstream-name . "mlr3spatiotempcv")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-paradox
                             r-mlr3misc
                             r-mlr3
                             r-ggplot2
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://mlr3spatiotempcv.mlr-org.com/")
    (synopsis "Spatiotemporal Resampling Methods for 'mlr3'")
    (description
     "Extends the mlr3 ML framework with spatio-temporal resampling methods to account
for the presence of spatiotemporal autocorrelation (STAC) in predictor
variables.  STAC may cause highly biased performance estimates in
cross-validation if ignored.")
    (license license:lgpl3)))

(define-public r-mlr3spatial
  (package
    (name "r-mlr3spatial")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3spatial" version))
       (sha256
        (base32 "105wscgkrlgckrmisr1b5xf8wlj9w2w7ir8w1280isyg2zp1gva8"))))
    (properties `((upstream-name . "mlr3spatial")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sf
                             r-r6
                             r-mlr3misc
                             r-mlr3
                             r-lgr
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://mlr3spatial.mlr-org.com")
    (synopsis "Support for Spatial Objects Within the 'mlr3' Ecosystem")
    (description
     "Extends the mlr3 ML framework with methods for spatial objects.  Data storage
and prediction are supported for packages terra', raster and stars'.")
    (license license:lgpl3)))

(define-public r-mlr3shiny
  (package
    (name "r-mlr3shiny")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3shiny" version))
       (sha256
        (base32 "0rv5ybg6l1n7j6gq8wmvl1gd1mdmqd52vl91hkpv9k1l1w8m8mph"))))
    (properties `((upstream-name . "mlr3shiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-shinywidgets
                             r-shinyjs
                             r-shinydashboard
                             r-shinyalert
                             r-shiny
                             r-purrr
                             r-plyr
                             r-patchwork
                             r-mlr3pipelines
                             r-mlr3measures
                             r-mlr3learners
                             r-mlr3
                             r-metrics
                             r-dt
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=mlr3shiny")
    (synopsis "Machine Learning in 'shiny' with 'mlr3'")
    (description
     "This package provides a web-based graphical user interface to provide the basic
steps of a machine learning workflow.  It uses the functionalities of the mlr3
framework.")
    (license license:bsd-2)))

(define-public r-mlr3resampling
  (package
    (name "r-mlr3resampling")
    (version "2024.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3resampling" version))
       (sha256
        (base32 "0mw30yhin2j4r695xn14mnnsyf7mrih96r6vx4dycdpq6lzrprj7"))))
    (properties `((upstream-name . "mlr3resampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-paradox
                             r-mlr3misc
                             r-mlr3
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tdhock/mlr3resampling")
    (synopsis "Resampling Algorithms for 'mlr3' Framework")
    (description
     "This package provides a supervised learning algorithm inputs a train set, and
outputs a prediction function, which can be used on a test set.  If each data
point belongs to a group (such as geographic region, year, etc), then how do we
know if it is possible to train on one group, and predict accurately on another
group? Cross-validation can be used to determine the extent to which this is
possible, by first assigning fold IDs from 1 to K to all data (possibly using
stratification, usually by group and label).  Then we loop over test sets
(group/fold combinations), train sets (same group, other groups, all groups),
and compute test/prediction accuracy for each combination.  Comparing
test/prediction accuracy between same and other, we can determine the extent to
which it is possible (perfect if same/other have similar test accuracy for each
group; other is usually somewhat less accurate than same; other can be just as
bad as featureless baseline when the groups have different patterns).  For more
information, <https://tdhock.github.io/blog/2023/R-gen-new-subsets/> describes
the method in depth.  How many train samples are required to get accurate
predictions on a test set? Cross-validation can be used to answer this question,
with variable size train sets.")
    (license license:gpl3)))

(define-public r-mlr3oml
  (package
    (name "r-mlr3oml")
    (version "0.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3oml" version))
       (sha256
        (base32 "0qlgqq22zy5kdgp6l6fg3yx8ywxpy4yaa2567j9z1pg5ij9yd49h"))))
    (properties `((upstream-name . "mlr3oml")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-uuid
                             r-stringi
                             r-r6
                             r-paradox
                             r-mlr3misc
                             r-mlr3
                             r-lgr
                             r-jsonlite
                             r-data-table
                             r-curl
                             r-checkmate
                             r-bit64
                             r-backports))
    (home-page "https://mlr3oml.mlr-org.com")
    (synopsis "Connector Between 'mlr3' and 'OpenML'")
    (description
     "This package provides an interface to @code{OpenML.org} to list and download
machine learning data, tasks and experiments.  The @code{OpenML} objects can be
automatically converted to mlr3 objects.  For a more sophisticated interface
with more upload options, see the @code{OpenML} package.")
    (license license:lgpl3)))

(define-public r-mlr3fda
  (package
    (name "r-mlr3fda")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3fda" version))
       (sha256
        (base32 "0y086z58zbazcxcdqk82rvfs1yifah6y4ps3vd679n09z08wg3mm"))))
    (properties `((upstream-name . "mlr3fda")))
    (build-system r-build-system)
    (propagated-inputs (list r-tf
                             r-r6
                             r-paradox
                             r-mlr3pipelines
                             r-mlr3misc
                             r-mlr3
                             r-lgr
                             r-data-table
                             r-checkmate))
    (home-page "https://mlr3fda.mlr-org.com")
    (synopsis "Extending 'mlr3' to Functional Data Analysis")
    (description
     "Extends the mlr3 ecosystem to functional analysis by adding support for
irregular and regular functional data as defined in the tf package.  The package
provides @code{PipeOps} for preprocessing functional columns and for extracting
scalar features, thereby allowing standard machine learning algorithms to be
applied afterwards.  Available operations include simple functional features
such as the mean or maximum, smoothing, interpolation, flattening, and
functional PCA'.")
    (license license:lgpl3)))

(define-public r-mlr3fairness
  (package
    (name "r-mlr3fairness")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3fairness" version))
       (sha256
        (base32 "0rm6l50prwjjy55p14zs9mkdrczbyl7f63fqsmh7r5xjahcsnfi7"))))
    (properties `((upstream-name . "mlr3fairness")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-r6
                             r-paradox
                             r-mlr3pipelines
                             r-mlr3misc
                             r-mlr3measures
                             r-mlr3learners
                             r-mlr3
                             r-ggplot2
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://mlr3fairness.mlr-org.com")
    (synopsis "Fairness Auditing and Debiasing for 'mlr3'")
    (description
     "Integrates fairness auditing and bias mitigation methods for the mlr3 ecosystem.
 This includes fairness metrics, reporting tools, visualizations and bias
mitigation techniques such as \"Reweighing\" described in Kamiran, Calders (2012)
<doi:10.1007/s10115-011-0463-8> and \"Equalized Odds\" described in Hardt et al.
(2016)
<https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
 Integration with mlr3 allows for auditing of ML models as well as convenient
joint tuning of machine learning algorithms and debiasing methods.")
    (license license:lgpl3)))

(define-public r-mlr3db
  (package
    (name "r-mlr3db")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3db" version))
       (sha256
        (base32 "1cq22h9yj27ighh4clyak1xwx2wb5v4803hd7lrhnlgzs85dmhj2"))))
    (properties `((upstream-name . "mlr3db")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-mlr3misc
                             r-mlr3
                             r-data-table
                             r-checkmate
                             r-backports))
    (home-page "https:///mlr3db.mlr-org.com")
    (synopsis "Data Base Backend for 'mlr3'")
    (description
     "Extends the mlr3 package with a backend to transparently work with databases
such as SQLite', @code{DuckDB}', @code{MySQL}', @code{MariaDB}', or
@code{PostgreSQL}'.  The package provides two additional backends:
@code{DataBackendDplyr} relies on the abstraction of package dbplyr to interact
with most DBMS. @code{DataBackendDuckDB} operates on @code{DuckDB} data bases
and also on Apache Parquet files.")
    (license license:lgpl3)))

(define-public r-mlr3benchmark
  (package
    (name "r-mlr3benchmark")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3benchmark" version))
       (sha256
        (base32 "1bhn0lm0k9dm9mbji6cwgqkwsa1qg928fbjs8chra9s3d3bzcb0x"))))
    (properties `((upstream-name . "mlr3benchmark")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-mlr3misc r-ggplot2 r-data-table
                             r-checkmate))
    (home-page "https://mlr3benchmark.mlr-org.com")
    (synopsis "Analysis and Visualisation of Benchmark Experiments")
    (description
     "This package implements methods for post-hoc analysis and visualisation of
benchmark experiments, for mlr3 and beyond.")
    (license license:lgpl3)))

(define-public r-mlr3batchmark
  (package
    (name "r-mlr3batchmark")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlr3batchmark" version))
       (sha256
        (base32 "1qirzjmb17y1rzq69x1lpl56qk5w63zjq30if97vfab3spsd2ilf"))))
    (properties `((upstream-name . "mlr3batchmark")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid
                             r-mlr3misc
                             r-mlr3
                             r-lgr
                             r-data-table
                             r-checkmate
                             r-batchtools))
    (home-page "https:///mlr3batchmark.mlr-org.com")
    (synopsis "Batch Experiments for 'mlr3'")
    (description
     "Extends the mlr3 package with a connector to the package batchtools'.  This
allows to run large-scale benchmark experiments on scheduled high-performance
computing clusters.")
    (license license:lgpl3)))

(define-public r-mlquantify
  (package
    (name "r-mlquantify")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlquantify" version))
       (sha256
        (base32 "1fpwvix0f4bmqb1y1b6wryndqp6j24dwsspbfx1zrnhzwj1v4i9i"))))
    (properties `((upstream-name . "mlquantify")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-fnn r-caret))
    (home-page "https://github.com/andregustavom/mlquantify")
    (synopsis "Algorithms for Class Distribution Estimation")
    (description
     "Quantification is a prominent machine learning task that has received an
increasing amount of attention in the last years.  The objective is to predict
the class distribution of a data sample.  This package is a collection of
machine learning algorithms for class distribution estimation.  This package
include algorithms from different paradigms of quantification.  These methods
are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista.
 The importance of the test set size in quantification assessment.  In
Proceedings of the Twenty-Ninth International Joint Conference on Artificial
Intelligence, IJCAI20, pages 2640â2646, 2020. <doi:10.24963/ijcai.2020/366>.")
    (license license:gpl2+)))

(define-public r-mlpwr
  (package
    (name "r-mlpwr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlpwr" version))
       (sha256
        (base32 "0pxlqmpsyzb0s5h0mj16xx09xkvjc7h6r8a6nbk8rk8rbmzxszl0"))))
    (properties `((upstream-name . "mlpwr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist
                             r-rgenoud
                             r-randtoolbox
                             r-ggplot2
                             r-digest
                             r-dicekriging))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/flxzimmer/mlpwr")
    (synopsis "Power Analysis Toolbox to Find Cost-Efficient Study Designs")
    (description
     "We implement a surrogate modeling algorithm to guide simulation-based sample
size planning.  The method is described in detail in our paper (Zimmer & Debelak
(2023) <doi:10.1037/met0000611>).  It supports multiple study design parameters
and optimization with respect to a cost function.  It can find optimal designs
that correspond to a desired statistical power or that fulfill a cost
constraint.  We also provide a tutorial paper (Zimmer et al. (2023)
<doi:10.3758/s13428-023-02269-0>).")
    (license license:gpl3+)))

(define-public r-mlpugs
  (package
    (name "r-mlpugs")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLPUGS" version))
       (sha256
        (base32 "1yda37073g640g1qrkjrwa8hbyq6bjlcw3wpznx0bk5h0639bc0s"))))
    (properties `((upstream-name . "MLPUGS")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bearloga/MLPUGS")
    (synopsis
     "Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)")
    (description
     "An implementation of classifier chains (CC's) for multi-label prediction.  Users
can employ an external package (e.g. @code{randomForest}', C50'), or supply
their own.  The package can train a single set of CC's or train an ensemble of
CC's -- in parallel if running in a multi-core environment.  New observations
are classified using a Gibbs sampler since each unobserved label is conditioned
on the others.  The package includes methods for evaluating the predictions for
accuracy and aggregating across iterations and models to produce binary or
probabilistic classifications.")
    (license license:expat)))

(define-public r-mlpreemption
  (package
    (name "r-mlpreemption")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLpreemption" version))
       (sha256
        (base32 "0fvrl7ahaiv93sq637yhf18j8bf3w28f0l4b8sjs2ssc5pbg1448"))))
    (properties `((upstream-name . "MLpreemption")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis "Maximum Likelihood Estimation of the Niche Preemption Model")
    (description
     "This package provides functions for obtaining estimates of the parameter of the
niche preemption model (also known as the geometric series), in particular a
maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>.
 The niche preemption model is a widely used model in ecology and biodiversity
studies.")
    (license license:gpl2+)))

(define-public r-mlpack
  (package
    (name "r-mlpack")
    (version "4.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlpack" version))
       (sha256
        (base32 "1sn9ij9jjzdda4qwm144rvd8vi9mv1maqfpxm1nbshpa9q2ij2qf"))))
    (properties `((upstream-name . "mlpack")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppensmallen r-rcpparmadillo r-rcpp))
    (home-page "https://www.mlpack.org/doc/mlpack-git/r_documentation.html")
    (synopsis "'Rcpp' Integration for the 'mlpack' Library")
    (description
     "This package provides a fast, flexible machine learning library, written in C++,
that aims to provide fast, extensible implementations of cutting-edge machine
learning algorithms.  See also Curtin et al. (2023) <doi:10.21105/joss.05026>.")
    (license license:bsd-3)))

(define-public r-mlogitbma
  (package
    (name "r-mlogitbma")
    (version "0.1-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlogitBMA" version))
       (sha256
        (base32 "1c9v2q4nbd3rm3whm4gv3kn32nv40m6mgr5hivimrv559q68msmj"))))
    (properties `((upstream-name . "mlogitBMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-maxlik r-bma r-abind))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=mlogitBMA")
    (synopsis "Bayesian Model Averaging for Multinomial Logit Models")
    (description
     "This package provides a modified function bic.glm of the BMA package that can be
applied to multinomial logit (MNL) data.  The data is converted to binary logit
using the Begg & Gray approximation.  The package also contains functions for
maximum likelihood estimation of MNL.")
    (license license:gpl2+)))

(define-public r-mlogit
  (package
    (name "r-mlogit")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlogit" version))
       (sha256
        (base32 "0hyi6gi03n895iydrfr1ba2ifbsd6lyjyhkq62d95ghhniysjgkg"))))
    (properties `((upstream-name . "mlogit")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-statmod
                             r-rdpack
                             r-mass
                             r-lmtest
                             r-formula
                             r-dfidx))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mlogit")
    (synopsis "Multinomial Logit Models")
    (description
     "Maximum likelihood estimation of random utility discrete choice models.  The
software is described in Croissant (2020) <doi:10.18637/jss.v095.i11> and the
underlying methods in Train (2009) <doi:10.1017/CBO9780511805271>.")
    (license license:gpl2+)))

(define-public r-mlmusingr
  (package
    (name "r-mlmusingr")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLMusingR" version))
       (sha256
        (base32 "0ra3r9qrjv6pl36c0imvwzkkm12jk0xpm60n08siys3pj57ljh8z"))))
    (properties `((upstream-name . "MLMusingR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-performance
                             r-nlme
                             r-matrix
                             r-magrittr
                             r-lme4
                             r-generics
                             r-dplyr
                             r-broom))
    (home-page "https://github.com/flh3/MLMusingR")
    (synopsis "Practical Multilevel Modeling")
    (description
     "Convenience functions and datasets to be used with Practical Multilevel Modeling
using R. The package includes functions for calculating group means, group mean
centered variables, and displaying some basic missing data information.  A
function for computing robust standard errors for linear mixed models based on
Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and
@code{McCaffrey} (2002)
<https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=@code{NxMjN1YZ>}
is included as well as a function for checking for level-one homoskedasticity
(Raudenbush & Bryk, 2002, ISBN:076191904X).")
    (license license:gpl2)))

(define-public r-mlmts
  (package
    (name "r-mlmts")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmts" version))
       (sha256
        (base32 "0268zypg99yhizh5ashdwivckaqzfw60fb01f2lryz8c9hpa21mr"))))
    (properties `((upstream-name . "mlmts")))
    (build-system r-build-system)
    (propagated-inputs (list r-waveslim
                             r-tsfeatures
                             r-tserieschaos
                             r-tseries
                             r-tsdist
                             r-tsclust
                             r-tsa
                             r-rfast
                             r-rdpack
                             r-ranger
                             r-randomforest
                             r-quantspec
                             r-psych
                             r-pspline
                             r-pracma
                             r-multiwave
                             r-mts
                             r-matrix
                             r-mass
                             r-igraph
                             r-ggplot2
                             r-geigen
                             r-freqdom
                             r-forecast
                             r-fda-usc
                             r-e1071
                             r-dtw
                             r-desctools
                             r-complexplus
                             r-clusterr
                             r-caret
                             r-aid))
    (home-page "https://cran.r-project.org/package=mlmts")
    (synopsis "Machine Learning Algorithms for Multivariate Time Series")
    (description
     "An implementation of several machine learning algorithms for multivariate time
series.  The package includes functions allowing the execution of clustering,
classification or outlier detection methods, among others.  It also incorporates
a collection of multivariate time series datasets which can be used to analyse
the performance of new proposed algorithms.  Some of these datasets are stored
in @code{GitHub} data packages ueadata1 to ueadata8'.  To access these data
packages, run install.packages(c('ueadata1', ueadata2', ueadata3', ueadata4',
ueadata5', ueadata6', ueadata7', ueadata8'),
repos='<https://anloor7.github.io/drat/>')'.  The installation takes a couple of
minutes but we strongly encourage the users to do it if they want to have
available all datasets of mlmts.  Practitioners from a broad variety of fields
could benefit from the general framework provided by mlmts'.")
    (license license:gpl2)))

(define-public r-mlmtools
  (package
    (name "r-mlmtools")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmtools" version))
       (sha256
        (base32 "1fwa0cwrrhzxr5zkvmf0q1w2wlrhj58lwmrvqxfn00v0v9d6bi0g"))))
    (properties `((upstream-name . "mlmtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-lme4 r-ggplot2))
    (home-page "https://cran.r-project.org/package=mlmtools")
    (synopsis "Multi-Level Model Assessment Kit")
    (description
     "Multilevel models (mixed effects models) are the statistical tool of choice for
analyzing multilevel data (Searle et al, 2009).  These models account for the
correlated nature of observations within higher level units by adding
group-level error terms that augment the singular residual error of a standard
OLS regression.  Multilevel and mixed effects models often require specialized
data pre-processing and further post-estimation derivations and graphics to gain
insight into model results.  The package presented here, mlmtools', is a suite
of pre- and post-estimation tools for multilevel models in R'.  Package
implements post-estimation tools designed to work with models estimated using
lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects
regression models.  Searle, S. R., Casella, G., & @code{McCulloch}, C. E. (2009,
ISBN:978-0470009598).  Bates, D., MÃ¤chler, M., Bolker, B., & Walker, S. (2014)
<doi:10.18637/jss.v067.i01>.")
    (license license:gpl3+)))

(define-public r-mlmrev
  (package
    (name "r-mlmrev")
    (version "1.0-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmRev" version))
       (sha256
        (base32 "0ln4nazhc1prnlmvhfzhx268vx3klb0xnz1nsiwh0pwyskskyzym"))))
    (properties `((upstream-name . "mlmRev")))
    (build-system r-build-system)
    (propagated-inputs (list r-lme4))
    (home-page "https://cran.r-project.org/package=mlmRev")
    (synopsis "Examples from Multilevel Modelling Software Review")
    (description
     "Data and examples from a multilevel modelling software review as well as other
well-known data sets from the multilevel modelling literature.")
    (license license:gpl2+)))

(define-public r-mlmpower
  (package
    (name "r-mlmpower")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmpower" version))
       (sha256
        (base32 "0sakpiqi220j00w8s251ajaknm2lcg9jf9kif4l4qnz6z2vzn7y2"))))
    (properties `((upstream-name . "mlmpower")))
    (build-system r-build-system)
    (propagated-inputs (list r-vartestnlme r-lmertest r-lme4 r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bkeller2/mlmpower")
    (synopsis "Power Analysis and Data Simulation for Multilevel Models")
    (description
     "This package provides a declarative language for specifying multilevel models,
solving for population parameters based on specified variance-explained effect
size measures, generating data, and conducting power analyses to determine
sample size recommendations.  The specification allows for any number of
within-cluster effects, between-cluster effects, covariate effects at either
level, and random coefficients.  Moreover, the models do not assume orthogonal
effects, and predictors can correlate at either level and accommodate models
with multiple interaction effects.")
    (license license:gpl3)))

(define-public r-mlmoi
  (package
    (name "r-mlmoi")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLMOI" version))
       (sha256
        (base32 "1j85syszcsv0v30dvsq5rsdkaz1yj71hsrk53cw5k3m89229wzk1"))))
    (properties `((upstream-name . "MLMOI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmpfr r-rdpack r-openxlsx))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MLMOI")
    (synopsis
     "Estimating Frequencies, Prevalence and Multiplicity of Infection")
    (description
     "The implemented methods reach out to scientists that seek to estimate
multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences
at molecular markers using the maximum-likelihood method described in Schneider
(2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014)
<doi:10.1371/journal.pone.0097899>.  Users can import data from Excel files in
various formats, and perform maximum-likelihood estimation on the imported data
by the package's moimle() function.")
    (license license:gpl3)))

(define-public r-mlmodelselection
  (package
    (name "r-mlmodelselection")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLModelSelection" version))
       (sha256
        (base32 "1qfy2rfx2nvkwisl9wwllqsq2qhqzsdmrh90z5zn5qs07d0305wn"))))
    (properties `((upstream-name . "MLModelSelection")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppdist r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://github.com/kuojunglee/")
    (synopsis "Model Selection in Multivariate Longitudinal Data Analysis")
    (description
     "An efficient Gibbs sampling algorithm is developed for Bayesian multivariate
longitudinal data analysis with the focus on selection of important elements in
the generalized autoregressive matrix.  It provides posterior samples and
estimates of parameters.  In addition, estimates of several information criteria
such as Akaike information criterion (AIC), Bayesian information criterion
(BIC), deviance information criterion (DIC) and prediction accuracy such as the
marginal predictive likelihood (MPL) and the mean squared prediction error
(MSPE) are provided for model selection.")
    (license license:gpl2)))

(define-public r-mlmm-gwas
  (package
    (name "r-mlmm-gwas")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmm.gwas" version))
       (sha256
        (base32 "14xp8k7sww1skyw5l2006gpaw2c6yrri5nmfx4yav4vqdlhcj2k1"))))
    (properties `((upstream-name . "mlmm.gwas")))
    (build-system r-build-system)
    (propagated-inputs (list r-sommer r-multcompview r-multcomp r-matrix
                             r-coxme))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mlmm.gwas")
    (synopsis "Pipeline for GWAS Using MLMM")
    (description
     "Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from
Segura V, VilhjÃ¡lmsson BJ et al. (2012) <doi:10.1038/ng.2314>.  The pipeline
include detection of associated SNPs with MLMM, model selection by lowest
@code{eBIC} and p-value threshold, estimation of the effects of the SNPs in the
selected model and graphical functions.")
    (license license:gpl3)))

(define-public r-mlml2r
  (package
    (name "r-mlml2r")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLML2R" version))
       (sha256
        (base32 "1jgk0h3chab831g5nsppgc6p0xplv11j82491iymnxixqfswb08w"))))
    (properties `((upstream-name . "MLML2R")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/samarafk/MLML2R")
    (synopsis
     "Maximum Likelihood Estimation of DNA Methylation and Hydroxymethylation Proportions")
    (description
     "Maximum likelihood estimates (MLE) of the proportions of 5-@code{mC} and
5-@code{hmC} in the DNA using information from BS-conversion, TAB-conversion,
and @code{oxBS-conversion} methods.  One can use information from all three
methods or any combination of two of them.  Estimates are based on Binomial
model by Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and Kiihl et al.
(2019) <doi:10.1515/sagmb-2018-0031>.")
    (license license:expat)))

(define-public r-mlmi
  (package
    (name "r-mlmi")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmi" version))
       (sha256
        (base32 "1nkg7h0r716yv9y6xnx8qcpvrqr6cxlhqrcyiihkwgwbmkgkp5sz"))))
    (properties `((upstream-name . "mlmi")))
    (build-system r-build-system)
    (propagated-inputs (list r-norm
                             r-nlme
                             r-mix
                             r-matrix
                             r-mass
                             r-gsl
                             r-cat))
    (home-page "https://cran.r-project.org/package=mlmi")
    (synopsis "Maximum Likelihood Multiple Imputation")
    (description
     "This package implements so called Maximum Likelihood Multiple Imputation as
described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.  A number
of different imputations are available, by utilising the norm', cat and mix
packages.  Inferences can be performed either using combination rules similar to
Rubin's or using a likelihood score based approach based on theory by Wang and
Robins (1998) <doi:10.1093/biomet/85.4.935>.")
    (license license:gpl3)))

(define-public r-mlmhelpr
  (package
    (name "r-mlmhelpr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmhelpr" version))
       (sha256
        (base32 "16p94w6lh6k5dlfmi1kfdnm7q05c0d30gf08qcm9z9mm9yyilp0d"))))
    (properties `((upstream-name . "mlmhelpr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-mathjaxr r-lme4))
    (home-page "https://github.com/lrocconi/mlmhelpr")
    (synopsis "Multilevel/Mixed Model Helper Functions")
    (description
     "This package provides a collection of miscellaneous helper function for running
multilevel/mixed models in lme4'.  This package aims to provide functions to
compute common tasks when estimating multilevel models such as computing the
intraclass correlation and design effect, centering variables, estimating the
proportion of variance explained at each level, pseudo-R squared, random
intercept and slope reliabilities, tests for homogeneity of variance at level-1,
and cluster robust and bootstrap standard errors.  The tests and statistics
reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049),
Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012,
ISBN:9781849202015).")
    (license license:expat)))

(define-public r-mlmetrics
  (package
    (name "r-mlmetrics")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLmetrics" version))
       (sha256
        (base32 "11dgpw6lzs3vrfa3v9kf8x2rph3vpicxbw5af89afa175by3amrc"))))
    (properties `((upstream-name . "MLmetrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr))
    (home-page "https://github.com/yanyachen/MLmetrics")
    (synopsis "Machine Learning Evaluation Metrics")
    (description
     "This package provides a collection of evaluation metrics, including loss, score
and utility functions, that measure regression, classification and ranking
performance.")
    (license license:gpl2)))

(define-public r-mlmc
  (package
    (name "r-mlmc")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlmc" version))
       (sha256
        (base32 "177ih6x701pnjnwrs34pwj8376s6177a1xkmr8zkcgx2wjwjiv4x"))))
    (properties `((upstream-name . "mlmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ggplot2))
    (home-page "https://mlmc.louisaslett.com/")
    (synopsis "Multi-Level Monte Carlo")
    (description
     "An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008)
<doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>, for
R. This package builds on the original Matlab and C++ implementations by Mike
Giles to provide a full MLMC driver and example level samplers.  Multi-core
parallel sampling of levels is provided built-in.")
    (license license:gpl2)))

(define-public r-mlma
  (package
    (name "r-mlma")
    (version "6.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlma" version))
       (sha256
        (base32 "16v3rwdc7kzn2k84ghkss14p1kqx86md9k1xv2k2x187f5lncamh"))))
    (properties `((upstream-name . "mlma")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-lme4
                             r-gplots
                             r-coxme
                             r-car
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mlma")
    (synopsis "Multilevel Mediation Analysis")
    (description
     "Do multilevel mediation analysis with generalized additive multilevel models.
The analysis method is described in Yu and Li (2020), \"Third-Variable Effect
Analysis with Multilevel Additive Models\", P@code{LoS} ONE 15(10): e0241072.")
    (license license:gpl2+)))

(define-public r-mllrnrs
  (package
    (name "r-mllrnrs")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mllrnrs" version))
       (sha256
        (base32 "0yrsab0m3p8fjxhbvb58k7lz3l2vvnf339gfb1wfc68wjmpczk5z"))))
    (properties `((upstream-name . "mllrnrs")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-mlexperiments r-kdry r-data-table))
    (native-inputs (list r-quarto))
    (home-page "https://github.com/kapsner/mllrnrs")
    (synopsis "R6-Based ML Learners for 'mlexperiments'")
    (description
     "Enhances mlexperiments <https://CRAN.R-project.org/package=mlexperiments> with
additional machine learning ('ML') learners.  The package provides R6-based
learners for the following algorithms: glmnet
<https://CRAN.R-project.org/package=glmnet>, ranger
<https://CRAN.R-project.org/package=ranger>, xgboost
<https://CRAN.R-project.org/package=xgboost>, and lightgbm
<https://CRAN.R-project.org/package=lightgbm>.  These can be used directly with
the mlexperiments R package.")
    (license license:gpl3+)))

(define-public r-mlim
  (package
    (name "r-mlim")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlim" version))
       (sha256
        (base32 "16p3nprnxc0cghfbwcs3hf57aasqpvd4qv3wajhzrajqhmclzn0q"))))
    (properties `((upstream-name . "mlim")))
    (build-system r-build-system)
    (propagated-inputs (list r-missranger
                             r-mice
                             r-memuse
                             r-md-log
                             r-h2o
                             r-curl))
    (home-page "https://github.com/haghish/mlim")
    (synopsis "Single and Multiple Imputation with Automated Machine Learning")
    (description
     "Machine learning algorithms have been used for performing single missing data
imputation and most recently, multiple imputations.  However, this is the first
attempt for using automated machine learning algorithms for performing both
single and multiple imputation.  Automated machine learning is a procedure for
fine-tuning the model automatic, performing a random search for a model that
results in less error, without overfitting the data.  The main idea is to allow
the model to set its own parameters for imputing each variable separately
instead of setting fixed predefined parameters to impute all variables of the
dataset.  Using automated machine learning, the package fine-tunes an Elastic
Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme
Gradient Boosting, or Stacked Ensemble machine learning model (from one or a
combination of other supported algorithms) for imputing the missing
observations.  This procedure has been implemented for the first time by this
package and is expected to outperform other packages for imputing missing data
that do not fine-tune their models.  The multiple imputation is implemented via
bootstrapping without letting the duplicated observations to harm the
cross-validation procedure, which is the way imputed variables are evaluated.
Most notably, the package implements automated procedure for handling imputing
imbalanced data (class rarity problem), which happens when a factor variable has
a level that is far more prevalent than the other(s).  This is known to result
in biased predictions, hence, biased imputation of missing data.  However, the
autobalancing procedure ensures that instead of focusing on maximizing accuracy
(classification error) in imputing factor variables, a fairer procedure and
imputation method is practiced.")
    (license license:expat)))

(define-public r-mlid
  (package
    (name "r-mlid")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLID" version))
       (sha256
        (base32 "0ygahmcfbydzic5q3h1ppv5b2ks4lh8mcyfqq27ssmn4g1n6hdvp"))))
    (properties `((upstream-name . "MLID")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/profrichharris/MLID")
    (synopsis "Multilevel Index of Dissimilarity")
    (description
     "This package provides tools and functions to fit a multilevel index of
dissimilarity.")
    (license license:gpl3)))

(define-public r-mlgl
  (package
    (name "r-mlgl")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLGL" version))
       (sha256
        (base32 "1vn9r867mj6g407ca2ncd1f2c3x83jcdllyxfalm3962xgwrg08m"))))
    (properties `((upstream-name . "MLGL")))
    (build-system r-build-system)
    (propagated-inputs (list r-paralleldist
                             r-matrix
                             r-mass
                             r-gglasso
                             r-fastcluster
                             r-factominer))
    (home-page "https://cran.r-project.org/package=MLGL")
    (synopsis "Multi-Layer Group-Lasso")
    (description
     "It implements a new procedure of variable selection in the context of redundancy
between explanatory variables, which holds true with high dimensional data
(Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).")
    (license license:gpl2+)))

(define-public r-mlgdata
  (package
    (name "r-mlgdata")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLGdata" version))
       (sha256
        (base32 "0ysvrnx8m903h99wr40zgg1c6d26m9vsh62zjxh8mvndh69ikzid"))))
    (properties `((upstream-name . "MLGdata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MLGdata")
    (synopsis "Datasets for Use with Salvan, Sartori and Pace (2020)")
    (description
     "This package contains the datasets for use with the book Salvan, Sartori and
Pace (2020, ISBN:978-88-470-4002-1) \"Modelli Lineari Generalizzati\".")
    (license license:gpl2+)))

(define-public r-mlfs
  (package
    (name "r-mlfs")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLFS" version))
       (sha256
        (base32 "0xrc266hz69vpfi2h59lma0wbyi1wfcw4wb3dbjbnqqwsq1hc0dz"))))
    (properties `((upstream-name . "MLFS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-reshape2
                             r-ranger
                             r-pscl
                             r-naivebayes
                             r-magrittr
                             r-dplyr
                             r-brnn))
    (home-page "https://cran.r-project.org/package=MLFS")
    (synopsis "Machine Learning Forest Simulator")
    (description
     "Climate-sensitive forest simulator based on the principles of machine learning.
It stimulates all key processes in the forest: radial growth, height growth,
mortality, crown recession, regeneration and harvesting.  The method for
predicting tree heights was described by Skudnik and JevÅ¡enak (2022)
<doi:10.1016/j.foreco.2022.120017>, while the method for predicting basal area
increments (BAI) was described by JevÅ¡enak and Skudnik (2021)
<doi:10.1016/j.foreco.2020.118601>.")
    (license license:gpl3)))

(define-public r-mlflow
  (package
    (name "r-mlflow")
    (version "2.16.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlflow" version))
       (sha256
        (base32 "0mqyssdvp0ccfclmjh7ms8v86vgzirq0vm0bs3zr77as6bz1rqca"))))
    (properties `((upstream-name . "mlflow")))
    (build-system r-build-system)
    (propagated-inputs (list r-zeallot
                             r-yaml
                             r-withr
                             r-tibble
                             r-swagger
                             r-rlang
                             r-purrr
                             r-processx
                             r-openssl
                             r-jsonlite
                             r-ini
                             r-httr
                             r-httpuv
                             r-glue
                             r-git2r
                             r-fs
                             r-forge
                             r-base64enc))
    (home-page "https://github.com/mlflow/mlflow")
    (synopsis "Interface to 'MLflow'")
    (description
     "R interface to MLflow', open source platform for the complete machine learning
life cycle, see <https://mlflow.org/>.  This package supports installing
MLflow', tracking experiments, creating and running projects, and saving and
serving models.")
    (license license:asl2.0)))

(define-public r-mlfit
  (package
    (name "r-mlfit")
    (version "0.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlfit" version))
       (sha256
        (base32 "0nyk5vmy2gkpz022djacy80gibskv96z0rsmjz2rw14zhc5kizsh"))))
    (properties `((upstream-name . "mlfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-wrswor
                             r-tibble
                             r-rlang
                             r-plyr
                             r-matrix
                             r-lifecycle
                             r-kimisc
                             r-hms
                             r-forcats
                             r-dplyr
                             r-bb))
    (home-page "https://mlfit.github.io/mlfit/")
    (synopsis
     "Iterative Proportional Fitting Algorithms for Nested Structures")
    (description
     "The Iterative Proportional Fitting (IPF) algorithm operates on count data.  This
package offers implementations for several algorithms that extend this to nested
structures: parent and child items for both of which constraints can be
provided.  The fitting algorithms include Iterative Proportional Updating
<https://trid.trb.org/view/881554>, Hierarchical IPF
<doi:10.3929/ethz-a-006620748>, Entropy Optimization
<https://trid.trb.org/view/881144>, and Generalized Raking
<doi:10.2307/2290793>.  Additionally, a number of replication methods is also
provided such as Truncate, replicate, sample
<doi:10.1016/j.compenvurbsys.2013.03.004>.")
    (license license:gpl3+)))

(define-public r-mlf
  (package
    (name "r-mlf")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlf" version))
       (sha256
        (base32 "09ihnibpknpp26hdlbxyr99yz7z0n8qpsjs5aq3zngng0yh3wdn9"))))
    (properties `((upstream-name . "mlf")))
    (build-system r-build-system)
    (home-page "http://mlf-project.us/")
    (synopsis "Machine Learning Foundations")
    (description
     "Offers a gentle introduction to machine learning concepts for practitioners with
a statistical pedigree: decomposition of model error (bias-variance trade-off),
nonlinear correlations, information theory and functional permutation/bootstrap
simulations.  SzÃ©kely GJ, Rizzo ML, Bakirov NK. (2007).
<doi:10.1214/009053607000000505>.  Reshef DN, Reshef YA, Finucane HK, Grossman
SR, @code{McVean} G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011).
<doi:10.1126/science.1205438>.")
    (license license:gpl2)))

(define-public r-mlexperiments
  (package
    (name "r-mlexperiments")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlexperiments" version))
       (sha256
        (base32 "1l2zrlar8wv4hi1w76kcbrhx8fbnbh03wq0jj5y0sbmy9x3aza76"))))
    (properties `((upstream-name . "mlexperiments")))
    (build-system r-build-system)
    (propagated-inputs (list r-splittools r-r6 r-progress r-kdry r-data-table))
    (native-inputs (list r-quarto))
    (home-page "https://github.com/kapsner/mlexperiments")
    (synopsis "Machine Learning Experiments")
    (description
     "This package provides R6 objects to perform parallelized hyperparameter
optimization and cross-validation.  Hyperparameter optimization can be performed
with Bayesian optimization (via @code{ParBayesianOptimization}
<https://cran.r-project.org/package=@code{ParBayesianOptimization>}) and grid
search.  The optimized hyperparameters can be validated using k-fold
cross-validation.  Alternatively, hyperparameter optimization and validation can
be performed with nested cross-validation.  While mlexperiments focuses on core
wrappers for machine learning experiments, additional learner algorithms can be
supplemented by inheriting from the provided learner base class.")
    (license license:gpl3+)))

(define-public r-mleval
  (package
    (name "r-mleval")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLeval" version))
       (sha256
        (base32 "1g2w5lrr7199n4zyaalbp9xdgl858hdg1pqr3rpd7v5fmamawc69"))))
    (properties `((upstream-name . "MLeval")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MLeval")
    (synopsis "Machine Learning Model Evaluation")
    (description
     "Straightforward and detailed evaluation of machine learning models.  MLeval can
produce receiver operating characteristic (ROC) curves, precision-recall (PR)
curves, calibration curves, and PR gain curves.  MLeval accepts a data frame of
class probabilities and ground truth labels, or, it can automatically interpret
the Caret train function results from repeated cross validation, then select the
best model and analyse the results.  MLeval produces a range of evaluation
metrics with confidence intervals.")
    (license license:agpl3)))

(define-public r-mlergm
  (package
    (name "r-mlergm")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlergm" version))
       (sha256
        (base32 "0bpdljmbn4lfm263jcsp0hrknixn49m7hqsx0jjk3pfwhgjlajh8"))))
    (properties `((upstream-name . "mlergm")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sna
                             r-reshape2
                             r-plyr
                             r-network
                             r-matrix
                             r-lpsolve
                             r-ggplot2
                             r-ggally
                             r-ergm
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mlergm")
    (synopsis "Multilevel Exponential-Family Random Graph Models")
    (description
     "Estimates exponential-family random graph models for multilevel network data,
assuming the multilevel structure is observed.  The scope, at present, covers
multilevel models where the set of nodes is nested within known blocks.  The
estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods
to estimate a variety of canonical or curved exponential family models for
binary random graphs.  MCMLE methods for curved exponential-family random graph
models can be found in Hunter and Handcock (2006) <DOI:
10.1198/106186006X133069>.  The package supports parallel computing, and
provides methods for assessing goodness-of-fit of models and visualization of
networks.")
    (license license:gpl3)))

(define-public r-mlelod
  (package
    (name "r-mlelod")
    (version "1.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlelod" version))
       (sha256
        (base32 "1rjdpc252ydxsh878nlgw65s41wl2hn5iwvd8mskxs72in9m8qsm"))))
    (properties `((upstream-name . "mlelod")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mlelod")
    (synopsis
     "MLE for Normally Distributed Data Censored by Limit of Detection")
    (description
     "Values below the limit of detection (LOD) are a problem in several fields of
science, and there are numerous approaches for replacing the missing data.  We
present a new mathematical solution for maximum likelihood estimation that
allows us to estimate the true values of the mean and standard deviation for
normal distributions and is significantly faster than previous implementations.
The article with the details was submitted to JSS and can be currently seen on
<https://www2.arnes.si/~tverbo/LOD/Verbovsek_Sega_2_Manuscript.pdf>.")
    (license license:gpl2)))

(define-public r-mlegp
  (package
    (name "r-mlegp")
    (version "3.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlegp" version))
       (sha256
        (base32 "1cs0l04axp6f6f6m4h4kw8z4pasxgp5la3xib04wxzb2l4bnsab3"))))
    (properties `((upstream-name . "mlegp")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mlegp")
    (synopsis "Maximum Likelihood Estimates of Gaussian Processes")
    (description
     "Maximum likelihood Gaussian process modeling for univariate and
multi-dimensional outputs with diagnostic plots following Santner et al (2003)
<doi:10.1007/978-1-4757-3799-8>.  Contact the maintainer for a package version
that includes sensitivity analysis.")
    (license license:gpl2+)))

(define-public r-mlece
  (package
    (name "r-mlece")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLEce" version))
       (sha256
        (base32 "190i2if8hgwqlznhls780vpkwprfmnsr9jm6zdsa8av0gh6kldzh"))))
    (properties `((upstream-name . "MLEce")))
    (build-system r-build-system)
    (propagated-inputs (list r-sirt
                             r-reshape
                             r-nleqslv
                             r-mvtnorm
                             r-laplacesdemon
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=MLEce")
    (synopsis
     "Asymptotic Efficient Closed-Form Estimators for Multivariate Distributions")
    (description
     "Asymptotic efficient closed-form estimators (MLEces) are provided in this
package for three multivariate distributions(gamma, Weibull and Dirichlet) whose
maximum likelihood estimators (MLEs) are not in closed forms.  Closed-form
estimators are strong consistent, and have the similar asymptotic normal
distribution like MLEs.  But the calculation of MLEces are much faster than the
corresponding MLEs.  Further details and explanations of MLEces can be found in.
 Jang, et al. (2023) <doi:10.1111/stan.12299>.  Kim, et al. (2023)
<doi:10.1080/03610926.2023.2179880>.")
    (license license:gpl2)))

(define-public r-mle-tools
  (package
    (name "r-mle-tools")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mle.tools" version))
       (sha256
        (base32 "02yndj1if31zr9y805mq5km5n8jz4w9jz1bmaz9nnqsqimrnigrb"))))
    (properties `((upstream-name . "mle.tools")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mle.tools")
    (synopsis
     "Expected/Observed Fisher Information and Bias-Corrected Maximum Likelihood Estimate(s)")
    (description
     "Calculates the expected/observed Fisher information and the bias-corrected
maximum likelihood estimate(s) via Cox-Snell Methodology.")
    (license license:gpl2+)))

(define-public r-mle
  (package
    (name "r-mle")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLE" version))
       (sha256
        (base32 "1nj7jl33k0nwkjd3k5hpmxkypmym8kzqgahyjqgsxsq91jighpxg"))))
    (properties `((upstream-name . "MLE")))
    (build-system r-build-system)
    (propagated-inputs (list r-skellam
                             r-rfast2
                             r-rfast
                             r-mn
                             r-gp
                             r-geppe
                             r-directional
                             r-compositional
                             r-bivpois
                             r-bgfd))
    (home-page "https://cran.r-project.org/package=MLE")
    (synopsis
     "Maximum Likelihood Estimation of Various Univariate and Multivariate Distributions")
    (description
     "Several functions for maximum likelihood estimation of various univariate and
multivariate distributions.  The list includes more than 100 univariate
continuous and discrete distributions, distributions that lie on the real line,
the positive line, interval restricted, circular distributions.  Further,
multivariate continuous and discrete distributions, distributions for
compositional and directional data, etc.  Some references include Johnson N. L.,
Kotz S. and Balakrishnan N. (1994). \"Continuous Univariate Distributions, Volume
1\" <ISBN:978-0-471-58495-7>, Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel
(2005). \"Univariate Discrete Distributions\". <ISBN:978-0-471-71580-1> and
Mardia, K. V. and Jupp, P. E. (2000). \"Directional Statistics\".
<ISBN:978-0-471-95333-3>.")
    (license license:gpl2+)))

(define-public r-mlds
  (package
    (name "r-mlds")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLDS" version))
       (sha256
        (base32 "0c8bmdjn6980lqihzc0rgg864hs1a51n2lrwi1vrxdp4xylvnqdd"))))
    (properties `((upstream-name . "MLDS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MLDS")
    (synopsis "Maximum Likelihood Difference Scaling")
    (description
     "Difference scaling is a method for scaling perceived supra-threshold
differences.  The package contains functions that allow the user to design and
run a difference scaling experiment, to fit the resulting data by maximum
likelihood and test the internal validity of the estimated scale.")
    (license license:gpl2+)))

(define-public r-mldr-resampling
  (package
    (name "r-mldr-resampling")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mldr.resampling" version))
       (sha256
        (base32 "0knxv6ndni50g17b86gb6xshks6h776y5aa82hmz9q7n2mq1861v"))))
    (properties `((upstream-name . "mldr.resampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-vecsets r-pbapply r-mldr r-e1071 r-data-table))
    (home-page "https://cran.r-project.org/package=mldr.resampling")
    (synopsis "Resampling Algorithms for Multi-Label Datasets")
    (description
     "Collection of the state of the art multi-label resampling algorithms.  The
objective of these algorithms is to achieve balance in multi-label datasets.")
    (license license:expat)))

(define-public r-mldr-datasets
  (package
    (name "r-mldr-datasets")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mldr.datasets" version))
       (sha256
        (base32 "0zyfv8xy5yik0k3j0kf9r43xrvj528qzdb1v74sfi24vim6k6503"))))
    (properties `((upstream-name . "mldr.datasets")))
    (build-system r-build-system)
    (home-page "https://github.com/fcharte/mldr.datasets")
    (synopsis "R Ultimate Multilabel Dataset Repository")
    (description
     "Large collection of multilabel datasets along with the functions needed to
export them to several formats, to make partitions, and to obtain bibliographic
information.")
    (license (list license:lgpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mldr
  (package
    (name "r-mldr")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mldr" version))
       (sha256
        (base32 "07lbxpydz78fya11bl3l1ckhbps3nx43ddh3z09ygg70jjbdhxwq"))))
    (properties `((upstream-name . "mldr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-shiny r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fcharte/mldr")
    (synopsis
     "Exploratory Data Analysis and Manipulation of Multi-Label Data Sets")
    (description
     "Exploratory data analysis and manipulation functions for multi- label data sets
along with an interactive Shiny application to ease their use.")
    (license (list license:lgpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mldatar
  (package
    (name "r-mldatar")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLDataR" version))
       (sha256
        (base32 "0sbm79hpgxiqnqjyr5n88rhs29lkp17czz9jmhd2d5x97ncpqw9f"))))
    (properties `((upstream-name . "MLDataR")))
    (build-system r-build-system)
    (propagated-inputs (list r-workflows
                             r-varhandle
                             r-rsample
                             r-recipes
                             r-ranger
                             r-parsnip
                             r-oddsplotty
                             r-ggplot2
                             r-dplyr
                             r-confusiontabler
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MLDataR")
    (synopsis
     "Collection of Machine Learning Datasets for Supervised Machine Learning")
    (description
     "This package contains a collection of datasets for working with machine learning
tasks.  It will contain datasets for supervised machine learning Jiang
(2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for
classification and regression.  The aim of this package is to use data generated
around health and other domains.")
    (license license:expat)))

(define-public r-mlcopula
  (package
    (name "r-mlcopula")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLCOPULA" version))
       (sha256
        (base32 "1addgypksr3hrhzrj9ypaa2w7vgscrqlrfkgx7n0rc7drm827wix"))))
    (properties `((upstream-name . "MLCOPULA")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsp
                             r-pracma
                             r-kde1d
                             r-igraph
                             r-gridcopula
                             r-copula))
    (home-page "https://cran.r-project.org/package=MLCOPULA")
    (synopsis "Classification Models with Copula Functions")
    (description
     "This package provides several classifiers based on probabilistic models.  These
classifiers allow to model the dependence structure of continuous features
through bivariate copula functions and graphical models, see Salinas-GutiÃ©rrez
et al. (2014) <doi:10.1007/s00180-013-0457-y>.")
    (license license:gpl3)))

(define-public r-mlcm
  (package
    (name "r-mlcm")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLCM" version))
       (sha256
        (base32 "1sz5c3wxvnyxd3bf53i8x2jw591phx2np6c6bj4cqppb98ab83f3"))))
    (properties `((upstream-name . "MLCM")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MLCM")
    (synopsis "Maximum Likelihood Conjoint Measurement")
    (description
     "Conjoint measurement is a psychophysical procedure in which stimulus pairs are
presented that vary along 2 or more dimensions and the observer is required to
compare the stimuli along one of them.  This package contains functions to
estimate the contribution of the n scales to the judgment by a maximum
likelihood method under several hypotheses of how the perceptual dimensions
interact.  Reference: Knoblauch & Maloney (2012) \"Modeling Psychophysical Data
in R\". <doi:10.1007/978-1-4614-4475-6>.")
    (license license:gpl2+)))

(define-public r-mlcirtwithin
  (package
    (name "r-mlcirtwithin")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MLCIRTwithin" version))
       (sha256
        (base32 "1x0xmka7kkbjnh3yv4zxxyl17cpmf0rb9hxmdl1srb6ijld4np1b"))))
    (properties `((upstream-name . "MLCIRTwithin")))
    (build-system r-build-system)
    (propagated-inputs (list r-multilcirt r-mass r-limsolve))
    (home-page "https://cran.r-project.org/package=MLCIRTwithin")
    (synopsis
     "Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality")
    (description
     "Framework for the Item Response Theory analysis of dichotomous and ordinal
polytomous outcomes under the assumption of within-item multidimensionality and
discreteness of the latent traits.  The fitting algorithms allow for missing
responses and for different item parametrizations and are based on the
Expectation-Maximization paradigm.  Individual covariates affecting the class
weights may be included in the new version together with possibility of
constraints on all model parameters.")
    (license license:gpl2+)))

(define-public r-mlbstats
  (package
    (name "r-mlbstats")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlbstats" version))
       (sha256
        (base32 "1pfsc1pc5986gykx8l6afahszhaj4940l8g33fdxyy7194kbcz70"))))
    (properties `((upstream-name . "mlbstats")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mlbstats")
    (synopsis "Major League Baseball Player Statistics Calculator")
    (description
     "Computational functions for player metrics in major league baseball including
batting, pitching, fielding, base-running, and overall player statistics.  This
package is actively maintained with new metrics being added as they are
developed.")
    (license license:expat)))

(define-public r-mlbplotr
  (package
    (name "r-mlbplotr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mlbplotR" version))
       (sha256
        (base32 "02509dghl22zwgbb94d794d61970svz1pqrx49bp8l9y3if9n36d"))))
    (properties `((upstream-name . "mlbplotR")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-magrittr
                             r-magick
                             r-lifecycle
                             r-gt
                             r-ggplot2
                             r-data-table
                             r-cli))
    (home-page "https://github.com/camdenk/mlbplotR")
    (synopsis
     "Create 'ggplot2' and 'gt' Visuals with Major League Baseball Logos")
    (description
     "This package provides tools to help visualize Major League Baseball analysis in
ggplot2 and gt'.  You provide team/player information and @code{mlbplotR} will
transform that information into team colors, logos, or player headshots for
graphics.")
    (license license:expat)))

(define-public r-ml2pvae
  (package
    (name "r-ml2pvae")
    (version "1.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ML2Pvae" version))
       (sha256
        (base32 "089cl5mcf06zxs26grk0aqmd0xy9x6svfkfa7dw0m8zqc0s0mkix"))))
    (properties `((upstream-name . "ML2Pvae")))
    (build-system r-build-system)
    (inputs (list tensorflow))
    (propagated-inputs (list r-tfprobability r-tensorflow r-reticulate r-keras))
    (native-inputs (list r-r-rsp))
    (home-page "https://converseg.github.io")
    (synopsis "Variational Autoencoder Models for IRT Parameter Estimation")
    (description
     "Based on the work of Curi, Converse, Hajewski, and Oliveira (2019)
<doi:10.1109/IJCNN.2019.8852333>.  This package provides easy-to-use functions
which create a variational autoencoder (VAE) to be used for parameter estimation
in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter
(ML2P) model.  To use a neural network as such, nontrivial modifications to the
architecture must be made, such as restricting the nonzero weights in the
decoder according to some binary matrix Q. The functions in this package allow
for straight-forward construction, training, and evaluation so that minimal
knowledge of tensorflow or keras is required.")
    (license license:expat)))

(define-public r-ml-msbd
  (package
    (name "r-ml-msbd")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ML.MSBD" version))
       (sha256
        (base32 "1cpwq0vilbyz837d8k8pjipw3cwdsggbs88nwp827s4w2f1m7pap"))))
    (properties `((upstream-name . "ML.MSBD")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ML.MSBD")
    (synopsis "Maximum Likelihood Inference on Multi-State Trees")
    (description
     "Inference of a multi-states birth-death model from a phylogeny, comprising a
number of states N, birth and death rates for each state and on which edges each
state appears.  Inference is done using a hybrid approach: states are
progressively added in a greedy approach.  For a fixed number of states N the
best model is selected via maximum likelihood.  Reference: J. Barido-Sottani, T.
G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.")
    (license license:gpl3)))

(define-public r-mkssd
  (package
    (name "r-mkssd")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mkssd" version))
       (sha256
        (base32 "1vgg5azr4qydqb6xbdbff9256bwz7gl3k3hpn7998rm9jhdmynnd"))))
    (properties `((upstream-name . "mkssd")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mkssd")
    (synopsis "Efficient Multi-Level k-Circulant Supersaturated Designs")
    (description
     "Generates efficient balanced non-aliased multi-level k-circulant supersaturated
designs by interchanging the elements of the generator vector.  Attempts to
generate a supersaturated design that has chisquare efficiency more than user
specified efficiency level (mef).  Displays the progress of generation of an
efficient multi-level k-circulant design through a progress bar.  The progress
of 100% means that one full round of interchange is completed.  More than one
full round (typically 4-5 rounds) of interchange may be required for larger
designs.")
    (license license:gpl2+)))

(define-public r-mkpower
  (package
    (name "r-mkpower")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKpower" version))
       (sha256
        (base32 "1bsagc422wkbx5sy5azx167xmrv3h0jlb8yg8vxibrjb8hv3wcvd"))))
    (properties `((upstream-name . "MKpower")))
    (build-system r-build-system)
    (propagated-inputs (list r-qqplotr
                             r-mvtnorm
                             r-mkinfer
                             r-mkdescr
                             r-matrixtests
                             r-ggplot2
                             r-coin))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stamats/MKpower")
    (synopsis "Power Analysis and Sample Size Calculation")
    (description
     "Power analysis and sample size calculation for Welch and Hsu (Hedderich and
Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations
of empirical power and type-I-error.  Power and sample size calculation for
Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations.  Power and
sample size required for the evaluation of a diagnostic test(-system) (Flahault
et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007),
<doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss
et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004),
<doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>),
comparing two negative binomial rates (Zhu and Lakkis (2014),
<doi:10.1002/sim.5947>), ANCOVA (Shieh (2020),
<doi:10.1007/s11336-019-09692-3>), reference ranges (Jennen-Steinmetz and Wellek
(2005), <doi:10.1002/sim.2177>), multiple primary endpoints (Sozu et al. (2015),
ISBN:978-3-319-22005-5), and AUC (Hanley and @code{McNeil} (1982),
<doi:10.1148/radiology.143.1.7063747>).")
    (license license:lgpl3)))

(define-public r-mkomics
  (package
    (name "r-mkomics")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKomics" version))
       (sha256
        (base32 "0ck8i7ip12bb6wilmwhdrbdmf56ymm3rp6973lr1p4bc2m2kvhif"))))
    (properties `((upstream-name . "MKomics")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-rcolorbrewer r-limma
                             r-complexheatmap r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://www.stamats.de/")
    (synopsis "Omics Data Analysis")
    (description
     "Similarity plots based on correlation and median absolute deviation (MAD);
adjusting colors for heatmaps; aggregate technical replicates; calculate
pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA;
simplified interface to package limma (Ritchie et al. (2015),
<doi:10.1093/nar/gkv007> ) for moderated t-test and one-way ANOVA; Hamming and
Levenshtein (edit) distance of strings as well as optimal alignment scores for
global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant
gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).")
    (license license:lgpl3)))

(define-public r-mknapsack
  (package
    (name "r-mknapsack")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mknapsack" version))
       (sha256
        (base32 "1kzmx7d512681a4hjirfgcd7a8rvndb4da66p16gms5nnxzsby9c"))))
    (properties `((upstream-name . "mknapsack")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve r-data-table r-assertthat))
    (home-page "https://github.com/madedotcom/mknapsack")
    (synopsis "Multiple Knapsack Problem Solver")
    (description
     "Package solves multiple knapsack optimisation problem.  Given a set of items,
each with volume and value, it will allocate them to knapsacks of a given size
in a way that value of top N knapsacks is as large as possible.")
    (license license:gpl2)))

(define-public r-mkmisc
  (package
    (name "r-mkmisc")
    (version "1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKmisc" version))
       (sha256
        (base32 "09kdwsfx5w3q88jbnxbaxzbzyx9fhhphqqaxn5qghhh1z2vpmy93"))))
    (properties `((upstream-name . "MKmisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-robustbase r-rcolorbrewer r-limma
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stamats/MKmisc")
    (synopsis "Miscellaneous Functions from M. Kohl")
    (description
     "This package contains several functions for statistical data analysis; e.g. for
sample size and power calculations, computation of confidence intervals and
tests, and generation of similarity matrices.")
    (license license:lgpl3)))

(define-public r-mkmeans
  (package
    (name "r-mkmeans")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKMeans" version))
       (sha256
        (base32 "1gfh53v65g7c102w787wvjzdnw3n1ifx0f5hf8mwz4l6s6nfz3r3"))))
    (properties `((upstream-name . "MKMeans")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MKMeans")
    (synopsis "Modern K-Means (MKMeans) Clustering Algorithm")
    (description
     "It's a Modern K-Means clustering algorithm allowing data of any number of
dimensions, any initial center, and any number of clusters to expect.")
    (license license:gpl2)))

(define-public r-mkle
  (package
    (name "r-mkle")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKLE" version))
       (sha256
        (base32 "0149l4ycn8f1v2ndfvl167xj7zb4zyi4wzaz12d9d5q5igavyx5x"))))
    (properties `((upstream-name . "MKLE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MKLE")
    (synopsis "Maximum Kernel Likelihood Estimation")
    (description
     "Package for fast computation of the maximum kernel likelihood estimator (mkle).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mkinfer
  (package
    (name "r-mkinfer")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKinfer" version))
       (sha256
        (base32 "10fy49b5bprbc30zrgz1y74pp480x3avrk9pig6mj9gg6fci207p"))))
    (properties `((upstream-name . "MKinfer")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme
                             r-mkdescr
                             r-miceadds
                             r-ggplot2
                             r-exactranktests
                             r-boot
                             r-arrangements))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stamats/MKinfer")
    (synopsis "Inferential Statistics")
    (description
     "Computation of various confidence intervals (Altman et al. (2000),
ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2)
including bootstrapped versions (Davison and Hinkley (1997),
ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018),
ISBN:978-3-662-56657-2), permutation (Janssen (1997),
<doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997),
ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015),
ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999),
<doi:10.1093/biomet/86.4.948>) t-test; furthermore, computation of
intersection-union z-test as well as multiple imputation Wilcoxon tests.
Graphical visualization by volcano and Bland-Altman plots (Bland and Altman
(1986), <doi:10.1016/S0140-6736(86)90837-8>; Shieh (2018),
<doi:10.1186/s12874-018-0505-y>).")
    (license license:lgpl3)))

(define-public r-mkin
  (package
    (name "r-mkin")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mkin" version))
       (sha256
        (base32 "1k90z1kqk57rib0f7rm23qmcvk942b0rggbjfyc7jzrba3v65qpb"))))
    (properties `((upstream-name . "mkin")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-saemix
                             r-rlang
                             r-r6
                             r-pkgbuild
                             r-numderiv
                             r-nlme
                             r-lmtest
                             r-inline
                             r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://pkgdown.jrwb.de/mkin/")
    (synopsis "Kinetic Evaluation of Chemical Degradation Data")
    (description
     "Calculation routines based on the FOCUS Kinetics Report (2006, 2014).  Includes
a function for conveniently defining differential equation models, model
solution based on eigenvalues if possible or using numerical solvers.  If a C
compiler (on windows: Rtools') is installed, differential equation models are
solved using automatically generated C functions.  Heteroscedasticity can be
taken into account using variance by variable or two-component error models as
described by Ranke and Meinecke (2018) <doi:10.3390/environments6120124>.
Hierarchical degradation models can be fitted using nonlinear mixed-effects
model packages as a back end as described by Ranke et al. (2021)
<doi:10.3390/environments8080071>.  Please note that no warranty is implied for
correctness of results or fitness for a particular purpose.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mkendall
  (package
    (name "r-mkendall")
    (version "1.5-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKendall" version))
       (sha256
        (base32 "15ldzsz99i30x05bi73j3wny1xl0gzdxz3jqjq7dfygdb2gyz1jy"))))
    (properties `((upstream-name . "MKendall")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MKendall")
    (synopsis "Matrix Kendall's Tau and Matrix Elliptical Factor Model")
    (description
     "Large-scale matrix-variate data have been widely observed nowadays in various
research areas such as finance, signal processing and medical imaging.
Modelling matrix-valued data by matrix-elliptical family not only provides a
flexible way to handle heavy-tail property and tail dependencies, but also
maintains the intrinsic row and column structure of random matrices.  We
proposed a new tool named matrix Kendall's tau which is efficient for analyzing
random elliptical matrices.  By applying this new type of Kendellâs tau to the
matrix elliptical factor model, we propose a Matrix-type Robust Two-Step (MRTS)
method to estimate the loading and factor spaces.  See the details in He at al.
(2022) <@code{arXiv:2207.09633>}.  In this package, we provide the algorithms
for calculating sample matrix Kendall's tau, the MRTS method and the Matrix
Kendall's tau Eigenvalue-Ratio (MKER) method which is used for determining the
number of factors.")
    (license license:gpl2)))

(define-public r-mkdescr
  (package
    (name "r-mkdescr")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKdescr" version))
       (sha256
        (base32 "0y4mg3lg94nvyvj8rns5lffd6qjw1yn6yw8kn03n94rig857amwp"))))
    (properties `((upstream-name . "MKdescr")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stamats/MKdescr")
    (synopsis "Descriptive Statistics")
    (description
     "Computation of standardized interquartile range (IQR), Huber-type skipped mean
(Hampel (1985), <doi:10.2307/1268758>), robust coefficient of variation (CV)
(Arachchige et al. (2019), <@code{arXiv:1907.01110>}), robust signal to noise
ratio (SNR), z-score, standardized mean difference (SMD), as well as functions
that support graphical visualization such as boxplots based on quartiles (not
hinges), negative logarithms and generalized logarithms for ggplot2 (Wickham
(2016), ISBN:978-3-319-24277-4).")
    (license license:lgpl3)))

(define-public r-mkde
  (package
    (name "r-mkde")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mkde" version))
       (sha256
        (base32 "1g6g8zh0inza6lak4l6az2czqz22c8sf1pbcakibbhc5f1h2b8g9"))))
    (properties `((upstream-name . "mkde")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-stars r-sf r-rcpp))
    (home-page "https://cran.r-project.org/package=mkde")
    (synopsis "2D and 3D Movement-Based Kernel Density Estimates (MKDEs)")
    (description
     "This package provides functions to compute and visualize movement-based kernel
density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial
dimensions.")
    (license license:gpl3+)))

(define-public r-mkclass
  (package
    (name "r-mkclass")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MKclass" version))
       (sha256
        (base32 "1f6k00552vs3fql35mxrpqq52a8jw5g253k5a81shc11j9qapg9x"))))
    (properties `((upstream-name . "MKclass")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stamats/MKclass")
    (synopsis "Statistical Classification")
    (description
     "Performance measures and scores for statistical classification such as accuracy,
sensitivity, specificity, recall, similarity coefficients, AUC, GINI index,
Brier score and many more.  Calculation of optimal cut-offs and decision stumps
(Iba and Langley (1991), <doi:10.1016/B978-1-55860-247-2.50035-8>) for all
implemented performance measures.  Hosmer-Lemeshow goodness of fit tests
(Lemeshow and Hosmer (1982), <doi:10.1093/oxfordjournals.aje.a113284>; Hosmer et
al (1997),
<doi:10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O>).
Statistical and epidemiological risk measures such as relative risk, odds ratio,
number needed to treat (Porta (2014),
<doi:10.1093%2Facref%2F9780199976720.001.0001>).")
    (license license:lgpl3)))

(define-public r-mjmbamlss
  (package
    (name "r-mjmbamlss")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MJMbamlss" version))
       (sha256
        (base32 "03alz38375lks1597646cp5xcfc9vivz3bbz7gwniljn2vznrcfq"))))
    (properties `((upstream-name . "MJMbamlss")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-statmod
                             r-sparseflmm
                             r-refund
                             r-rcppeigen
                             r-rcpp
                             r-mvtnorm
                             r-mgcv
                             r-mfpca
                             r-matrix
                             r-gamm4
                             r-fundata
                             r-foreach
                             r-fdapace
                             r-coda
                             r-bamlss))
    (home-page "https://cran.r-project.org/package=MJMbamlss")
    (synopsis "Multivariate Joint Models with 'bamlss'")
    (description
     "Multivariate joint models of longitudinal and time-to-event data based on
functional principal components implemented with bamlss'.  Implementation for
Volkmann, Umlauf, Greven (2023) <@code{arXiv:2311.06409>}.")
    (license license:gpl3)))

(define-public r-mizer
  (package
    (name "r-mizer")
    (version "2.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mizer" version))
       (sha256
        (base32 "01yb00mrp17cihms80nd5qnmmrvik9m2mvnq623fw3038ipbfkb1"))))
    (properties `((upstream-name . "mizer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-reshape2
                             r-rcpp
                             r-progress
                             r-plyr
                             r-plotly
                             r-lubridate
                             r-lifecycle
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-desolve
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://sizespectrum.org/mizer/")
    (synopsis "Dynamic Multi-Species Size Spectrum Modelling")
    (description
     "This package provides a set of classes and methods to set up and run
multi-species, trait based and community size spectrum ecological models,
focused on the marine environment.")
    (license license:gpl3)))

(define-public r-mize
  (package
    (name "r-mize")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mize" version))
       (sha256
        (base32 "1dfhldxsm1il9avfxvaiir9r878dp7swyc9c8ilm6llx208sd5d5"))))
    (properties `((upstream-name . "mize")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jlmelville/mize")
    (synopsis "Unconstrained Numerical Optimization Algorithms")
    (description
     "Optimization algorithms implemented in R, including conjugate gradient (CG),
Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS)
methods.  Most internal parameters can be set through the call interface.  The
solvers hold up quite well for higher-dimensional problems.")
    (license (license:fsdg-compatible "BSD 2-clause License + file LICENSE"))))

(define-public r-mixvlmc
  (package
    (name "r-mixvlmc")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixvlmc" version))
       (sha256
        (base32 "1bx9qvdi5fq69bwa25l9sqcdyihswjhmqrsslmnqfq7wzfxy3d3q"))))
    (properties `((upstream-name . "mixvlmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-vgam
                             r-stringr
                             r-rlang
                             r-rcpp
                             r-proc
                             r-nnet
                             r-ggplot2
                             r-butcher
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fabrice-rossi/mixvlmc")
    (synopsis "Variable Length Markov Chains with Covariates")
    (description
     "Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates
models from discrete sequences.  Supports model selection via information
criteria and simulation of new sequences from an estimated model.  See
BÃ¼hlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and
Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615>
for VLMC with covariates.")
    (license license:gpl3+)))

(define-public r-mixvir
  (package
    (name "r-mixvir")
    (version "3.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixviR" version))
       (sha256
        (base32 "0npwwpb96id4zkrp4qzpiv5sgw6y4aqhhwx7v8vbg54029p6jj6w"))))
    (properties `((upstream-name . "MixviR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcfr
                             r-tidyr
                             r-stringr
                             r-shiny
                             r-readr
                             r-plotly
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-glue
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-biostrings))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mikesovic/MixviR")
    (synopsis "Analysis and Exploration of Mixed Microbial Genomic Samples")
    (description
     "Tool for exploring DNA and amino acid variation and inferring the presence of
target lineages from microbial high-throughput genomic DNA samples that
potentially contain mixtures of variants/lineages. @code{MixviR} was originally
created to help analyze environmental SARS-@code{CoV-2/Covid-19} samples from
environmental sources such as wastewater or dust, but can be applied to any
microbial group.  Inputs include reference genome information in commonly-used
file formats (fasta, bed) and one or more variant call format (VCF) files, which
can be generated with programs such as Illumina's DRAGEN, the Genome Analysis
Toolkit, or bcftools.  See @code{DePristo} et al (2011) <doi:10.1038/ng.806> and
Danecek et al (2021) <doi:10.1093/gigascience/giab008> for these tools,
respectively.  Available outputs include a table of mutations observed in the
sample(s), estimates of proportions of target lineages in the sample(s), and an
R Shiny dashboard to interactively explore the data.")
    (license license:gpl3)))

(define-public r-mixtwice
  (package
    (name "r-mixtwice")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixTwice" version))
       (sha256
        (base32 "11j591z1pnql84sfczvry7mg4hs0jghgp170y5j5wwhs7l5kc7r7"))))
    (properties `((upstream-name . "MixTwice")))
    (build-system r-build-system)
    (propagated-inputs (list r-iso r-fdrtool r-ashr r-alabama))
    (home-page "https://cran.r-project.org/package=MixTwice")
    (synopsis "Large-Scale Hypothesis Testing by Variance Mixing")
    (description
     "This package implements large-scale hypothesis testing by variance mixing.  It
takes two statistics per testing unit -- an estimated effect and its associated
squared standard error -- and fits a nonparametric, shape-constrained mixture
separately on two latent parameters.  It reports local false discovery rates
(lfdr) and local false sign rates (lfsr).  Manuscript describing algorithm of
@code{MixTwice}: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.")
    (license license:gpl2)))

(define-public r-mixturemissing
  (package
    (name "r-mixturemissing")
    (version "3.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixtureMissing" version))
       (sha256
        (base32 "1qlkj37znl7zjaq6hxqkwpnalg7npr8ig8lnh8bfx0cc4mix8aw4"))))
    (properties `((upstream-name . "MixtureMissing")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv
                             r-mvtnorm
                             r-mnormt
                             r-mice
                             r-mclust
                             r-mass
                             r-cluster
                             r-bessel))
    (home-page "https://cran.r-project.org/package=MixtureMissing")
    (synopsis
     "Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random")
    (description
     "Implementations of various robust and flexible model-based clustering methods
for data sets with missing values at random.  Two main models are: Multivariate
Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022,
<doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic
Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>).  Mixtures
via some special or limiting cases of the multivariate generalized hyperbolic
distribution are also included: Normal-Inverse Gaussian, Symmetric
Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal,
Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic,
and Symmetric Hyperbolic.")
    (license license:gpl2+)))

(define-public r-mixture
  (package
    (name "r-mixture")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixture" version))
       (sha256
        (base32 "0a7dy6p5lhs4sff5w1p6fha9w2vwd39fav34b0zsmyw0k4fjg2ah"))))
    (properties `((upstream-name . "mixture")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-rcppgsl r-rcpparmadillo r-rcpp r-lattice r-bh))
    (home-page "https://cran.r-project.org/package=mixture")
    (synopsis "Mixture Models for Clustering and Classification")
    (description
     "An implementation of 14 parsimonious mixture models for model-based clustering
or model-based classification.  Gaussian, Student's t, generalized hyperbolic,
variance-gamma or skew-t mixtures are available.  All approaches work with
missing data.  Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>,
Browne and @code{McNicholas} (2014) <doi:10.1007/s11634-013-0139-1>, Browne and
@code{McNicholas} (2015) <doi:10.1002/cjs.11246>.")
    (license license:gpl2+)))

(define-public r-mixtur
  (package
    (name "r-mixtur")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixtur" version))
       (sha256
        (base32 "02hybyc647jhl3jcyv26kcg1ijq4qlami18m6xyckygw8m2fb85l"))))
    (properties `((upstream-name . "mixtur")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-ggplot2 r-dplyr))
    (home-page "https://github.com/JimGrange/mixtur")
    (synopsis "Modelling Continuous Report Visual Short-Term Memory Studies")
    (description
     "This package provides a set of utility functions for analysing and modelling
data from continuous report short-term memory experiments using either the
2-component mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or
the 3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>.  Users
are also able to simulate from these models.")
    (license license:gpl3)))

(define-public r-mixtox
  (package
    (name "r-mixtox")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixtox" version))
       (sha256
        (base32 "1lca26s80f92c18wkpp5gs2ifylnyqyg933w7r5ir3xvsfbk0lfh"))))
    (properties `((upstream-name . "mixtox")))
    (build-system r-build-system)
    (propagated-inputs (list r-minpack-lm))
    (home-page "https://github.com/ichxw/mixtox")
    (synopsis "Dose Response Curve Fitting and Mixture Toxicity Assessment")
    (description
     "Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response
data.  Predicting mixture toxicity based on reference models such as
concentration addition', independent action', and generalized concentration
addition'.")
    (license license:gpl2)))

(define-public r-mixssg
  (package
    (name "r-mixssg")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixSSG" version))
       (sha256
        (base32 "0aqzyfpq568vgk2rjh7i93azfa990mvapddcdjwh3xyyb3flsas4"))))
    (properties `((upstream-name . "mixSSG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-mass r-ars))
    (home-page "https://cran.r-project.org/package=mixSSG")
    (synopsis "Clustering Using Mixtures of Sub Gaussian Stable Distributions")
    (description
     "Developed for model-based clustering using the finite mixtures of skewed
sub-Gaussian stable distributions developed by Teimouri (2022)
<@code{arXiv:2205.14067>} and estimating parameters of the symmetric stable
distribution within the Bayesian framework.")
    (license license:gpl2+)))

(define-public r-mixspe
  (package
    (name "r-mixspe")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixSPE" version))
       (sha256
        (base32 "1fcafsvnv9hgh0cnjqwfj09y4fw3scm888fnnax3pjialklzmxh7"))))
    (properties `((upstream-name . "mixSPE")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=mixSPE")
    (synopsis
     "Mixtures of Power Exponential and Skew Power Exponential Distributions for Use in Model-Based Clustering and Classification")
    (description
     "Mixtures of skewed and elliptical distributions are implemented using mixtures
of multivariate skew power exponential and power exponential distributions,
respectively.  A generalized expectation-maximization framework is used for
parameter estimation.  See citation() for how to cite.")
    (license license:gpl2+)))

(define-public r-mixsmsn
  (package
    (name "r-mixsmsn")
    (version "1.1-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixsmsn" version))
       (sha256
        (base32 "1w31br65la62zw847bw4bz0shvgna680wagxnqmc71kpy4a9mv3x"))))
    (properties `((upstream-name . "mixsmsn")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=mixsmsn")
    (synopsis
     "Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions")
    (description
     "This package provides functions to fit finite mixture of scale mixture of
skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013)
<doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012)
<doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010)
<doi:10.1016/j.csda.2009.09.031>.")
    (license license:gpl2+)))

(define-public r-mixsim
  (package
    (name "r-mixsim")
    (version "1.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixSim" version))
       (sha256
        (base32 "0zfk3qkwap6p014jfdkl1ng5w4mqdm4klij7cnkx8zsgigs938lk"))))
    (properties `((upstream-name . "MixSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MixSim")
    (synopsis "Simulating Data to Study Performance of Clustering Algorithms")
    (description
     "The utility of this package is in simulating mixtures of Gaussian distributions
with different levels of overlap between mixture components.  Pairwise overlap,
defined as a sum of two misclassification probabilities, measures the degree of
interaction between components and can be readily employed to control the
clustering complexity of datasets simulated from mixtures.  These datasets can
then be used for systematic performance investigation of clustering and finite
mixture modeling algorithms.  Among other capabilities of @code{MixSim}', there
are computing the exact overlap for Gaussian mixtures, simulating Gaussian and
non-Gaussian data, simulating outliers and noise variables, calculating various
measures of agreement between two partitionings, and constructing parallel
distribution plots for the graphical display of finite mixture models.")
    (license license:gpl2+)))

(define-public r-mixsiar
  (package
    (name "r-mixsiar")
    (version "3.1.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixSIAR" version))
       (sha256
        (base32 "07wiv7x2c9mhqk37zndxvdb76m9shizc48qz2bp2f4g5z9hciid7"))))
    (properties `((upstream-name . "MixSIAR")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-splancs
                             r-reshape2
                             r-reshape
                             r-rcolorbrewer
                             r-r2jags
                             r-mcmcpack
                             r-mass
                             r-loo
                             r-lattice
                             r-ggplot2
                             r-ggmcmc
                             r-coda
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brianstock/MixSIAR")
    (synopsis "Bayesian Mixing Models in R")
    (description
     "This package creates and runs Bayesian mixing models to analyze biological
tracer data (i.e.  stable isotopes, fatty acids), which estimate the proportions
of source (prey) contributions to a mixture (consumer). @code{MixSIAR} is not
one model, but a framework that allows a user to create a mixing model based on
their data structure and research questions, via options for fixed/ random
effects, source data types, priors, and error terms. @code{MixSIAR} incorporates
several years of advances since @code{MixSIR} and SIAR'.")
    (license license:gpl3)))

(define-public r-mixsemirob
  (package
    (name "r-mixsemirob")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixSemiRob" version))
       (sha256
        (base32 "0rvpwb4skd5s0f7qnm2mjrhfz4ppa6inqyfz970mpycihig3vilc"))))
    (properties `((upstream-name . "MixSemiRob")))
    (build-system r-build-system)
    (propagated-inputs (list r-ucminf
                             r-robustbase
                             r-rlab
                             r-quadprog
                             r-pracma
                             r-mvtnorm
                             r-mixtools
                             r-mass
                             r-gofkernel))
    (home-page "https://cran.r-project.org/package=MixSemiRob")
    (synopsis "Mixture Models: Parametric, Semiparametric, and Robust")
    (description
     "Various functions are provided to estimate parametric mixture models (with
Gaussian, t, Laplace, log-concave distributions, etc.) and non-parametric
mixture models.  The package performs hypothesis tests and addresses label
switching issues in mixture models.  The package also allows for parameter
estimation in mixture of regressions, proportion-varying mixture of regressions,
and robust mixture of regressions.")
    (license license:gpl2+)))

(define-public r-mixsal
  (package
    (name "r-mixsal")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixSAL" version))
       (sha256
        (base32 "15dymyzfs3bwskaf6xndj291g6nhw7k1q93y1cba55a6fdjh5qz9"))))
    (properties `((upstream-name . "MixSAL")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MixSAL")
    (synopsis
     "Mixtures of Multivariate Shifted Asymmetric Laplace (SAL) Distributions")
    (description
     "The current version of the @code{MixSAL} package allows users to generate data
from a multivariate SAL distribution or a mixture of multivariate SAL
distributions, evaluate the probability density function of a multivariate SAL
distribution or a mixture of multivariate SAL distributions, and fit a mixture
of multivariate SAL distributions using the Expectation-Maximization (EM)
algorithm (see Franczak et.  al, 2014, <doi:10.1109/TPAMI.2013.216>, for
details).")
    (license license:gpl2+)))

(define-public r-mixrf
  (package
    (name "r-mixrf")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixRF" version))
       (sha256
        (base32 "1av1wx7wmfc3z8a4bc6g3i3b807nc28n3s2rs3yh0rcwq1pfl0ir"))))
    (properties `((upstream-name . "MixRF")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-lme4 r-foreach r-doparallel))
    (home-page "https://github.com/randel/MixRF")
    (synopsis
     "Random-Forest-Based Approach for Imputing Clustered Incomplete Data")
    (description
     "It offers random-forest-based functions to impute clustered incomplete data.
The package is tailored for but not limited to imputing multitissue expression
data, in which a gene's expression is measured on the collected tissues of an
individual but missing on the uncollected tissues.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mixraschtools
  (package
    (name "r-mixraschtools")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixRaschTools" version))
       (sha256
        (base32 "1giq6d97sg6f66sh0gw6x1f2ax4ys91kfnrlpdv37m7qakk5npyj"))))
    (properties `((upstream-name . "mixRaschTools")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mixRaschTools")
    (synopsis
     "Plotting and Average Theta Functions for Multiple Class Mixed Rasch Models")
    (description
     "This package provides supplemental functions for the @code{mixRasch} package
(Willse, 2014),
<https://cran.r-project.org/package=@code{mixRasch/mixRasch.pdf>} including a
plotting function to compare item parameters for multiple class models and a
function that provides average theta values for each class in a mixture model.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mixr
  (package
    (name "r-mixr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixR" version))
       (sha256
        (base32 "1iarsys7jyi3zck01y7415xwyd1knnh1fkc8z4ipfd63nnlm2pqc"))))
    (properties `((upstream-name . "mixR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mixR")
    (synopsis "Finite Mixture Modeling for Raw and Binned Data")
    (description
     "This package performs maximum likelihood estimation for finite mixture models
for families including Normal, Weibull, Gamma and Lognormal by using EM
algorithm, together with Newton-Raphson algorithm or bisection method when
necessary.  It also conducts mixture model selection by using information
criteria or bootstrap likelihood ratio test.  The data used for mixture model
fitting can be raw data or binned data.  The model fitting process is
accelerated by using R package Rcpp'.")
    (license license:gpl2+)))

(define-public r-mixpoissonreg
  (package
    (name "r-mixpoissonreg")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixpoissonreg" version))
       (sha256
        (base32 "1pk45p96kwl3gvi5523vm824s1bb5aqpn9qqpqy8kjy2pd2a4k32"))))
    (properties `((upstream-name . "mixpoissonreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-statmod
                             r-rlang
                             r-rfast
                             r-pbapply
                             r-magrittr
                             r-lmtest
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-generics
                             r-gamlss-dist
                             r-gamlss
                             r-formula
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/vpnsctl/mixpoissonreg/")
    (synopsis "Mixed Poisson Regression for Overdispersed Count Data")
    (description
     "Fits mixed Poisson regression models (Poisson-Inverse Gaussian or
Negative-Binomial) on data sets with response variables being count data.  The
models can have varying precision parameter, where a linear regression structure
(through a link function) is assumed to hold on the precision parameter.  The
Expectation-Maximization algorithm for both these models (Poisson Inverse
Gaussian and Negative Binomial) is an important contribution of this package.
Another important feature of this package is the set of functions to perform
global and local influence analysis.  See Barreto-Souza and Simas (2016)
<doi:10.1007/s11222-015-9601-6> for further details.")
    (license license:gpl2)))

(define-public r-mixphm
  (package
    (name "r-mixphm")
    (version "0.7-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixPHM" version))
       (sha256
        (base32 "1wvkdb9zj2j8dpppnyins05rg877zbydqsl3qaan62wznkknxcac"))))
    (properties `((upstream-name . "mixPHM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-lattice))
    (home-page "https://cran.r-project.org/package=mixPHM")
    (synopsis "Mixtures of Proportional Hazard Models")
    (description
     "Fits multiple variable mixtures of various parametric proportional hazard models
using the EM-Algorithm.  Proportionality restrictions can be imposed on the
latent groups and/or on the variables.  Several survival distributions can be
specified.  Missing values and censored values are allowed.  Independence is
assumed over the single variables.")
    (license license:gpl2)))

(define-public r-mixoptim
  (package
    (name "r-mixoptim")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixOptim" version))
       (sha256
        (base32 "14blyb1dqbdb14rby4nrggpqdx6b43lbb2xwi3059biq9mrj3a2x"))))
    (properties `((upstream-name . "MixOptim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-patchwork r-ggplot2 r-desirability))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MixOptim")
    (synopsis "Mixture Optimization Algorithm")
    (description
     "Simple tools to perform mixture optimization based on the desirability package
by Max Kuhn.  It also provides a plot routine using ggplot2 and patchwork'.")
    (license license:gpl2)))

(define-public r-mixopt
  (package
    (name "r-mixopt")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixopt" version))
       (sha256
        (base32 "0zgdkazsfmrqn5k4qanc18mw0scjwjyfzh9jwgjrbamv36ygry6n"))))
    (properties `((upstream-name . "mixopt")))
    (build-system r-build-system)
    (propagated-inputs (list r-splitfngr r-ggplot2 r-dplyr))
    (home-page "https://github.com/CollinErickson/mixopt")
    (synopsis "Mixed Variable Optimization")
    (description
     "Mixed variable optimization for non-linear functions.  Can optimize function
whose inputs are a combination of continuous, ordered, and unordered variables.")
    (license license:lgpl3+)))

(define-public r-mixoofa
  (package
    (name "r-mixoofa")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixOofA" version))
       (sha256
        (base32 "1npa4hbs9xgirb4smsbjpwvk8jk8nh4y6i644lwdsr3zf34rxr7d"))))
    (properties `((upstream-name . "mixOofA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-mixexp r-doofa r-crossdes r-combinat))
    (home-page "https://cran.r-project.org/package=mixOofA")
    (synopsis "Design and Analysis of Order-of-Addition Mixture Experiments")
    (description
     "This package provides a facility to generate various classes of fractional
designs for order-of-addition experiments namely fractional order-of-additions
orthogonal arrays, see Voelkel, Joseph G. (2019). \"The design of
order-of-addition experiments.\" Journal of Quality Technology 51:3, 230-241,
<doi:10.1080/00224065.2019.1569958>.  Provides facility to construct component
orthogonal arrays, see Jian-Feng Yang, Fasheng Sun and Hongquan Xu (2020). \"A
Component Position Model, Analysis and Design for Order-of-Addition
Experiments.\" Technometrics, <doi:10.1080/00401706.2020.1764394>.  Supports
generation of fractional designs for order-of-addition mixture experiments.
Analysis of data from order-of-addition mixture experiments is also supported.")
    (license license:gpl2+)))

(define-public r-mixmeta
  (package
    (name "r-mixmeta")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixmeta" version))
       (sha256
        (base32 "093f6jjq0hc49k5kmjp17s2lv3swpssfxnra80bwv59ggav8w8d8"))))
    (properties `((upstream-name . "mixmeta")))
    (build-system r-build-system)
    (home-page "https://github.com/gasparrini/mixmeta")
    (synopsis "An Extended Mixed-Effects Framework for Meta-Analysis")
    (description
     "This package provides a collection of functions to perform various
meta-analytical models through a unified mixed-effects framework, including
standard univariate fixed and random-effects meta-analysis and meta-regression,
and non-standard extensions such as multivariate, multilevel, longitudinal, and
dose-response models.")
    (license license:gpl2+)))

(define-public r-mixmatrix
  (package
    (name "r-mixmatrix")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixMatrix" version))
       (sha256
        (base32 "0idrci3gksvm0am02dqlj9yfnfk64rnj8hd3hqkx7amcxpv30i7z"))))
    (properties `((upstream-name . "MixMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-glue r-cholwishart))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gzt/MixMatrix/")
    (synopsis "Classification with Matrix Variate Normal and t Distributions")
    (description
     "This package provides sampling and density functions for matrix variate normal,
t, and inverted t distributions; ML estimation for matrix variate normal and t
distributions using the EM algorithm, including some restrictions on the
parameters; and classification by linear and quadratic discriminant analysis for
matrix variate normal and t distributions described in Thompson et al. (2019)
<doi:10.1080/10618600.2019.1696208>.  Performs clustering with matrix variate
normal and t mixture models.")
    (license license:gpl3)))

(define-public r-mixlm
  (package
    (name "r-mixlm")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixlm" version))
       (sha256
        (base32 "0ghs818zdlrjs390223qrr69amx3ca9zfdc7w1sapj5np70wmljb"))))
    (properties `((upstream-name . "mixlm")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-pls r-multcomp r-leaps r-car))
    (home-page "https://github.com/khliland/mixlm/")
    (synopsis "Mixed Model ANOVA and Statistics for Education")
    (description
     "The main functions perform mixed models analysis by least squares or REML by
adding the function r() to formulas of lm() and glm().  A collection of
text-book statistics for higher education is also included, e.g. modifications
of the functions lm(), glm() and associated summaries from the package stats'.")
    (license license:gpl2+)))

(define-public r-mixlfa
  (package
    (name "r-mixlfa")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixLFA" version))
       (sha256
        (base32 "1vp157cppsd6izag8vhadw6z1s4bay3jvq7wsw2mja7wvgc3p5kq"))))
    (properties `((upstream-name . "MixLFA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-pheatmap
                             r-gparotation
                             r-ggplot2
                             r-ggally
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=MixLFA")
    (synopsis "Mixture of Longitudinal Factor Analysis Methods")
    (description
     "This package provides a function for the estimation of mixture of longitudinal
factor analysis models using the iterative expectation-maximization algorithm
(Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) <doi:10.1002/sim.9804>)
and several tools for visualizing and interpreting the models parameters.")
    (license license:gpl3)))

(define-public r-mixl
  (package
    (name "r-mixl")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixl" version))
       (sha256
        (base32 "1j3mqbrjgib57cyirmqgsx6livm63bx9xzps2j4fa6wgivwbk5xr"))))
    (properties `((upstream-name . "mixl")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sandwich
                             r-readr
                             r-rcpp
                             r-randtoolbox
                             r-numderiv
                             r-maxlik))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/joemolloy/fast-mixed-mnl")
    (synopsis
     "Simulated Maximum Likelihood Estimation of Mixed Logit Models for Large Datasets")
    (description
     "Specification and estimation of multinomial logit models.  Large datasets and
complex models are supported, with an intuitive syntax.  Multinomial Logit
Models, Mixed models, random coefficients and Hybrid Choice are all supported.
For more information, see Molloy et al. (2021)
<https://www.research-collection.ethz.ch/handle/20.500.11850/477416>.")
    (license license:gpl2+)))

(define-public r-mixkernel
  (package
    (name "r-mixkernel")
    (version "0.9-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixKernel" version))
       (sha256
        (base32 "0vak6qfhva666mjqwn16ivcdjsw31qic36g62g4p3ng2jvv57f32"))))
    (properties `((upstream-name . "mixKernel")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-reticulate
                             r-quadprog
                             r-psych
                             r-phyloseq
                             r-mixomics
                             r-matrix
                             r-markdown
                             r-ldrtools
                             r-ggplot2
                             r-corrplot))
    (native-inputs (list r-knitr))
    (home-page "http://mixkernel.clementine.wf")
    (synopsis "Omics Data Integration Using Kernel Methods")
    (description
     "Kernel-based methods are powerful methods for integrating heterogeneous types of
data. @code{mixKernel} aims at providing methods to combine kernel for
unsupervised exploratory analysis.  Different solutions are provided to compute
a meta-kernel, in a consensus way or in a way that best preserves the original
topology of the data. @code{mixKernel} also integrates kernel PCA to visualize
similarities between samples in a non linear space and from the multiple source
point of view <doi:10.1093/bioinformatics/btx682>.  A method to select (as well
as funtions to display) important variables is also provided
<doi:10.1093/nargab/lqac014>.")
    (license license:gpl2+)))

(define-public r-mixindependr
  (package
    (name "r-mixindependr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixIndependR" version))
       (sha256
        (base32 "0wjzs56axdjas2a9ynw3gd78h0fgcfjpx3bk7nm895lmadyzih6y"))))
    (properties `((upstream-name . "mixIndependR")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ice4prince/mixIndependR")
    (synopsis "Genetics and Independence Testing of Mixed Genetic Panels")
    (description
     "Developed to deal with multi-locus genotype data, this package is especially
designed for those panel which include different type of markers.  Basic genetic
parameters like allele frequency, genotype frequency, heterozygosity and
Hardy-Weinberg test of mixed genetic data can be obtained.  In addition, a new
test for mutual independence which is compatible for mixed genetic data is
developed in this package.")
    (license license:gpl2+)))

(define-public r-mixhvg
  (package
    (name "r-mixhvg")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixhvg" version))
       (sha256
        (base32 "1n41k5jrx5ffxsxaw22smy2gdmx8qr8bdawwmhg0v2qyswny3kdi"))))
    (properties `((upstream-name . "mixhvg")))
    (build-system r-build-system)
    (propagated-inputs (list r-singlecellexperiment r-seurat r-scuttle r-scran
                             r-matrix))
    (home-page "https://cran.r-project.org/package=mixhvg")
    (synopsis "Mixture of Multiple Highly Variable Feature Selection Methods")
    (description
     "Highly variable gene selection methods, including popular public available
methods, and also the mixture of multiple highly variable gene selection
methods, <https://github.com/@code{RuzhangZhao/mixhvg>}.  Reference:
<doi:10.1101/2024.08.25.608519>.")
    (license license:gpl3)))

(define-public r-mixghd
  (package
    (name "r-mixghd")
    (version "2.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixGHD" version))
       (sha256
        (base32 "0l1qivl9a5b8rayg9srxlgkk4wilv2m9yw4lsca29s5wd3wpyksv"))))
    (properties `((upstream-name . "MixGHD")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv
                             r-mvtnorm
                             r-mixture
                             r-mass
                             r-ghyp
                             r-e1071
                             r-cluster
                             r-bessel))
    (home-page "https://cran.r-project.org/package=MixGHD")
    (synopsis
     "Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions")
    (description
     "Carries out model-based clustering, classification and discriminant analysis
using five different models.  The models are all based on the generalized
hyperbolic distribution.  The first model MGHD (Browne and @code{McNicholas}
(2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized
hyperbolic distributions.  The MGHFA (Tortora et al. (2016)
<doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor
analyzers for high dimensional data sets.  The MSGHD is the mixture of multiple
scaled generalized hyperbolic distributions, the @code{cMSGHD} is a MSGHD with
convex contour plots and the MCGHD', mixture of coalesced generalized hyperbolic
distributions is a new more flexible model (Tortora et al.
(2019)<doi:10.1007/s00357-019-09319-3>.  The paper related to the software can
be found at <doi:10.18637/jss.v098.i03>.")
    (license license:gpl2+)))

(define-public r-mixgb
  (package
    (name "r-mixgb")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixgb" version))
       (sha256
        (base32 "1i6sckdbgckp84wvmsbay20vfqqzzphif2r7v3fc158gfw1vvkr4"))))
    (properties `((upstream-name . "mixgb")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-tidyr
                             r-scales
                             r-rlang
                             r-rfast
                             r-mice
                             r-matrix
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/agnesdeng/mixgb")
    (synopsis "Multiple Imputation Through 'XGBoost'")
    (description
     "Multiple imputation using XGBoost', subsampling, and predictive mean matching as
described in Deng and Lumley (2023) <@code{arXiv:2106.01574>}.  Our method
utilizes the capabilities of XGBoost, a highly efficient implementation of
gradient boosted trees, to capture interactions and non-linear relations
automatically.  Moreover, we have integrated subsampling and predictive mean
matching to minimize bias and reflect appropriate imputation variability.  This
package supports various types of variables and offers flexible settings for
subsampling and predictive mean matching.  Additionally, it includes diagnostic
tools for evaluating the quality of the imputed values.")
    (license license:gpl3+)))

(define-public r-mixfmri
  (package
    (name "r-mixfmri")
    (version "0.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixfMRI" version))
       (sha256
        (base32 "1y09q50fc8kj08nv30d87z1vqxfp85rnjkgn0bgyad2rwsm59f5l"))))
    (properties `((upstream-name . "MixfMRI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-mixsim
                             r-matrix
                             r-mass
                             r-fftw
                             r-emcluster))
    (home-page "https://github.com/snoweye/MixfMRI")
    (synopsis "Mixture fMRI Clustering Analysis")
    (description
     "Utilizing model-based clustering (unsupervised) for functional magnetic
resonance imaging (@code{fMRI}) data.  The developed methods (Chen and Maitra
(2023) <doi:10.1002/hbm.26425>) include 2D and 3D clustering analyses (for
p-values with voxel locations) and segmentation analyses (for p-values alone)
for @code{fMRI} data where p-values indicate significant level of activation
responding to stimulate of interesting.  The analyses are mainly identifying
active voxel/signal associated with normal brain behaviors.  Analysis pipelines
(R scripts) utilizing this package (see examples in inst/workflow/') is also
implemented with high performance techniques.")
    (license (license:fsdg-compatible "Mozilla Public License 2.0"))))

(define-public r-mixfim
  (package
    (name "r-mixfim")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIXFIM" version))
       (sha256
        (base32 "1xx25b7hfyzynfh3vsqrkriqnqnn173gn8j1vwqmc434c1vy4nx2"))))
    (properties `((upstream-name . "MIXFIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstan r-mvtnorm r-ggplot2))
    (home-page "https://cran.r-project.org/package=MIXFIM")
    (synopsis "Evaluation of the FIM in NLMEMs using MCMC")
    (description
     "Evaluation and optimization of the Fisher Information Matrix in @code{NonLinear}
Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete
data.")
    (license license:gpl3)))

(define-public r-mixexp
  (package
    (name "r-mixexp")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixexp" version))
       (sha256
        (base32 "06bp8w9r6fxhnp2ga3hig7fbl2nsvfvwxggbfd7nfc60c91p80mq"))))
    (properties `((upstream-name . "mixexp")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-daewr))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=mixexp")
    (synopsis "Design and Analysis of Mixture Experiments")
    (description
     "This package provides functions for creating designs for mixture experiments,
making ternary contour plots, and making mixture effect plots.")
    (license license:gpl2+)))

(define-public r-mixedts
  (package
    (name "r-mixedts")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixedTS" version))
       (sha256
        (base32 "0gwcg115idbcm5llgzqsygvqgshq8dywawxkaddsmw4sbbhj4555"))))
    (properties `((upstream-name . "MixedTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MixedTS")
    (synopsis "Mixed Tempered Stable Distribution")
    (description
     "We provide detailed functions for univariate Mixed Tempered Stable distribution.")
    (license license:gpl2+)))

(define-public r-mixedsde
  (package
    (name "r-mixedsde")
    (version "5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixedsde" version))
       (sha256
        (base32 "0ss1ng4nx91gxj85wxxfyh9ljxsj3ag6lsj1a5d2815p9jzl7dsv"))))
    (properties `((upstream-name . "mixedsde")))
    (build-system r-build-system)
    (propagated-inputs (list r-sde r-plot3d r-moments r-mass))
    (home-page "https://cran.r-project.org/package=mixedsde")
    (synopsis
     "Estimation Methods for Stochastic Differential Mixed Effects Models")
    (description
     "Inference on stochastic differential models Ornstein-Uhlenbeck or
Cox-Ingersoll-Ross, with one or two random effects in the drift function.")
    (license license:gpl2+)))

(define-public r-mixedpsy
  (package
    (name "r-mixedpsy")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixedPsy" version))
       (sha256
        (base32 "1iz3cl19xlznzbqxvichbjhyhams4v9611p9094r6q5fpmf71p88"))))
    (properties `((upstream-name . "MixedPsy")))
    (build-system r-build-system)
    (propagated-inputs (list r-mnormt
                             r-matrix
                             r-lme4
                             r-ggplot2
                             r-brglm
                             r-boot
                             r-beepr))
    (home-page "https://mixedpsychophysics.wordpress.com")
    (synopsis "Statistical Tools for the Analysis of Psychophysical Data")
    (description
     "This package provides tools for the analysis of psychophysical data in R. This
package allows to estimate the Point of Subjective Equivalence (PSE) and the
Just Noticeable Difference (JND), either from a psychometric function or from a
Generalized Linear Mixed Model (GLMM).  Additionally, the package allows
plotting the fitted models and the response data, simulating psychometric
functions of different shapes, and simulating data sets.  For a description of
the use of GLMMs applied to psychophysical data, refer to Moscatelli et al.
(2012).")
    (license license:gpl2+)))

(define-public r-mixedpoisson
  (package
    (name "r-mixedpoisson")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixedPoisson" version))
       (sha256
        (base32 "0g5mpzj0wda6g46q3cg5mm3x4dvpz9gdclzn2m17l62lzny2i10p"))))
    (properties `((upstream-name . "MixedPoisson")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmpfr r-mass r-gaussquad))
    (home-page "https://cran.r-project.org/package=MixedPoisson")
    (synopsis "Mixed Poisson Models")
    (description "The estimation of the parameters in mixed Poisson models.")
    (license license:gpl2)))

(define-public r-mixedmem
  (package
    (name "r-mixedmem")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixedMem" version))
       (sha256
        (base32 "1p0gplfcn663xp40dk0yg70qffn1fwa9gxv7bppflgm9wkn8g3l5"))))
    (properties `((upstream-name . "mixedMem")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-gtools r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mixedMem")
    (synopsis "Tools for Discrete Multivariate Mixed Membership Models")
    (description
     "Fits mixed membership models with discrete multivariate data (with or without
repeated measures) following the general framework of Erosheva et al (2004).
This package uses a Variational EM approach by approximating the posterior
distribution of latent memberships and selecting hyperparameters through a
pseudo-MLE procedure.  Currently supported data types are Bernoulli, multinomial
and rank (Plackett-Luce).  The extended @code{GoM} model with fixed stayers from
Erosheva et al (2007) is now also supported.  See Airoldi et al (2014) for other
examples of mixed membership models.")
    (license license:gpl2+)))

(define-public r-mixedlsr
  (package
    (name "r-mixedlsr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixedLSR" version))
       (sha256
        (base32 "0a514w88k1lwcdmq7ginn4qmf0mdj747lhgxzx15nh0hp0xq26lc"))))
    (properties `((upstream-name . "mixedLSR")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-mass r-grpreg r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://alexanderjwhite.github.io/mixedLSR/")
    (synopsis
     "Mixed, Low-Rank, and Sparse Multivariate Regression on High-Dimensional Data")
    (description
     "Mixed, low-rank, and sparse multivariate regression ('@code{mixedLSR}') provides
tools for performing mixture regression when the coefficient matrix is low-rank
and sparse. @code{mixedLSR} allows subgroup identification by alternating
optimization with simulated annealing to encourage global optimum convergence.
This method is data-adaptive, automatically performing parameter selection to
identify low-rank substructures in the coefficient matrix.")
    (license license:expat)))

(define-public r-mixedlevelrsds
  (package
    (name "r-mixedlevelrsds")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixedLevelRSDs" version))
       (sha256
        (base32 "1k2angaggzhiwp7idjdl41ws4j5kp15az74p4ajy6ia878bj0sf4"))))
    (properties `((upstream-name . "MixedLevelRSDs")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MixedLevelRSDs")
    (synopsis "Mixed Level Response Surface Designs")
    (description
     "Response Surface Designs (RSDs) involving factors not all at same levels are
called Mixed Level RSDs (or Asymmetric RSDs).  In many practical situations,
RSDs with asymmetric levels will be more suitable as it explores more regions in
the design space. (J.S. Mehta and M.N. Das (1968) <doi:10.2307/1267046>.
\"Asymmetric rotatable designs and orthogonal transformations\").This package
contains function named ATORDs_I() for generating asymmetric third order
rotatable designs (ATORDs) based on third order designs given by Das and
Narasimham (1962).  Function ATORDs_II() generates asymmetric third order
rotatable designs developed using t-design of unequal set sizes, which are
smaller in size as compared to design generated by function ATORDs_I().  In
general, third order rotatable designs can be classified into two classes viz.,
designs that are suitable for sequential experimentation and designs for
non-sequential experimentation.  The sequential experimentation approach
involves conducting the trials step by step whereas, in the non-sequential
experimentation approach, the entire runs are executed in one go (M. N. Das and
V. Narasimham (1962) <doi:10.1214/AOMS/1177704374>. \"Construction of Rotatable
Designs through Balanced Incomplete Block Designs\").  ATORDs_I() and ATORDs_II()
functions generate non-sequential asymmetric third order designs.  Function
named @code{SeqTORD}() generates symmetric sequential third order design in
blocks and also gives G-efficiency of the given design.  Function named
Asymseq() generates asymmetric sequential third order designs in blocks (M.
Hemavathi, Eldho Varghese, Shashi Shekhar and Seema Jaggi (2020)
<doi:10.1080/02664763.2020.1864817>. \"Sequential asymmetric third order
rotatable designs (SATORDs)\").  In response surface design, situations may arise
in which some of the factors are qualitative in nature (Jyoti Divecha and Bharat
Tarapara (2017) <doi:10.1080/08982112.2016.1217338>. \"Small, balanced,
efficient, optimal, and near rotatable response surface designs for factorial
experiments asymmetrical in some quantitative, qualitative factors\").  The
Function named @code{QualRSD}() generates second order design with qualitative
factors along with their D-efficiency and G-efficiency.  The function named
@code{RotatabilityQ}() calculates a measure of rotatability (measure Q, 0 <= Q
<= 1) given by Draper and Pukelshiem(1990) for given a design based on a second
order model, (Norman R. Draper and Friedrich Pukelsheim(1990)
<doi:10.1080/00401706.1990.10484635>. \"Another look at rotatability\").")
    (license license:gpl2+)))

(define-public r-mixedindtests
  (package
    (name "r-mixedindtests")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixedIndTests" version))
       (sha256
        (base32 "0gbjjfj9h4zx5hxzdrbphpk0flzwrg7jb3xxjbvyfd3n038zxqxz"))))
    (properties `((upstream-name . "MixedIndTests")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-ggplot2 r-foreach r-doparallel
                             r-copula))
    (home-page "https://cran.r-project.org/package=MixedIndTests")
    (synopsis "Tests of Randomness and Tests of Independence")
    (description
     "This package provides functions for testing randomness for a univariate time
series with arbitrary distribution (discrete, continuous, mixture of both types)
and for testing independence between random variables with arbitrary
distributions.  The test statistics are based on the multilinear empirical
copula and multipliers are used to compute P-values.  The test of independence
between random variables appeared in Genest, NeÅ¡lehovÃ¡, RÃ©millard & Murphy
(2019) and the test of randomness appeared in Nasri (2022).")
    (license license:gpl3)))

(define-public r-mixedcca
  (package
    (name "r-mixedcca")
    (version "1.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixedCCA" version))
       (sha256
        (base32 "07apl37kwj47w14jxsvwdiacnwd517zs3ff2c70fhb6ncbgp8adv"))))
    (properties `((upstream-name . "mixedCCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-pcapp
                             r-mnormt
                             r-matrix
                             r-mass
                             r-latentcor
                             r-irlba
                             r-fmultivar))
    (home-page "https://cran.r-project.org/package=mixedCCA")
    (synopsis
     "Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data")
    (description
     "Semi-parametric approach for sparse canonical correlation analysis which can
handle mixed data types: continuous, binary and truncated continuous.  Bridge
functions are provided to connect Kendall's tau to latent correlation under the
Gaussian copula model.  The methods are described in Yoon, Carroll and Gaynanova
(2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021)
<doi:10.1080/10618600.2021.1882468>.")
    (license license:gpl3)))

(define-public r-mixedbayes
  (package
    (name "r-mixedbayes")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixedBayes" version))
       (sha256
        (base32 "1jcbkbai1sm0nncixkmfbzphf1x2gnwi52bri31bay9d591p4w9h"))))
    (properties `((upstream-name . "mixedBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/kunfa/mixedBayes")
    (synopsis "Bayesian Longitudinal Regularized Quantile Mixed Model")
    (description
     "In longitudinal studies, the same subjects are measured repeatedly over time,
leading to correlations among the repeated measurements.  Properly accounting
for the intra-cluster correlations in the presence of data heterogeneity and
long tailed distributions of the disease phenotype is challenging, especially in
the context of high dimensional regressions.  Here, we aim at developing novel
Bayesian regularized quantile mixed effect models to tackle these challenges.
We have proposed a Bayesian variable selection in the mixed effect models for
longitudinal genomics studies.  To dissect important gene - environment
interactions, our model can simultaneously identify important main and
interaction effects on the individual and group level, which have been
facilitated by imposing the spike- and -slab priors through Laplacian shrinkage
in the Bayesian quantile hierarchical models.  The within - subject dependence
among data can be accommodated by incorporating the random effects.  An
efficient Gibbs sampler has been developed to facilitate fast computation.  The
Markov chain Monte Carlo algorithms of the proposed and alternative methods are
efficiently implemented in C++'.  The development of this software package and
the associated statistical methods have been partially supported by an
Innovative Research Award from Johnson Cancer Research Center, Kansas State
University.")
    (license license:gpl2)))

(define-public r-mixdist
  (package
    (name "r-mixdist")
    (version "0.5-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixdist" version))
       (sha256
        (base32 "1wchhysd33xz395hq4wgd3zv1qc92pbiqxqd7g2v6m1pb0j8ma9g"))))
    (properties `((upstream-name . "mixdist")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org/")
    (synopsis "Finite Mixture Distribution Models")
    (description
     "Fit finite mixture distribution models to grouped data and conditional data by
maximum likelihood using a combination of a Newton-type algorithm and the EM
algorithm.")
    (license license:gpl2+)))

(define-public r-mixdir
  (package
    (name "r-mixdir")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixdir" version))
       (sha256
        (base32 "1vgxxzsrl660agk4cjw4q4gx28fv5mkzpjy4qanq5c9mqpcxbxkc"))))
    (properties `((upstream-name . "mixdir")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-extradistr))
    (home-page "https://github.com/const-ae/mixdir")
    (synopsis "Cluster High Dimensional Categorical Datasets")
    (description
     "Scalable Bayesian clustering of categorical datasets.  The package implements a
hierarchical Dirichlet (Process) mixture of multinomial distributions.  It is
thus a probabilistic latent class model (LCM) and can be used to reduce the
dimensionality of hierarchical data and cluster individuals into latent classes.
 It can automatically infer an appropriate number of latent classes or find k
classes, as defined by the user.  The model is based on a paper by Dunson and
Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable
variational inference algorithm so that it is applicable to large datasets.  It
is described and tested in the accompanying paper by Ahlmann-Eltze and Yau
(2018) <doi:10.1109/DSAA.2018.00068>.")
    (license license:gpl3)))

(define-public r-mixcure
  (package
    (name "r-mixcure")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixcure" version))
       (sha256
        (base32 "0jjbamn53iqrrx658wk3cz43grsbn1275p81256h4lia35s7cww3"))))
    (properties `((upstream-name . "mixcure")))
    (build-system r-build-system)
    (propagated-inputs (list r-timereg
                             r-survival
                             r-survey
                             r-gam
                             r-flexsurv
                             r-boot))
    (home-page "https://cran.r-project.org/package=mixcure")
    (synopsis "Mixture Cure Models")
    (description
     "Implementation of parametric and semiparametric mixture cure models based on
existing R packages.  See details of the models in Peng and Yu (2020) <ISBN:
9780367145576>.")
    (license license:gpl3)))

(define-public r-mixchar
  (package
    (name "r-mixchar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixchar" version))
       (sha256
        (base32 "1az2gggwipkzg86pvn9w2csgfgrpdc88kca51f5qcw0zk4wx5hlx"))))
    (properties `((upstream-name . "mixchar")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-tmvtnorm r-nloptr r-minpack-lm))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/smwindecker/mixchar")
    (synopsis "Mixture Model for the Deconvolution of Thermal Decay Curves")
    (description
     "Deconvolution of thermal decay curves allows you to quantify proportions of
biomass components in plant litter.  Thermal decay curves derived from
thermogravimetric analysis (TGA) are imported, modified, and then modelled in a
three- or four- part mixture model using the Fraser-Suzuki function.  The output
is estimates for weights of pseudo-components corresponding to hemicellulose,
cellulose, and lignin.  For more information see: MÃ¼ller-Hagedorn, M. and
Bockhorn, H. (2007) <doi:10.1016/j.jaap.2006.12.008>, ÃrfÃ£o, J. J. M. and
Figueiredo, J. L. (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and
Yan, R. and Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006)
<doi:10.1021/ef0580117>.")
    (license license:expat)))

(define-public r-mixcat
  (package
    (name "r-mixcat")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixcat" version))
       (sha256
        (base32 "19v6y981js18xz9q49slclkhcf6w53iar55ynjs0lvhn1y1b2517"))))
    (properties `((upstream-name . "mixcat")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod))
    (home-page "https://cran.r-project.org/package=mixcat")
    (synopsis "Mixed Effects Cumulative Link and Logistic Regression Models")
    (description
     "Mixed effects cumulative and baseline logit link models for the analysis of
ordinal or nominal responses, with non-parametric distribution for the random
effects.")
    (license license:gpl2+)))

(define-public r-mixbox
  (package
    (name "r-mixbox")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixbox" version))
       (sha256
        (base32 "1mns85bp9qm8xvkay672qwwjjvrav682afnf5c1aa6qzpsradx08"))))
    (properties `((upstream-name . "mixbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-stabledist r-gigrvg))
    (home-page "https://cran.r-project.org/package=mixbox")
    (synopsis "Observed Fisher Information Matrix for Finite Mixture Model")
    (description
     "Developed for the following tasks.  1- simulating realizations from the
canonical, restricted, and unrestricted finite mixture models.  2- Monte Carlo
approximation for density function of the finite mixture models.  3- Monte Carlo
approximation for the observed Fisher information matrix, asymptotic standard
error, and the corresponding confidence intervals for parameters of the mixture
models sing the method proposed by Basford et al. (1997)
<https://espace.library.uq.edu.au/view/UQ:57525>.")
    (license license:gpl2+)))

(define-public r-mixar
  (package
    (name "r-mixar")
    (version "0.22.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixAR" version))
       (sha256
        (base32 "1fr3i54yvxqvh78pgn8y5hdwp1sf00gl9shg5bc7nnmmm84ki78q"))))
    (properties `((upstream-name . "mixAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate
                             r-rdpack
                             r-permute
                             r-mvtnorm
                             r-mcmcpack
                             r-gbutils
                             r-fgarch
                             r-e1071
                             r-combinat
                             r-bb))
    (home-page "https://geobosh.github.io/mixAR/")
    (synopsis "Mixture Autoregressive Models")
    (description
     "Model time series using mixture autoregressive (MAR) models.  Implemented are
frequentist (EM) and Bayesian methods for estimation, prediction and model
evaluation.  See Wong and Li (2002) <doi:10.1111/1467-9868.00222>, Boshnakov
(2009) <doi:10.1016/j.spl.2009.04.009>), and the extensive references in the
documentation.")
    (license license:gpl2+)))

(define-public r-mixall
  (package
    (name "r-mixall")
    (version "1.5.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MixAll" version))
       (sha256
        (base32 "134nf62cc2l998dl6vhp4nlyjfbybw9l9rycnxys4i0wzcsl8lz5"))))
    (properties `((upstream-name . "MixAll")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtkore r-rcpp))
    (home-page "https://cran.r-project.org/package=MixAll")
    (synopsis "Clustering and Classification using Model-Based Mixture Models")
    (description
     "Algorithms and methods for model-based clustering and classification.  It
supports various types of data: continuous, categorical and counting and can
handle mixed data of these types.  It can fit Gaussian (with diagonal covariance
structure), gamma, categorical and Poisson models.  The algorithms also support
missing values.")
    (license license:gpl2+)))

(define-public r-mixak
  (package
    (name "r-mixak")
    (version "5.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mixAK" version))
       (sha256
        (base32 "16prqmlhy462glp3n73f0fvcqajnqf89jibicsdjzg08g9gqb08w"))))
    (properties `((upstream-name . "mixAK")))
    (build-system r-build-system)
    (propagated-inputs (list r-mnormt r-lme4 r-fastghquad r-colorspace r-coda))
    (home-page "https://msekce.karlin.mff.cuni.cz/~komarek/")
    (synopsis
     "Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering")
    (description
     "This package contains a mixture of statistical methods including the MCMC
methods to analyze normal mixtures.  Additionally, model based clustering
methods are implemented to perform classification based on (multivariate)
longitudinal (or otherwise correlated) data.  The basis for such clustering is a
mixture of multivariate generalized linear mixed models.  The package is
primarily related to the publications KomÃ¡rek (2009, Comp.  Stat.  and Data
Anal.) <doi:10.1016/j.csda.2009.05.006> and KomÃ¡rek and KomÃ¡rkovÃ¡ (2014, J.
of Stat.  Soft.) <doi:10.18637/jss.v059.i12>.  It also implements methods
published in KomÃ¡rek and KomÃ¡rkovÃ¡ (2013, Ann.  of Appl.  Stat.)
<doi:10.1214/12-AOAS580>, Hughes, KomÃ¡rek, Bonnett, Czanner, GarcÃ­a-FiÃ±ana
(2017, Stat.  in Med.) <doi:10.1002/sim.7397>, Jaspers, KomÃ¡rek, Aerts (2018,
Biom.  J.) <doi:10.1002/bimj.201600253> and Hughes, KomÃ¡rek, Czanner,
GarcÃ­a-FiÃ±ana (2018, Stat.  Meth.  in Med.  Res)
<doi:10.1177/0962280216674496>.")
    (license license:gpl3+)))

(define-public r-mix
  (package
    (name "r-mix")
    (version "1.0-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mix" version))
       (sha256
        (base32 "05a3mklaawkch7spcin1rfzx556w315bklwipd64kzhfxj5nds1x"))))
    (properties `((upstream-name . "mix")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=mix")
    (synopsis
     "Estimation/Multiple Imputation for Mixed Categorical and Continuous Data")
    (description
     "Estimation/multiple imputation programs for mixed categorical and continuous
data.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-miwqs
  (package
    (name "r-miwqs")
    (version "0.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miWQS" version))
       (sha256
        (base32 "0fmcc86hiz89kdynkzppp9z0srgh4gl8327szivjxadxip4zm21i"))))
    (properties `((upstream-name . "miWQS")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-tmvtnorm
                             r-tmvmixnorm
                             r-tidyr
                             r-survival
                             r-rsolnp
                             r-rlist
                             r-purrr
                             r-mvtnorm
                             r-mcmcpack
                             r-matrixnormal
                             r-mass
                             r-invgamma
                             r-hmisc
                             r-glm2
                             r-ggplot2
                             r-condmvnorm
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=miWQS")
    (synopsis "Multiple Imputation Using Weighted Quantile Sum Regression")
    (description
     "The @code{miWQS} package handles the uncertainty due to below the detection
limit in a correlated component mixture problem.  Researchers want to determine
if a set/mixture of continuous and correlated components/chemicals is associated
with an outcome and if so, which components are important in that mixture.
These components share a common outcome but are interval-censored between zero
and low thresholds, or detection limits, that may be different across the
components.  This package applies the multiple imputation (MI) procedure to the
weighted quantile sum regression (WQS) methodology for continuous, binary, or
count outcomes (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>).
The imputation models are: bootstrapping imputation (Lubin et.al (2004)
<doi:10.1289/ehp.7199>), univariate Bayesian imputation (Hargarten & Wheeler
(2020) <doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian regression
imputation.")
    (license license:gpl3)))

(define-public r-mittagleffler
  (package
    (name "r-mittagleffler")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MittagLeffleR" version))
       (sha256
        (base32 "1qlz418im27v7q3xk09a2w3zdx7ky5imcyi3kpg1s3bz5zx57p2f"))))
    (properties `((upstream-name . "MittagLeffleR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stabledist))
    (native-inputs (list r-knitr))
    (home-page "https://strakaps.github.io/MittagLeffleR/")
    (synopsis "Mittag-Leffler Family of Distributions")
    (description
     "This package implements the Mittag-Leffler function, distribution, random
variate generation, and estimation.  Based on the Laplace-Inversion algorithm by
Garrappa, R. (2015) <doi:10.1137/140971191>.")
    (license license:gpl2+)))

(define-public r-mitre
  (package
    (name "r-mitre")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mitre" version))
       (sha256
        (base32 "0r5phzxiibwwhx3qh623grfy070c3vzm4q07rzhsp5q5ycb5jhn5"))))
    (properties `((upstream-name . "mitre")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-rjsonio
                             r-plyr
                             r-jsonlite
                             r-igraph
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/motherhack3r/mitre")
    (synopsis "Cybersecurity MITRE Standards Data and Digraphs")
    (description
     "Extract, transform and load MITRE standards.  This package gives you an approach
to cybersecurity data sets.  All data sets are build on runtime downloading raw
data from MITRE public services.  MITRE <https://www.mitre.org/> is a
government-funded research organization based in Bedford and @code{McLean}.
Current version includes most used standards as data frames.  It also provide a
list of nodes and edges with all relationships.")
    (license license:cc0)))

(define-public r-mitohear
  (package
    (name "r-mitohear")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MitoHEAR" version))
       (sha256
        (base32 "1lb36w374wjp6f9ydkas21rwc2shlav1xq2rfzig1k034fkva8bj"))))
    (properties `((upstream-name . "MitoHEAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsamtools
                             r-rlist
                             r-reshape2
                             r-rdist
                             r-mcclust
                             r-magrittr
                             r-iranges
                             r-gridextra
                             r-ggplot2
                             r-genomicranges
                             r-dynamictreecut
                             r-complexheatmap
                             r-circlize
                             r-biostrings))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MitoHEAR")
    (synopsis "Quantification of Mitochondrial DNA Heteroplasmy")
    (description
     "Allows the estimation and downstream statistical analysis of the mitochondrial
DNA Heteroplasmy calculated from single-cell datasets
<https://github.com/@code{ScialdoneLab/MitoHEAR/tree/master>}.")
    (license license:artistic2.0)))

(define-public r-misuvi
  (package
    (name "r-misuvi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "misuvi" version))
       (sha256
        (base32 "0l62hm1d9bjxn53k50aq71i9f8fgj8v55m75l3f9qc88wr8fhqf2"))))
    (properties `((upstream-name . "misuvi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tigris r-sf r-curl))
    (home-page "https://github.com/brendensm/misuvi")
    (synopsis
     "Access the Michigan Substance Use Vulnerability Index (MI-SUVI)")
    (description
     "Easily import the MI-SUVI data sets.  The user can import data sets with full
metrics, percentiles, Z-scores, or rankings.  Data is available at both the
County and Zip Code Tabulation Area (ZCTA) levels.  This package also includes a
function to import shape files for easy mapping and a function to access the
full technical documentation.  All data is sourced from
(<https://www.michigan.gov/opioids/category-data>).")
    (license license:cc0)))

(define-public r-misty
  (package
    (name "r-misty")
    (version "0.6.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "misty" version))
       (sha256
        (base32 "06b169407x067fsypjxcjqvmadmb8nx6f9ppvjlxrybr8js6i1z9"))))
    (properties `((upstream-name . "misty")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-rstudioapi
                             r-readxl
                             r-lme4
                             r-lavaan
                             r-haven
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=misty")
    (synopsis "Miscellaneous Functions 'T. Yanagida'")
    (description
     "Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean
centering, coding variables and reverse coding items, scale and cluster scores,
reading and writing Excel and SPSS files), (2) descriptive statistics (e.g.,
frequency table, cross tabulation, effect size measures), (3) missing data
(e.g., descriptive statistics for missing data, missing data pattern, Little's
test of Missing Completely at Random, and auxiliary variable analysis), (4)
multilevel data (e.g., multilevel descriptive statistics, within-group and
between-group correlation matrix, multilevel confirmatory factor analysis,
level-specific fit indices, cross-level measurement equivalence evaluation,
multilevel composite reliability, and multilevel R-squared measures), (5) item
analysis (e.g., confirmatory factor analysis, coefficient alpha and omega,
between-group and longitudinal measurement equivalence evaluation), (6)
statistical analysis (e.g., confidence intervals, collinearity and residual
diagnostics, dominance analysis, between- and within-subject analysis of
variance, latent class analysis, t-test, z-test, sample size determination), and
(7) functions to interact with Blimp and Mplus'.")
    (license license:expat)))

(define-public r-mistral
  (package
    (name "r-mistral")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mistral" version))
       (sha256
        (base32 "1ssgglw2y1bkhlwcrmw8bv8ic6vl5nj4l482hlq8kv1dgyrnbsas"))))
    (properties `((upstream-name . "mistral")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-quadprog
                             r-mvtnorm
                             r-matrix
                             r-iterators
                             r-ggplot2
                             r-foreach
                             r-e1071
                             r-doparallel
                             r-dicekriging))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=mistral")
    (synopsis "Methods in Structural Reliability")
    (description
     "Various reliability analysis methods for rare event inference (computing failure
probability and quantile from model/function outputs).")
    (license license:gpl2)))

(define-public r-mistr
  (package
    (name "r-mistr")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mistr" version))
       (sha256
        (base32 "103gvzcyrxscpsd8kv6zj8zx3r9mlbdjwd56msy0ngzcgrb4sb9d"))))
    (properties `((upstream-name . "mistr")))
    (build-system r-build-system)
    (propagated-inputs (list r-bbmle))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mistr")
    (synopsis "Mixture and Composite Distributions")
    (description
     "This package provides a flexible computational framework for mixture
distributions with the focus on the composite models.")
    (license license:gpl3)))

(define-public r-misssom
  (package
    (name "r-misssom")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missSOM" version))
       (sha256
        (base32 "15cqqz6f2npsz78yhj3ialzmlx7a8jsf27gfmmj4h68r01czyhs1"))))
    (properties `((upstream-name . "missSOM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-kpodclustr))
    (home-page "https://cran.r-project.org/package=missSOM")
    (synopsis "Self-Organizing Maps with Built-in Missing Data Imputation")
    (description
     "The Self-Organizing Maps with Built-in Missing Data Imputation.  Missing values
are imputed and regularly updated during the online Kohonen algorithm.  Our
method can be used for data visualisation, clustering or imputation of missing
data.  It is an extension of the online algorithm of the kohonen package.  The
method is described in the article \"Self-Organizing Maps for Exploration of
Partially Observed Data and Imputation of Missing Values\" by S. Rejeb, C.
Duveau, T. Rebafka (2022) <@code{arXiv:2202.07963>}.")
    (license license:gpl2+)))

(define-public r-misssbm
  (package
    (name "r-misssbm")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missSBM" version))
       (sha256
        (base32 "1gw485bn7mddj5i555clp3saxas77r9r4970ahf1y3vpk43wjj02"))))
    (properties `((upstream-name . "missSBM")))
    (build-system r-build-system)
    (propagated-inputs (list r-sbm
                             r-rspectra
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-nloptr
                             r-matrix
                             r-magrittr
                             r-igraph
                             r-ggplot2
                             r-future-apply))
    (native-inputs (list r-knitr))
    (home-page "https://grosssbm.github.io/missSBM/")
    (synopsis "Handling Missing Data in Stochastic Block Models")
    (description
     "When a network is partially observed (here, NAs in the adjacency matrix rather
than 1 or 0 due to missing information between node pairs), it is possible to
account for the underlying process that generates those NAs. @code{missSBM}',
presented in Barbillon, Chiquet and Tabouy (2022) <doi:10.18637/jss.v101.i12>,
adjusts the popular stochastic block model from network data sampled under
various missing data conditions, as described in Tabouy, Barbillon and Chiquet
(2019) <doi:10.1080/01621459.2018.1562934>.")
    (license license:gpl3)))

(define-public r-missranger
  (package
    (name "r-missranger")
    (version "2.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missRanger" version))
       (sha256
        (base32 "1qwjimdzb88pmqscpkl35y2lawrybps5wn3dnrpkh2zj2zxjf1md"))))
    (properties `((upstream-name . "missRanger")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger r-fnn))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mayer79/missRanger")
    (synopsis "Fast Imputation of Missing Values")
    (description
     "Alternative implementation of the beautiful @code{MissForest} algorithm used to
impute mixed-type data sets by chaining random forests, introduced by Stekhoven,
D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>.  Under the
hood, it uses the lightning fast random forest package ranger'.  Between the
iterative model fitting, we offer the option of using predictive mean matching.
This firstly avoids imputation with values not already present in the original
data (like a value 0.3334 in 0-1 coded variable).  Secondly, predictive mean
matching tries to raise the variance in the resulting conditional distributions
to a realistic level.  This would allow, e.g., to do multiple imputation when
repeating the call to @code{missRanger}().  Out-of-sample application is
supported as well.")
    (license license:gpl2+)))

(define-public r-missplot
  (package
    (name "r-missplot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Missplot" version))
       (sha256
        (base32 "00qpmkmfv3k13faxb2wqcdcmd5q5abdhjwr8rrf9b0bv6qqnlm84"))))
    (properties `((upstream-name . "Missplot")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Missplot")
    (synopsis "Missing Plot Technique in Design of Experiment")
    (description
     "This package provides a system for testing differential effects among treatments
in case of Randomised Block Design and Latin Square Design when there is one
missing observation.  Methods for this process are as described in
A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).")
    (license license:gpl3)))

(define-public r-misspi
  (package
    (name "r-misspi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "misspi" version))
       (sha256
        (base32 "0rbkwfq3b7gvq3qwn3acbrdv4npvm5ixz6y7877alraqd0qjg1c8"))))
    (properties `((upstream-name . "misspi")))
    (build-system r-build-system)
    (propagated-inputs (list r-sis
                             r-plotly
                             r-lightgbm
                             r-glmnet
                             r-ggplot2
                             r-foreach
                             r-dosnow
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=misspi")
    (synopsis "Missing Value Imputation in Parallel")
    (description
     "This package provides a framework that boosts the imputation of
@code{missForest} by Stekhoven, D.J. and BÃ¼hlmann, P. (2012)
<doi:10.1093/bioinformatics/btr597> by harnessing parallel processing and
through the fast Gradient Boosted Decision Trees (GBDT) implementation
@code{LightGBM} by Ke, Guolin et al.(2017)
<https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.
 misspi has the following main advantages: 1.  Allows embrassingly parallel
imputation on large scale data.  2.  Accepts a variety of machine learning
models as methods with friendly user portal.  3.  Supports multiple
initializations methods.  4.  Supports early stopping that prohibits unnecessary
iterations.")
    (license license:gpl2)))

(define-public r-missonet
  (package
    (name "r-missonet")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missoNet" version))
       (sha256
        (base32 "1k7959wjxxpzgrnwil7n51zf76zyri8c2m4m5h2w9az5pil2qmf0"))))
    (properties `((upstream-name . "missoNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-scatterplot3d
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-mvtnorm
                             r-glasso
                             r-complexheatmap
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yixiao-zeng/missoNet")
    (synopsis "Missingness in Multi-Task Regression with Network Estimation")
    (description
     "Efficient procedures for fitting conditional graphical lasso models that link a
set of predictor variables to a set of response variables (or tasks), even when
the response data may contain missing values. @code{missoNet} simultaneously
estimates the predictor coefficients for all tasks by leveraging information
from one another, in order to provide more accurate predictions in comparison to
modeling them individually.  Additionally, @code{missoNet} estimates the
response network structure influenced by conditioning predictor variables using
a L1-regularized conditional Gaussian graphical model.  Unlike most penalized
multi-task regression methods (e.g., MRCE), @code{missoNet} is capable of
obtaining estimates even when the response data is corrupted by missing values.
The method automatically enjoys the theoretical and computational benefits of
convexity, and returns solutions that are comparable to the estimates obtained
without missingness.")
    (license license:gpl2)))

(define-public r-missmethods
  (package
    (name "r-missmethods")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missMethods" version))
       (sha256
        (base32 "1vsgc315px3x44rqlsj6r6lyh9flqjibgpvbx0crd8b644ab6qh2"))))
    (properties `((upstream-name . "missMethods")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/torockel/missMethods")
    (synopsis "Methods for Missing Data")
    (description
     "Supply functions for the creation and handling of missing data as well as tools
to evaluate missing data methods.  Nearly all possibilities of generating
missing data discussed by Santos et al. (2019) <doi:10.1109/ACCESS.2019.2891360>
and some additional are implemented.  Functions are supplied to compare
parameter estimates and imputed values to true values to evaluate missing data
methods.  Evaluations of these types are done, for example, by Cetin-Berber et
al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
<doi:10.1093/bioinformatics/bth499>.")
    (license license:gpl3)))

(define-public r-missmech
  (package
    (name "r-missmech")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MissMech" version))
       (sha256
        (base32 "0r4jdy2wr4rqpyshlgfvfhvq78v0w6g7v7iixljgh4kv9ryl476d"))))
    (properties `((upstream-name . "MissMech")))
    (build-system r-build-system)
    (home-page "https://github.com/indenkun/MissMech")
    (synopsis
     "Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random")
    (description
     "To test whether the missing data mechanism, in a set of incompletely observed
data, is one of missing completely at random (MCAR).  For detailed description
see Jamshidian, M. Jalal, S., and Jansen, C. (2014). \"@code{MissMech}: An R
Package for Testing Homoscedasticity, Multivariate Normality, and Missing
Completely at Random (MCAR)\", Journal of Statistical Software, 56(6), 1-31.
<https://www.jstatsoft.org/v56/i06/> <doi:10.18637/jss.v056.i06>.")
    (license license:gpl2+)))

(define-public r-missmda
  (package
    (name "r-missmda")
    (version "1.19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missMDA" version))
       (sha256
        (base32 "0p76jlzqayhwqwinaxhf69s3c5hxk4hmncvw4dsyybwvha25hrzr"))))
    (properties `((upstream-name . "missMDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm
                             r-mice
                             r-ggplot2
                             r-foreach
                             r-factominer
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "http://factominer.free.fr/missMDA/index.html")
    (synopsis "Handling Missing Values with Multivariate Data Analysis")
    (description
     "Imputation of incomplete continuous or categorical datasets; Missing values are
imputed with a principal component analysis (PCA), a multiple correspondence
analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple
imputation with and in PCA or MCA.")
    (license license:gpl2+)))

(define-public r-missingplotrbd
  (package
    (name "r-missingplotrbd")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MissingPlotRBD" version))
       (sha256
        (base32 "1w7i9zcdw7k0hb89lm7022pq5623lkxwa8j0jw2imvikhmj2mf79"))))
    (properties `((upstream-name . "MissingPlotRBD")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MissingPlotRBD")
    (synopsis "Missing Plot in RBD")
    (description
     "This package provides a system for Analysis of RBD when there is one missing
observation.  Methods for this process is described in
A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).")
    (license license:gpl3)))

(define-public r-missingplotlsd
  (package
    (name "r-missingplotlsd")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MissingPlotLSD" version))
       (sha256
        (base32 "07y9ri6k3g850jd6hmqm3q82791sl9sbw1xmz5bhi7wrfgvyhji0"))))
    (properties `((upstream-name . "MissingPlotLSD")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MissingPlotLSD")
    (synopsis "Missing Plot in LSD")
    (description
     "This package provides a system for Analysis of LSD when there is one missing
observation.  Methods for this process is described in
A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).")
    (license license:gpl3)))

(define-public r-missinghe
  (package
    (name "r-missinghe")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missingHE" version))
       (sha256
        (base32 "0my6a768w7lixh6cw453bl2nrb5aqdiri309p5kp4psp17pzllak"))))
    (properties `((upstream-name . "missingHE")))
    (build-system r-build-system)
    (propagated-inputs (list r-r2jags
                             r-mcmcr
                             r-mcmcplots
                             r-loo
                             r-gridextra
                             r-ggthemes
                             r-ggpubr
                             r-ggplot2
                             r-ggmcmc
                             r-coda
                             r-bcea
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=missingHE")
    (synopsis "Missing Outcome Data in Health Economic Evaluation")
    (description
     "This package contains a suite of functions for health economic evaluations with
missing outcome data.  The package can fit different types of statistical models
under a fully Bayesian approach using the software JAGS (which should be
installed locally and which is loaded in @code{missingHE} via the R package
R2jags').  Three classes of models can be fitted under a variety of missing data
assumptions: selection models, pattern mixture models and hurdle models.  In
addition to model fitting, @code{missingHE} provides a set of specialised
functions to assess model convergence and fit, and to summarise the statistical
and economic results using different types of measures and graphs.  The methods
implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs
(2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019)
<doi:10.1002/sim.8045>.")
    (license license:gpl2)))

(define-public r-missinghandle
  (package
    (name "r-missinghandle")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MissingHandle" version))
       (sha256
        (base32 "09mfjyygxmcpd50148a5pcda0c7gvfrxgxbw1v6i01ymwpfb4b2p"))))
    (properties `((upstream-name . "MissingHandle")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-imputets r-dplyr))
    (home-page "https://cran.r-project.org/package=MissingHandle")
    (synopsis
     "Handles Missing Dates and Data and Converts into Weekly and Monthly from Daily")
    (description
     "Many times, you will not find data for all dates.  After first January, 2011 you
may have next data on 20th January, 2011 and so on.  Also available dates may
have zero values.  Try to gather all such kinds of data in different excel
sheets of a single excel file.  Every sheet will contain two columns (1st one is
dates and second one is the data).  After loading all the sheets into different
elements of a list, using this you can fill the gaps for all the sheets and mark
all the corresponding values as zeros.  Here I am talking about daily data.
Finally, it will combine all the filled results into one data frame (first
column is date and other columns will be corresponding values of your sheets)
and give one combined data frame.  Number of columns in the data frame will be
number of sheets plus one.  Then imputation will be done.  Daily to monthly and
weekly conversion is also possible.  More details can be found in Garai and
others (2023) <doi:10.13140/RG.2.2.11977.42087>.")
    (license license:gpl3)))

(define-public r-missforestpredict
  (package
    (name "r-missforestpredict")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missForestPredict" version))
       (sha256
        (base32 "0p9bmphv77f6xxdvnz4zzalbbv8ch3bdyqqy33r78iimg5rnmi5x"))))
    (properties `((upstream-name . "missForestPredict")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sibipx/missForestPredict")
    (synopsis
     "Missing Value Imputation using Random Forest for Prediction Settings")
    (description
     "Missing data imputation based on the @code{missForest} algorithm (Stekhoven,
Daniel J (2012) <doi:10.1093/bioinformatics/btr597>) with adaptations for
prediction settings.  The function @code{missForest}() is used to impute a
(training) dataset with missing values and to learn imputation models that can
be later used for imputing new observations.  The function
@code{missForestPredict}() is used to impute one or multiple new observations
(test set) using the models learned on the training data.")
    (license license:gpl2+)))

(define-public r-missdiag
  (package
    (name "r-missdiag")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missDiag" version))
       (sha256
        (base32 "04zd3nds44sy7nz73kdq9wjis3r9zyp0kda54mfqps1sh9jnizlq"))))
    (properties `((upstream-name . "missDiag")))
    (build-system r-build-system)
    (propagated-inputs (list r-formula r-cobalt))
    (home-page "https://github.com/sumtxt/missDiag/")
    (synopsis "Comparing Observed and Imputed Values under MAR and MCAR")
    (description
     "This package implements the computation of discrepancy statistics summarizing
differences between the density of imputed and observed values and the
construction of weights to balance covariates that are part of the missing data
mechanism as described in Marbach (2021) <@code{arXiv:2107.05427>}.")
    (license license:gpl3)))

(define-public r-missdeaths
  (package
    (name "r-missdeaths")
    (version "2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missDeaths" version))
       (sha256
        (base32 "1qwrzb5nhbigyg7ivhsh3yqhrp0qaq3abgfffy808fry5rkz6sj6"))))
    (properties `((upstream-name . "missDeaths")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rms
                             r-relsurv
                             r-rcpp
                             r-mitools
                             r-mass
                             r-cmprsk))
    (home-page "https://cran.r-project.org/package=missDeaths")
    (synopsis
     "Simulating and Analyzing Time to Event Data in the Presence of Population Mortality")
    (description
     "This package implements two methods: a nonparametric risk adjustment and a data
imputation method that use general population mortality tables to allow a
correct analysis of time to disease recurrence.  Also includes a powerful set of
object oriented survival data simulation functions.")
    (license license:gpl2+)))

(define-public r-misscp
  (package
    (name "r-misscp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MissCP" version))
       (sha256
        (base32 "0xmfkxwlkci11wwxdazs25k6z8apcl2vffkbpv8zic5lx1r9g2k4"))))
    (properties `((upstream-name . "MissCP")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsevar
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-glmnet
                             r-ggplot2
                             r-factoextra))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MissCP")
    (synopsis "Change Point Detection with Missing Values")
    (description
     "This package provides a four step change point detection method that can detect
break points with the presence of missing values proposed by Liu and Safikhani
(2023)
<https://drive.google.com/file/d/1a8@code{sV3RJ8VofLWikTDTQ7W4XJ76cEj4Fg/view?usp=drive_link>}.")
    (license license:gpl2)))

(define-public r-misscompare
  (package
    (name "r-misscompare")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missCompare" version))
       (sha256
        (base32 "03j69xw54d076znx19dplw4xznvgky90hxh3wgypg0g5pjg63y53"))))
    (properties `((upstream-name . "missCompare")))
    (build-system r-build-system)
    (propagated-inputs (list r-vim
                             r-tidyr
                             r-rlang
                             r-plyr
                             r-pcamethods
                             r-missmda
                             r-missforest
                             r-mice
                             r-mi
                             r-matrix
                             r-mass
                             r-magrittr
                             r-ltm
                             r-hmisc
                             r-ggplot2
                             r-ggdendro
                             r-dplyr
                             r-data-table
                             r-amelia))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=missCompare")
    (synopsis "Intuitive Missing Data Imputation Framework")
    (description
     "Offers a convenient pipeline to test and compare various missing data imputation
algorithms on simulated and real data.  These include simpler methods, such as
mean and median imputation and random replacement, but also include more
sophisticated algorithms already implemented in popular R packages, such as mi',
described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; mice', described by
van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>;
@code{missForest}', described by Stekhoven and Buhlmann (2012)
<doi:10.1093/bioinformatics/btr597>; @code{missMDA}', described by Josse and
Husson (2016) <doi:10.18637/jss.v070.i01>; and @code{pcaMethods}', described by
Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>.  The central
assumption behind @code{missCompare} is that structurally different datasets
(e.g. larger datasets with a large number of correlated variables vs. smaller
datasets with non correlated variables) will benefit differently from different
missing data imputation algorithms. @code{missCompare} takes measurements of
your dataset and sets up a sandbox to try a curated list of standard and
sophisticated missing data imputation algorithms and compares them assuming
custom missingness patterns. @code{missCompare} will also impute your real-life
dataset for you after the selection of the best performing algorithm in the
simulations.  The package also provides various post-imputation diagnostics and
visualizations to help you assess imputation performance.")
    (license license:expat)))

(define-public r-misscforest
  (package
    (name "r-misscforest")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "missCforest" version))
       (sha256
        (base32 "1xdff1iddvagsw5b27wl0hbv21baj8cnkg17imfg9fkh1fik2s00"))))
    (properties `((upstream-name . "missCforest")))
    (build-system r-build-system)
    (propagated-inputs (list r-partykit))
    (home-page "https://github.com/ielbadisy/missCforest")
    (synopsis "Ensemble Conditional Trees for Missing Data Imputation")
    (description
     "Single imputation based on the Ensemble Conditional Trees (i.e.  Cforest
algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007)
<doi:10.1186/1471-2105-8-25>).")
    (license license:gpl3+)))

(define-public r-misreparma
  (package
    (name "r-misreparma")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MisRepARMA" version))
       (sha256
        (base32 "0bay8grq49yin9yy9h9ynb8flsbrb10h4lp7sdzp9ih5b9w21y7z"))))
    (properties `((upstream-name . "MisRepARMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-tseries r-mixtools r-boot))
    (home-page "https://cran.r-project.org/package=MisRepARMA")
    (synopsis "Misreported Time Series Analysis")
    (description
     "This package provides a simple and trustworthy methodology for the analysis of
misreported continuous time series.  See MoriÃ±a, D, FernÃ¡ndez-Fontelo, A,
CabaÃ±a, A, Puig P. (2021) <@code{arXiv:2003.09202v2>}.")
    (license license:gpl2+)))

(define-public r-mispu
  (package
    (name "r-mispu")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MiSPU" version))
       (sha256
        (base32 "1l6q0fm5z5i6gigr940h8kc5rka08psmhcprp8grmsxk3qy2kply"))))
    (properties `((upstream-name . "MiSPU")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-rcpparmadillo
                             r-rcpp
                             r-cluster
                             r-aspu
                             r-ape))
    (home-page "https://cran.r-project.org/package=MiSPU")
    (synopsis "Microbiome Based Sum of Powered Score (MiSPU) Tests")
    (description
     "There is an increasing interest in investigating how the compositions of
microbial communities are associated with human health and disease.  In this
package, we present a novel global testing method called @code{aMiSPU}, that is
highly adaptive and thus high powered across various scenarios, alleviating the
issue with the choice of a phylogenetic distance.  Our simulations and real data
analysis demonstrated that @code{aMiSPU} test was often more powerful than
several competing methods while correctly controlling type I error rates.")
    (license license:gpl2)))

(define-public r-misprime
  (package
    (name "r-misprime")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "misPRIME" version))
       (sha256
        (base32 "1975hnf5sizccrl08mlk47jgqp6xswvkhwg5lpdnzz17w0c62ws1"))))
    (properties `((upstream-name . "misPRIME")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-mass))
    (home-page "https://cran.r-project.org/package=misPRIME")
    (synopsis
     "Partial Replacement Imputation Estimation for Missing Covariates")
    (description
     "Partial Replacement Imputation Estimation (PRIME) can overcome problems caused
by missing covariates in additive partially linear model.  PRIME conducts
imputation and regression simultaneously with known and unknown model structure.
 More details can be referred to Zishu Zhan, Xiangjie Li and Jingxiao Zhang.
(2022) <@code{arXiv:2205.14994>}.")
    (license license:gpl3)))

(define-public r-mispr
  (package
    (name "r-mispr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mispr" version))
       (sha256
        (base32 "166piax3f7di8h0i07fbdx45lxz1p47j378v6yl6zdc3525adpjx"))))
    (properties `((upstream-name . "mispr")))
    (build-system r-build-system)
    (propagated-inputs (list r-penalized r-mass r-e1071))
    (home-page "https://cran.r-project.org/package=mispr")
    (synopsis "Multiple Imputation with Sequential Penalized Regression")
    (description
     "Generates multivariate imputations using sequential regression with L2 penalty.
For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.")
    (license license:gpl2)))

(define-public r-mispitools
  (package
    (name "r-mispitools")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mispitools" version))
       (sha256
        (base32 "09sgs6s005s8lqxb7v21k4vjwkw5kz0dg4b44k11fvgvb8l1i3ba"))))
    (properties `((upstream-name . "mispitools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tidyr
                             r-shiny
                             r-reshape2
                             r-purrr
                             r-pedtools
                             r-patchwork
                             r-ggplot2
                             r-forrel
                             r-dplyr
                             r-dirichletreg))
    (home-page "https://github.com/MarsicoFL/mispitools")
    (synopsis "Missing Person Identification Tools")
    (description
     "An open source software package written in R statistical language.  It consist
in a set of decision making tools to conduct missing person searches.
Particularly, it allows computing optimal LR threshold for declaring potential
matches in DNA-based database search.  More recently mispitools incorporates
preliminary investigation data based LRs.  Statistical weight of different
traces of evidence such as biological sex, age and hair color are presented.
For citing mispitools please use the following references: Marsico and Caridi,
2023 <doi:10.1016/j.fsigen.2023.102891> and Marsico, Vigeland et al.  2021
<doi:10.1016/j.fsigen.2021.102519>.")
    (license license:gpl3+)))

(define-public r-miselect
  (package
    (name "r-miselect")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miselect" version))
       (sha256
        (base32 "01jvxv79irqq1xkl826kqrdx9yx73lifb5xq90f5d34qycp1nxzi"))))
    (properties `((upstream-name . "miselect")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=miselect")
    (synopsis "Variable Selection for Multiply Imputed Data")
    (description
     "Penalized regression methods, such as lasso and elastic net, are used in many
biomedical applications when simultaneous regression coefficient estimation and
variable selection is desired.  However, missing data complicates the
implementation of these methods, particularly when missingness is handled using
multiple imputation.  Applying a variable selection algorithm on each imputed
dataset will likely lead to different sets of selected predictors, making it
difficult to ascertain a final active set without resorting to ad hoc
combination rules.  miselect presents Stacked Adaptive Elastic Net (saenet) and
Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed
by Du et al (2022) <doi:10.1080/10618600.2022.2035739>.  They, by construction,
force selection of the same variables across multiply imputed data.  miselect
also provides cross validated variants of these methods.")
    (license license:gpl3)))

(define-public r-miscset
  (package
    (name "r-miscset")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miscset" version))
       (sha256
        (base32 "1gwi7jnqdf2fa8yk8gmcc7dcv72bbdl7yj3yccj3r5jjk41vdp91"))))
    (properties `((upstream-name . "miscset")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-rcpp
                             r-gridextra
                             r-ggplot2
                             r-devtools
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/setempler/miscset")
    (synopsis "Miscellaneous Tools Set")
    (description
     "This package provides a collection of miscellaneous methods to simplify various
tasks, including plotting, data.frame and matrix transformations, environment
functions, regular expression methods, and string and logical operations, as
well as numerical and statistical tools.  Most of the methods are simple but
useful wrappers of common base R functions, which extend S3 generics or provide
default values for important parameters.")
    (license license:gpl3)))

(define-public r-miscmetabar
  (package
    (name "r-miscmetabar")
    (version "0.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MiscMetabar" version))
       (sha256
        (base32 "0x00g5yyl4w4dj8wsdba7s9jr03makxrbl3wywvjwh3qaa3lv5v7"))))
    (properties `((upstream-name . "MiscMetabar")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-purrr
                             r-phyloseq
                             r-lifecycle
                             r-ggplot2
                             r-dplyr
                             r-dada2
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/adrientaudiere/MiscMetabar")
    (synopsis "Miscellaneous Functions for Metabarcoding Analysis")
    (description
     "Facilitate the description, transformation, exploration, and reproducibility of
metabarcoding analyses. @code{MiscMetabar} is mainly built on top of the
phyloseq', dada2 and targets R packages.  It helps to build reproducible and
robust bioinformatics pipelines in R. @code{MiscMetabar} makes ecological
analysis of alpha and beta-diversity easier, more reproducible and more powerful
by integrating a large number of tools.  Important features are described in
TaudiÃ¨re A. (2023) <doi:10.21105/joss.06038>.")
    (license license:agpl3)))

(define-public r-misclassglm
  (package
    (name "r-misclassglm")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "misclassGLM" version))
       (sha256
        (base32 "0iph5nz4hnfgx32xwc8z201ap18b543x8by6hcpkbk8jcbxswhjq"))))
    (properties `((upstream-name . "misclassGLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-ucminf
                             r-numderiv
                             r-mlogit
                             r-matrix
                             r-mass
                             r-foreach))
    (home-page "https://cran.r-project.org/package=misclassGLM")
    (synopsis
     "Computation of Generalized Linear Models with Misclassified Covariates Using Side Information")
    (description
     "Estimates models that extend the standard GLM to take misclassification into
account.  The models require side information from a secondary data set on the
misclassification process, i.e.  some sort of misclassification probabilities
conditional on some common covariates.  A detailed description of the algorithm
can be found in Dlugosz, Mammen and Wilke (2015)
<https://www.zew.de/publikationen/generalised-partially-linear-regression-with-misclassified-data-and-an-application-to-labour-market-transitions>.")
    (license license:gpl3)))

(define-public r-miscic
  (package
    (name "r-miscic")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miscIC" version))
       (sha256
        (base32 "0pnwc3b318xxnmb7scrj4qkqlvqfyczspdg496kl0ixsiaafr1ad"))))
    (properties `((upstream-name . "miscIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnls))
    (home-page "https://cran.r-project.org/package=miscIC")
    (synopsis "Misclassified Interval Censored Time-to-Event Data")
    (description
     "Estimation of the survivor function for interval censored time-to-event data
subject to misclassification using nonparametric maximum likelihood estimation,
implementing the methods of Titman (2017) <doi:10.1007/s11222-016-9705-7>.
Misclassification probabilities can either be specified as fixed or estimated.
Models with time dependent misclassification may also be fitted.")
    (license license:gpl2+)))

(define-public r-miscfuncs
  (package
    (name "r-miscfuncs")
    (version "1.5-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miscFuncs" version))
       (sha256
        (base32 "119im7av5skmqcyf1lsywbpq3kckka2z8kn5hgd9vkrjcj2qnnd7"))))
    (properties `((upstream-name . "miscFuncs")))
    (build-system r-build-system)
    (propagated-inputs (list r-roxygen2 r-mvtnorm r-extradistr))
    (home-page "https://cran.r-project.org/package=miscFuncs")
    (synopsis
     "Miscellaneous Useful Functions Including LaTeX Tables, Kalman Filtering, QQplots with Simulation-Based Confidence Intervals, Linear Regression Diagnostics and Development Tools")
    (description
     "Implementing various things including functions for @code{LaTeX} tables, the
Kalman filter, QQ-plots with simulation-based confidence intervals, linear
regression diagnostics, web scraping, development tools, relative risk and odds
rati, GARCH(1,1) Forecasting.")
    (license license:gpl3)))

(define-public r-miscf
  (package
    (name "r-miscf")
    (version "0.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miscF" version))
       (sha256
        (base32 "0g6wk2zxqq6j949hnbvwmfd72y25zjfbrfrfxsj21q84w41fgjkb"))))
    (properties `((upstream-name . "miscF")))
    (build-system r-build-system)
    (propagated-inputs (list r-r2jags r-mvtnorm r-mcmcpack r-mass))
    (home-page "https://cran.r-project.org/package=miscF")
    (synopsis "Miscellaneous Functions")
    (description
     "Various functions for random number generation, density estimation,
classification, curve fitting, and spatial data analysis.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-misaem
  (package
    (name "r-misaem")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "misaem" version))
       (sha256
        (base32 "05m7k8mgbpkv5szislvf3q45pk5f8z54ibddhi8pkhwa9w00xkjk"))))
    (properties `((upstream-name . "misaem")))
    (build-system r-build-system)
    (propagated-inputs (list r-norm r-mvtnorm r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/julierennes/misaem")
    (synopsis
     "Linear Regression and Logistic Regression with Missing Covariates")
    (description
     "Estimate parameters of linear regression and logistic regression with missing
covariates with missing data, perform model selection and prediction, using
EM-type algorithms.  Jiang W., Josse J., Lavielle M., @code{TraumaBase} Group
(2020) <doi:10.1016/j.csda.2019.106907>.")
    (license license:gpl3)))

(define-public r-mirtsvd
  (package
    (name "r-mirtsvd")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mirtsvd" version))
       (sha256
        (base32 "0c06dpg0jr15hmk03s96fyp2rb6z4svihjzfs316nkl3dd03ami1"))))
    (properties `((upstream-name . "mirtsvd")))
    (build-system r-build-system)
    (propagated-inputs (list r-mirtjml r-gparotation))
    (home-page "https://cran.r-project.org/package=mirtsvd")
    (synopsis "SVD-Based Estimation for Exploratory Item Factor Analysis")
    (description
     "This package provides singular value decomposition based estimation algorithms
for exploratory item factor analysis (IFA) based on multidimensional item
response theory models.  For more information, please refer to: Zhang, H., Chen,
Y., & Li, X. (2020).  A note on exploratory item factor analysis by singular
value decomposition.  Psychometrika, 1-15, <DOI:10.1007/s11336-020-09704-7>.")
    (license license:gpl3)))

(define-public r-mirtjml
  (package
    (name "r-mirtjml")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mirtjml" version))
       (sha256
        (base32 "1zvm8acywpzi87lz0hpxmr8c9kyqkbx0490q3vlwxd90ffcdp9ml"))))
    (properties `((upstream-name . "mirtjml")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-gparotation))
    (home-page "https://github.com/slzhang-fd/mirtjml")
    (synopsis
     "Joint Maximum Likelihood Estimation for High-Dimensional Item Factor Analysis")
    (description
     "This package provides constrained joint maximum likelihood estimation algorithms
for item factor analysis (IFA) based on multidimensional item response theory
models.  So far, we provide functions for exploratory and confirmatory IFA based
on the multidimensional two parameter logistic (M2PL) model for binary response
data.  Comparing with traditional estimation methods for IFA, the methods
implemented in this package scale better to data with large numbers of
respondents, items, and latent factors.  The computation is facilitated by
multiprocessing @code{OpenMP} API. For more information, please refer to: 1.
Chen, Y., Li, X., & Zhang, S. (2018).  Joint Maximum Likelihood Estimation for
High-Dimensional Exploratory Item Factor Analysis.  Psychometrika, 1-23.
<doi:10.1007/s11336-018-9646-5>; 2.  Chen, Y., Li, X., & Zhang, S. (2019).
Structured Latent Factor Analysis for Large-scale Data: Identifiability,
Estimability, and Their Implications.  Journal of the American Statistical
Association, <doi: 10.1080/01621459.2019.1635485>.")
    (license license:gpl3)))

(define-public r-mirtest
  (package
    (name "r-mirtest")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miRtest" version))
       (sha256
        (base32 "1qgyldf3yhny8dmix6drisrrn64s5h58yvmk5pch6irdddq3i053"))))
    (properties `((upstream-name . "miRtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-limma r-globaltest r-globalancova
                             r-corpcor))
    (home-page "https://pubmed.ncbi.nlm.nih.gov/22723856/")
    (synopsis "Combined miRNA- And mRNA-Testing")
    (description "Package for combined @code{miRNA}- and @code{mRNA-testing}.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mirtcat
  (package
    (name "r-mirtcat")
    (version "1.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mirtCAT" version))
       (sha256
        (base32 "02774rxl1rpk7j60bhib77yygj80c0c0s3fzzwjzdpsz91hg29z1"))))
    (properties `((upstream-name . "mirtCAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-mirt
                             r-markdown
                             r-lpsolve
                             r-lattice))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/philchalmers/mirtCAT")
    (synopsis
     "Computerized Adaptive Testing with Multidimensional Item Response Theory")
    (description
     "This package provides tools to generate HTML interfaces for adaptive and
non-adaptive tests using the shiny package (Chalmers (2016)
<doi:10.18637/jss.v071.i05>).  Suitable for applying unidimensional and
multidimensional computerized adaptive tests (CAT) using item response theory
methodology and for creating simple questionnaires forms to collect response
data directly in R. Additionally, optimal test designs (e.g., \"shadow testing\")
are supported for tests that contain a large number of item selection
constraints.  Finally, package contains tools useful for performing Monte Carlo
simulations for studying test item banks.")
    (license license:gpl3+)))

(define-public r-mirt
  (package
    (name "r-mirt")
    (version "1.42")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mirt" version))
       (sha256
        (base32 "15yzgph7957wvrfnxwc9p27hckamkkxmi0c02x6p5i505ya16lly"))))
    (properties `((upstream-name . "mirt")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-simdesign
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-mgcv
                             r-matrix
                             r-lattice
                             r-gridextra
                             r-gparotation
                             r-deriv
                             r-dcurver))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/philchalmers/mirt")
    (synopsis "Multidimensional Item Response Theory")
    (description
     "Analysis of discrete response data using unidimensional and multidimensional
item analysis models under the Item Response Theory paradigm (Chalmers (2012)
<doi:10.18637/jss.v048.i06>).  Exploratory and confirmatory item factor analysis
models are estimated with quadrature (EM) or stochastic (MHRM) methods.
Confirmatory bi-factor and two-tier models are available for modeling item
testlets using dimension reduction EM algorithms, while multiple group analyses
and mixed effects designs are included for detecting differential item, bundle,
and test functioning, and for modeling item and person covariates.  Finally,
latent class models such as the DINA, DINO, multidimensional latent class,
mixture IRT models, and zero-inflated response models are supported.")
    (license license:gpl3+)))

(define-public r-mirsea
  (package
    (name "r-mirsea")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MiRSEA" version))
       (sha256
        (base32 "1pjahnsaiw2rcfcdhavq65d4gkp224aw8w69l731dlkb9iiydbgf"))))
    (properties `((upstream-name . "MiRSEA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MiRSEA")
    (synopsis "'MicroRNA' Set Enrichment Analysis")
    (description
     "The tools for @code{MicroRNA} Set Enrichment Analysis can identify risk
pathways(or prior gene sets) regulated by @code{microRNA} set in the context of
@code{microRNA} expression data. (1) This package constructs a correlation
profile of @code{microRNA} and pathways by the hypergeometric statistic test.
The gene sets of pathways derived from the three public databases (Kyoto
Encyclopedia of Genes and Genomes ('KEGG'); Reactome'; Biocarta') and the target
gene sets of @code{microRNA} are provided by four
databases('@code{TarBaseV6.0}'; mir2Disease'; @code{miRecords}';
@code{miRTarBase}';). (2) This package can quantify the change of correlation
between @code{microRNA} for each pathway(or prior gene set) based on a
@code{microRNA} expression data with cases and controls. (3) This package uses
the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES)
of a @code{microRNA} set that co-regulate to a pathway , which reflects the
degree to which a given pathway is associated with the specific phenotype. (4)
This package can provide the visualization of the results.")
    (license license:gpl2+)))

(define-public r-mirrorselect
  (package
    (name "r-mirrorselect")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mirrorselect" version))
       (sha256
        (base32 "1xxgf1w7njylgm8zjp36js8wirxbvc4q0kj6xh8y5lmb7hklnk7i"))))
    (properties `((upstream-name . "mirrorselect")))
    (build-system r-build-system)
    (propagated-inputs (list r-yulab-utils))
    (home-page "https://cran.r-project.org/package=mirrorselect")
    (synopsis "Test CRAN/Bioconductor Mirror Speed")
    (description
     "Testing CRAN and Bioconductor mirror speed by recording download time of
src/base/COPYING (for CRAN) and packages/release/bioc/html/ggtree.html (for
Bioconductor).")
    (license license:artistic2.0)))

(define-public r-mirnass
  (package
    (name "r-mirnass")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miRNAss" version))
       (sha256
        (base32 "0j4yvd0j7r98m2msllszlzkzrx67a2z4xpx00zpq90hbr5jykxc4"))))
    (properties `((upstream-name . "miRNAss")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-rcpp r-matrix r-corelearn))
    (home-page "https://cran.r-project.org/package=miRNAss")
    (synopsis "Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples")
    (description
     "Machine learning method specifically designed for pre-@code{miRNA} prediction.
It takes advantage of unlabeled sequences to improve the prediction rates even
when there are just a few positive examples, when the negative examples are
unreliable or are not good representatives of its class.  Furthermore, the
method can automatically search for negative examples if the user is unable to
provide them. @code{MiRNAss} can find a good boundary to divide the
pre-@code{miRNAs} from other groups of sequences; it automatically optimizes the
threshold that defines the classes boundaries, and thus, it is robust to high
class imbalance.  Each step of the method is scalable and can handle large
volumes of data.")
    (license license:asl2.0)))

(define-public r-mirnaqcd
  (package
    (name "r-mirnaqcd")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MiRNAQCD" version))
       (sha256
        (base32 "0512zv75adic1mw5ccs076b9yczmqrx514jibbbid33m5bv877hm"))))
    (properties `((upstream-name . "MiRNAQCD")))
    (build-system r-build-system)
    (propagated-inputs (list r-qpdf r-proc r-ggplot2))
    (home-page "https://cran.r-project.org/package=MiRNAQCD")
    (synopsis "Micro-RNA Quality Control and Diagnosis")
    (description
     "This package provides a complete and dedicated analytical toolbox for quality
control and diagnosis based on subject-related measurements of micro-RNA
(@code{miRNA}) expressions.  The package consists of a set of functions that
allow to train, optimize and use a Bayesian classifier that relies on multiplets
of measured @code{miRNA} expressions.  The package also implements the quality
control tools required to preprocess input datasets.  In addition, the package
provides a function to carry out a statistical analysis of @code{miRNA}
expressions, which can give insights to improve the classifier's performance.
The method implemented in the package was first introduced in L. Ricci, V. Del
Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, \"Statistical
analysis of a Bayesian classifier based on the expression of @code{miRNAs}\", BMC
Bioinformatics 16:287, 2015 <doi:10.1186/s12859-015-0715-9>.  The package is
thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti
and L. Ricci, \"@code{MiRNA-QC-and-Diagnosis}: An R package for diagnosis based
on @code{MiRNA} expression\", @code{SoftwareX} 12:100569, 2020
<doi:10.1016/j.softx.2020.100569>.  Please cite both these works if you use the
package for your analysis.  DISCLAIMER: The software in this package is for
general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is
NOT intended to form the basis of clinical decisions.  Please refer to the GNU
General Public License 3.0 (GPLv3) for further information.")
    (license license:gpl3)))

(define-public r-mirkat
  (package
    (name "r-mirkat")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MiRKAT" version))
       (sha256
        (base32 "0y9j2dmw69a1n63mnbz8qg4pk69dzzhg42l3cq9i0s0yg8pp4pj6"))))
    (properties `((upstream-name . "MiRKAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-quantreg
                             r-permute
                             r-pearsonds
                             r-mixtools
                             r-matrix
                             r-mass
                             r-lme4
                             r-gunifrac
                             r-compquadform))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=MiRKAT")
    (synopsis "Microbiome Regression-Based Kernel Association Tests")
    (description
     "Test for overall association between microbiome composition data and phenotypes
via phylogenetic kernels.  The phenotype can be univariate continuous or binary
(Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), survival outcomes
(Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), multivariate (Zhan et
al. (2017) <doi:10.1002/gepi.22030>) and structured phenotypes (Zhan et al.
(2017) <doi:10.1111/biom.12684>).  The package can also use robust regression
(unpublished work) and integrated quantile regression (Wang et al. (2021)
<doi:10.1093/bioinformatics/btab668>).  In each case, the microbiome community
effect is modeled nonparametrically through a kernel function, which can
incorporate phylogenetic tree information.")
    (license license:gpl2+)))

(define-public r-miretrieve
  (package
    (name "r-miretrieve")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miRetrieve" version))
       (sha256
        (base32 "0y9ycvqyxax4qdn4inwl8jnv77fvhs2i0b4v8nmrcvk1kvb1v8jw"))))
    (properties `((upstream-name . "miRetrieve")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xml2
                             r-wordcloud
                             r-topicmodels
                             r-tidytext
                             r-tidyr
                             r-textclean
                             r-stringr
                             r-scales
                             r-rlang
                             r-readxl
                             r-readr
                             r-purrr
                             r-plotly
                             r-openxlsx
                             r-magrittr
                             r-ggplot2
                             r-forcats
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=miRetrieve")
    (synopsis "miRNA Text Mining in Abstracts")
    (description
     "Providing tools for @code{microRNA} (@code{miRNA}) text mining.
@code{miRetrieve} summarizes @code{miRNA} literature by extracting, counting,
and analyzing @code{miRNA} names, thus aiming at gaining biological insights
into a large amount of text within a short period of time.  To do so,
@code{miRetrieve} uses regular expressions to extract @code{miRNAs} and
tokenization to identify meaningful @code{miRNA} associations.  In addition,
@code{miRetrieve} uses the latest @code{miRTarBase} version 8.0 (Hsi-Yuan Huang
et al. (2020) \"@code{miRTarBase} 2020: updates to the experimentally validated
@code{microRNAâtarget} interaction database\" <doi:10.1093/nar/gkz896>) to
display field-specific @code{miRNA-mRNA} interactions.  The most important
functions are available as a Shiny web application under
<https://miretrieve.shinyapps.io/@code{miRetrieve/>}.")
    (license license:gpl3)))

(define-public r-mires
  (package
    (name "r-mires")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIRES" version))
       (sha256
        (base32 "17k6f73qx85klhhn247nr6h7sinpabq9crlc5ag7qm6z7k6mr370"))))
    (properties `((upstream-name . "MIRES")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppeigen
                             r-rcpp
                             r-pracma
                             r-nlme
                             r-mvtnorm
                             r-logspline
                             r-hdinterval
                             r-formula
                             r-dirichletprocess
                             r-cubature
                             r-bh))
    (home-page "https://cran.r-project.org/package=MIRES")
    (synopsis
     "Measurement Invariance Assessment Using Random Effects Models and Shrinkage")
    (description
     "Estimates random effect latent measurement models, wherein the loadings,
residual variances, intercepts, latent means, and latent variances all vary
across groups.  The random effect variances of the measurement parameters are
then modeled using a hierarchical inclusion model, wherein the inclusion of the
variances (i.e., whether it is effectively zero or non-zero) is informed by
similar parameters (of the same type, or of the same item).  This additional
hierarchical structure allows the evidence in favor of partial invariance to
accumulate more quickly, and yields more certain decisions about measurement
invariance.  Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.")
    (license license:expat)))

(define-public r-mirecsurv
  (package
    (name "r-mirecsurv")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miRecSurv" version))
       (sha256
        (base32 "185bkqikkq0maagzwy4y3lxzq8vy1bjz7cz4dhk3v9818dr3fx3g"))))
    (properties `((upstream-name . "miRecSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-stringi r-matrixstats
                             r-compoissonreg))
    (home-page "https://cran.r-project.org/package=miRecSurv")
    (synopsis "Left-Censored Recurrent Events Survival Models")
    (description
     "Fitting recurrent events survival models for left-censored data with multiple
imputation of the number of previous episodes.  See HernÃ¡ndez-Herrera G,
MoriÃ±a D, Navarro A. (2020) <@code{arXiv:2007.15031>}.")
    (license license:gpl2+)))

(define-public r-mirai
  (package
    (name "r-mirai")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mirai" version))
       (sha256
        (base32 "1zyf9higa7c9bnbzxz10xbqd66yhb3w9y5lfgc97mkmgnmd2y685"))))
    (properties `((upstream-name . "mirai")))
    (build-system r-build-system)
    (propagated-inputs (list r-nanonext))
    (native-inputs (list r-litedown))
    (home-page "https://shikokuchuo.net/mirai/")
    (synopsis "Minimalist Async Evaluation Framework for R")
    (description
     "Designed for simplicity, a mirai evaluates an R expression asynchronously in a
parallel process, locally or distributed over the network, with the result
automatically available upon completion.  Modern networking and concurrency
built on nanonext and NNG (Nanomsg Next Gen) ensure reliable and efficient
scheduling, over fast inter-process communications or TCP/IP secured by TLS.
Advantages include being inherently queued thus handling many more tasks than
available processes, no storage on the file system, support for otherwise
non-exportable reference objects, an event-driven promises implementation, and
built-in asynchronous parallel map.")
    (license license:gpl3+)))

(define-public r-mipplot
  (package
    (name "r-mipplot")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mipplot" version))
       (sha256
        (base32 "126arvxq6gmaiwk3n3p0slnb1mhs3qf993m4lx95nlmnv612ag66"))))
    (properties `((upstream-name . "mipplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-showtextdb
                             r-showtext
                             r-shinywidgets
                             r-shinyalert
                             r-shiny-i18n
                             r-shiny
                             r-rlang
                             r-reshape2
                             r-reshape
                             r-readxl
                             r-readr
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mipplot")
    (synopsis
     "An Open-Source Tool for Visualization of Climate Mitigation Scenarios")
    (description
     "Generic functions to produce area/bar/box/line plots of data following IAMC
(Integrated Assessment Modeling Consortium) submission format.")
    (license license:expat)))

(define-public r-mipfp
  (package
    (name "r-mipfp")
    (version "3.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mipfp" version))
       (sha256
        (base32 "1gxazpg81vj5dywpb6jb29188jw28qil6pfygawa7znnjn3k5ca6"))))
    (properties `((upstream-name . "mipfp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-numderiv r-cmm))
    (home-page "https://github.com/jojo-/mipfp")
    (synopsis
     "Multidimensional Iterative Proportional Fitting and Alternative Models")
    (description
     "An implementation of the iterative proportional fitting (IPFP), maximum
likelihood, minimum chi-square and weighted least squares procedures for
updating a N-dimensional array with respect to given target marginal
distributions (which, in turn can be multidimensional).  The package also
provides an application of the IPFP to simulate multivariate Bernoulli
distributions.")
    (license license:gpl2)))

(define-public r-mintplates
  (package
    (name "r-mintplates")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MINTplates" version))
       (sha256
        (base32 "1jj51314p2ni3f3ncpd8gp7pgshhsp97ic123d732sc9xr0h5rnr"))))
    (properties `((upstream-name . "MINTplates")))
    (build-system r-build-system)
    (home-page "http://www.bio-inf.cn/")
    (synopsis "Encode \"License-Plates\" from Sequences and Decode Them Back")
    (description
     "It can be used to create/encode molecular \"license-plates\" from sequences and to
also decode the \"license-plates\" back to sequences.  While initially created for
transfer RNA-derived small fragments (@code{tRFs}), this tool can be used for
any genomic sequences including but not limited to: @code{tRFs},
@code{microRNAs}, etc.  The detailed information can reference to Pliatsika V,
Loher P, Telonis AG, Rigoutsos I (2016) <doi:10.1093/bioinformatics/btw194>.  It
can also be used to annotate @code{tRFs}.  The detailed information can
reference to Loher P, Telonis AG, Rigoutsos I (2017) <doi:10.1038/srep41184>.")
    (license license:gpl2+)))

(define-public r-minsnps
  (package
    (name "r-minsnps")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minSNPs" version))
       (sha256
        (base32 "00kg0mqr282hc7fj8dzagvlmm6d0h9wl5khx03b9yzpw42cs0yf5"))))
    (properties `((upstream-name . "minSNPs")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-biocparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ludwigHoon/minSNPs")
    (synopsis "Resolution-Optimised SNPs Searcher")
    (description
     "This is a R implementation of \"Minimum SNPs\" software as described in \"Price
E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M.\"
(2007) <doi:10.1186/1471-2105-8-278> \"Computer-aided identification of
polymorphism sets diagnostic for groups of bacterial and viral genetic
variants.\".")
    (license license:expat)))

(define-public r-minsample2
  (package
    (name "r-minsample2")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minsample2" version))
       (sha256
        (base32 "0q1rp6mdyhcp806a0m0sr4ldm5rj78wk2fljzylxfjs4c734lpbk"))))
    (properties `((upstream-name . "minsample2")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=minsample2")
    (synopsis "The Minimum Sample Size")
    (description
     "Using this package, one can determine the minimum sample size required so that
the mean square error of the sample mean and the population mean of a
distribution becomes less than some pre-determined epsilon, i.e.  it helps the
user to determine the minimum sample size required to attain the pre-fixed
precision level by minimizing the difference between the sample mean and
population mean.")
    (license license:gpl3)))

(define-public r-minsample1
  (package
    (name "r-minsample1")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minsample1" version))
       (sha256
        (base32 "13kmqxa3pbllv16fzn0v7msffsaar79y8xnvnbrcfhwa76031f8f"))))
    (properties `((upstream-name . "minsample1")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=minsample1")
    (synopsis "The Minimum Sample Size")
    (description
     "Using this package, one can determine the minimum sample size required so that
the absolute deviation of the sample mean and the population mean of a
distribution becomes less than some pre-determined epsilon, i.e.  it helps the
user to determine the minimum sample size required to attain the pre-fixed
precision level by minimizing the difference between the sample mean and
population mean.")
    (license license:gpl3)))

(define-public r-minque
  (package
    (name "r-minque")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minque" version))
       (sha256
        (base32 "0pczdg22i3if048i64chzcpnv3ay8p0anmhwjmzm29577z0jgms8"))))
    (properties `((upstream-name . "minque")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=minque")
    (synopsis "Various Linear Mixed Model Analyses")
    (description
     "This package offers three important components: (1) to construct a use-defined
linear mixed model, (2) to employ one of linear mixed model approaches: minimum
norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component
estimation and random effect prediction; and (3) to employ a jackknife
resampling technique to conduct various statistical tests.  In addition, this
package provides the function for model or data evaluations.This R package
offers fast computations for large data sets analyses for various irregular data
structures.")
    (license license:gpl3)))

(define-public r-minmse
  (package
    (name "r-minmse")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minMSE" version))
       (sha256
        (base32 "1fj6njqdc6zn73n2q60x01znwn6xlprjldwy5c13d8rd1n5shccm"))))
    (properties `((upstream-name . "minMSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mass))
    (home-page "https://www.sebastianoschneider.com")
    (synopsis
     "Implementation of the minMSE Treatment Assignment Method for One or Multiple Treatment Groups")
    (description
     "This package performs treatment assignment for (field) experiments considering
available, possibly multivariate and continuous, information (covariates,
observable characteristics), that is: forms balanced treatment groups, according
to the @code{minMSE-method} as proposed by Schneider and Schlather (2017)
<DOI:10419/161931>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-minirand
  (package
    (name "r-minirand")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Minirand" version))
       (sha256
        (base32 "19lib0qh3xfzvpgsskl3yypmjygss0w7pimz0136av0px5b4jran"))))
    (properties `((upstream-name . "Minirand")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Minirand")
    (synopsis "Minimization Randomization")
    (description
     "Randomization schedules are generated in the schemes with k (k>=2) treatment
groups and any allocation ratios by minimization algorithms.")
    (license license:gpl2+)))

(define-public r-minipch
  (package
    (name "r-minipch")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miniPCH" version))
       (sha256
        (base32 "1z8zsagas50ndwygkxfdi933x5vq3dmq5kwmi3lg28hqdaf2hcnm"))))
    (properties `((upstream-name . "miniPCH")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-checkmate))
    (home-page "https://simnph.github.io/miniPCH/")
    (synopsis "Survival Distributions with Piece-Wise Constant Hazards")
    (description
     "Density, distribution function, ...  hazard function, cumulative hazard
function, survival function for survival distributions with piece-wise constant
hazards and multiple states.")
    (license license:gpl3+)))

(define-public r-minioclient
  (package
    (name "r-minioclient")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minioclient" version))
       (sha256
        (base32 "1caqigfjlga5vv5nk7k0jmzhiakj4xrbvpf43n0byygqqfpxd6i8"))))
    (properties `((upstream-name . "minioclient")))
    (build-system r-build-system)
    (propagated-inputs (list r-processx r-jsonlite r-glue r-fs))
    (home-page "https://github.com/cboettig/minioclient")
    (synopsis "Interface to the 'MinIO' Client")
    (description
     "An R interface to the @code{MinIO} Client.  The @code{MinIO} Client ('mc')
provides a modern alternative to UNIX commands like ls', cat', cp', mirror',
diff', find etc.  It supports filesystems and Amazon \"S3\" compatible cloud
storage service (\"AWS\" Signature v2 and v4).  This package provides convenience
functions for installing the @code{MinIO} client and running any operations, as
described in the official documentation,
<https://min.io/docs/minio/linux/reference/minio-mc.html?ref=docs-redirect>.
This package provides a flexible and high-performance alternative to aws.s3'.")
    (license license:expat)))

(define-public r-minimeta
  (package
    (name "r-minimeta")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miniMeta" version))
       (sha256
        (base32 "1v0nfmk034xz6nn0pnas0g11bbg0k86c99qykqadqr9kr9iwjrki"))))
    (properties `((upstream-name . "miniMeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexls
                             r-shinywidgets
                             r-shinyjs
                             r-shiny
                             r-rhandsontable
                             r-readxl
                             r-metafor
                             r-meta
                             r-markdown
                             r-jsonlite
                             r-colourpicker))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thlytras/miniMeta")
    (synopsis "Web Application to Run Meta-Analyses")
    (description
     "Shiny web application to run meta-analyses.  Essentially a graphical front-end
to package meta for R. Can be useful as an educational tool, and for quickly
analyzing and sharing meta-analyses.  Provides output to quickly fill in GRADE
(Grading of Recommendations, Assessment, Development and Evaluations)
Summary-of-Findings tables.  Importantly, it allows further processing of the
results inside R, in case more specific analyses are needed.")
    (license license:gpl2+)))

(define-public r-minimaxapprox
  (package
    (name "r-minimaxapprox")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minimaxApprox" version))
       (sha256
        (base32 "1gk7rh17g3xdsr42a1f6lp23ny9vkzig8srfwscd0v5hxx2cbqr9"))))
    (properties `((upstream-name . "minimaxApprox")))
    (build-system r-build-system)
    (home-page "https://github.com/aadler/MiniMaxApprox")
    (synopsis
     "Implementation of Remez Algorithm for Polynomial and Rational Function Approximation")
    (description
     "This package implements the algorithm of Remez (1962) for polynomial minimax
approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational
minimax approximation.")
    (license (license:fsdg-compatible "MPL-2.0"))))

(define-public r-minimax
  (package
    (name "r-minimax")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minimax" version))
       (sha256
        (base32 "1jx90xbxflsi1q2wm3dl4vhz246yxbvhc8zybiv5f7fafam28h6p"))))
    (properties `((upstream-name . "minimax")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=minimax")
    (synopsis "The Minimax Distribution Family")
    (description
     "The minimax family of distributions is a two-parameter family like the beta
family, but computationally a lot more tractible.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-minimapr
  (package
    (name "r-minimapr")
    (version "0.0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minimapR" version))
       (sha256
        (base32 "1cf0mp0mc3zalsi4fa2khz139hky0vjzj0cfay70prh5cll7dm3b"))))
    (properties `((upstream-name . "minimapR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsamtools r-pafr r-git2r))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jake-bioinfo/minimapR")
    (synopsis "Wrapper for 'minimap2'")
    (description
     "Wrapper for Minimap2'.  Minimap2 is a very valuable long read aligner for the
Pacbio and Oxford Nanopore Technologies sequencing platforms. @code{minimapR} is
an R wrapper for minimap2 which was developed by Heng Li <me@@liheng.org>.
*SPECIAL NOTES 1.  Examples can only be run from @code{GitHub} installation.  2.
 If using a Windows operating system, installation of the MSYS2 Linux emulator
is required.  3.  If using a Mac operating system, installation of Homebrew is
required.  Li, Heng (2018) <doi:10.1093/bioinformatics/bty191> \"Minimap2:
pairwise alignment for nucleotide sequences\".")
    (license license:expat)))

(define-public r-minimap
  (package
    (name "r-minimap")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minimap" version))
       (sha256
        (base32 "0y5yzic9pwpzs01gnl82syankijcjp85n22jn5zda0bp3y01r53r"))))
    (properties `((upstream-name . "minimap")))
    (build-system r-build-system)
    (home-page "http://github.com/seankross/minimap")
    (synopsis "Create Tile Grid Maps")
    (description
     "Create tile grid maps, which are like choropleth maps except each region is
represented with equal visual space.")
    (license license:expat)))

(define-public r-minimalrsd
  (package
    (name "r-minimalrsd")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minimalRSD" version))
       (sha256
        (base32 "1p1jpi8lnv952k4c1wjgbgpb0hv4zg0f9qrd43cx0caibkqvhzf7"))))
    (properties `((upstream-name . "minimalRSD")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=minimalRSD")
    (synopsis "Minimally Changed CCD and BBD")
    (description
     "Generate central composite designs (CCD)with full as well as fractional
factorial points (half replicate) and Box Behnken designs (BBD) with minimally
changed run sequence.")
    (license license:gpl2+)))

(define-public r-minilnm
  (package
    (name "r-minilnm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miniLNM" version))
       (sha256
        (base32 "1ljs6av8lhghib5kn9lq9pzmfjv18l4vqx2qyw8jg4wm2lck3ln3"))))
    (properties `((upstream-name . "miniLNM")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-posterior
                             r-glue
                             r-formula-tools
                             r-fansi
                             r-dplyr
                             r-cli
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/krisrs1128/miniLNM/")
    (synopsis "Miniature Logistic-Normal Multinomial Models")
    (description
     "Logistic-normal Multinomial (LNM) models are common in problems with
multivariate count data.  This package gives a simple implementation with a 30
line Stan script.  This lightweight implementation makes it an easy starting
point for other projects, in particular for downstream tasks that require
analysis of \"compositional\" data.  It can be applied whenever a multinomial
probability parameter is thought to depend linearly on inputs in a transformed,
log ratio space.  Additional utilities make it easy to inspect, create
predictions, and draw samples using the fitted models.  More about the LNM can
be found in Xia et al. (2013) \"A Logistic Normal Multinomial Regression Model
for Microbiome Compositional Data Analysis\" <doi:10.1111/biom.12079> and
Sankaran and Holmes (2023) \"Generative Models: An Interdisciplinary Perspective\"
<doi:10.1146/annurev-statistics-033121-110134>.")
    (license license:cc0)))

(define-public r-minigui
  (package
    (name "r-minigui")
    (version "0.8-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miniGUI" version))
       (sha256
        (base32 "0qb2fbkjwzn909yrk89d4951bwf8n05i0s265l8v2d4lpns9hc2i"))))
    (properties `((upstream-name . "miniGUI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=miniGUI")
    (synopsis "Tcl/Tk Quick and Simple Function GUI")
    (description
     "Quick and simple Tcl/Tk Graphical User Interface to call functions.  Also
comprises a very simple experimental GUI framework.")
    (license (license:fsdg-compatible "GPL (<= 2.0)"))))

(define-public r-minidown
  (package
    (name "r-minidown")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minidown" version))
       (sha256
        (base32 "0h4szlw0dgxbdsw8grnzv50bq0qzzk3lm8zr1zp2pybisfvqaqkr"))))
    (properties `((upstream-name . "minidown")))
    (build-system r-build-system)
    (propagated-inputs (list r-xfun
                             r-sass
                             r-rmarkdown
                             r-mime
                             r-knitr
                             r-katex
                             r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://minidown.atusy.net")
    (synopsis
     "Create Simple Yet Powerful HTML Documents with Light Weight CSS Frameworks")
    (description
     "Create minimal, responsive, and style-agnostic HTML documents with the
lightweight CSS frameworks such as sakura', Water.css', and spcss'.  Powerful
features include table of contents floating as a sidebar, folding codes and
results, and more.")
    (license license:expat)))

(define-public r-minicran
  (package
    (name "r-minicran")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miniCRAN" version))
       (sha256
        (base32 "1dfqish0vhvdp1a6kiw18z088sghndv9pviby51s7kqv0bjs3ca7"))))
    (properties `((upstream-name . "miniCRAN")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-httr r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/andrie/miniCRAN")
    (synopsis
     "Create a Mini Version of CRAN Containing Only Selected Packages")
    (description
     "Makes it possible to create an internally consistent repository consisting of
selected packages from CRAN-like repositories.  The user specifies a set of
desired packages, and @code{miniCRAN} recursively reads the dependency tree for
these packages, then downloads only this subset.  The user can then install
packages from this repository directly, rather than from CRAN. This is useful in
production settings, e.g. server behind a firewall, or remote locations with
slow (or zero) Internet access.")
    (license license:gpl2)))

(define-public r-minic
  (package
    (name "r-minic")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minic" version))
       (sha256
        (base32 "0p95vzld2v7j4z25hxi0vkx9y7xdzxnjhsh11k4v63yqfl6iiikx"))))
    (properties `((upstream-name . "minic")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://github.com/BertvanderVeen/minic")
    (synopsis "Minimization Methods for Ill-Conditioned Problems")
    (description
     "Implementation of methods for minimizing ill-conditioned problems.  Currently
only includes regularized (quasi-)newton optimization (Kanzow and Steck et al.
(2023), <doi:10.1007/s12532-023-00238-4>).")
    (license license:gpl2+)))

(define-public r-minesweepr
  (package
    (name "r-minesweepr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mineSweepR" version))
       (sha256
        (base32 "0bnaa0652pspsd80v66b83np58429g8044941bsmr3apgf8660gj"))))
    (properties `((upstream-name . "mineSweepR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-pals
                             r-mmand
                             r-mgc
                             r-hms
                             r-gsignal
                             r-dplyr
                             r-complexheatmap))
    (home-page "https://cran.r-project.org/package=mineSweepR")
    (synopsis "Mine Sweeper Game")
    (description
     "This is the very popular mine sweeper game! The game requires you to find out
tiles that contain mines through clues from unmasking neighboring tiles.  Each
tile that does not contain a mine shows the number of mines in its adjacent
tiles.  If you unmask all tiles that do not contain mines, you win the game; if
you unmask any tile that contains a mine, you lose the game.  For further game
instructions, please run `help(run_game)` and check details.  This game runs in
X11-compatible devices with `@code{grDevices::x11()`}.")
    (license license:expat)))

(define-public r-minesweeper
  (package
    (name "r-minesweeper")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minesweeper" version))
       (sha256
        (base32 "17f4phqayjl82y8p80y0bmmigs244lj7c0jyl8mpa89vib5vnpr7"))))
    (properties `((upstream-name . "minesweeper")))
    (build-system r-build-system)
    (home-page "https://github.com/hrryt/minesweeper")
    (synopsis "Play Minesweeper")
    (description
     "Play and record games of minesweeper using a graphics device that supports event
handling.  Replay recorded games and save GIF animations of them.  Based on
classic minesweeper as detailed by Crow P. (1997)
<https://minesweepergame.com/math/a-mathematical-introduction-to-the-game-of-minesweeper-1997.pdf>.")
    (license license:expat)))

(define-public r-minerva
  (package
    (name "r-minerva")
    (version "1.5.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minerva" version))
       (sha256
        (base32 "03srkii3rmx40lyr050bnrxil6w0hfv5pr64k339m66ciwyka9ig"))))
    (properties `((upstream-name . "minerva")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://www.r-project.org")
    (synopsis
     "Maximal Information-Based Nonparametric Exploration for Variable Analysis")
    (description
     "Wrapper for minepy implementation of Maximal Information-based Nonparametric
Exploration statistics (MIC and MINE family).  Detailed information of the ANSI
C implementation of minepy can be found at
<http://minepy.readthedocs.io/en/latest>.")
    (license license:gpl3)))

(define-public r-minedfind
  (package
    (name "r-minedfind")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MinEDfind" version))
       (sha256
        (base32 "1y0iv92mp7hf14vrj54qd0yvmjrv52jv3vg25kc5q7pml63njkqa"))))
    (properties `((upstream-name . "MinEDfind")))
    (build-system r-build-system)
    (propagated-inputs (list r-iso r-gridextra r-ggplot2))
    (home-page "https://cran.r-project.org/package=MinEDfind")
    (synopsis "Bayesian Design for Minimum Effective Dosing-Finding Trial")
    (description
     "The nonparametric two-stage Bayesian adaptive design is a novel phase II
clinical trial design for finding the minimum effective dose (@code{MinED}).
This 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.  It is used to
design single-agent trials.")
    (license license:gpl2)))

(define-public r-mined
  (package
    (name "r-mined")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mined" version))
       (sha256
        (base32 "1ilfwm42r2mkcdxphqkz7b4szfr4m9gpfi9qid3625sq3rjk622r"))))
    (properties `((upstream-name . "mined")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=mined")
    (synopsis "Minimum Energy Designs")
    (description
     "This is a method (@code{MinED}) for mining probability distributions using
deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019)
<DOI:10.1080/00401706.2018.1552203>.  The @code{MinED} samples can be used for
approximating the target distribution.  They can be generated from a density
function that is known only up to a proportionality constant and thus, it might
find applications in Bayesian computation.  Moreover, the @code{MinED} samples
are generated with much fewer evaluations of the density function compared to
random sampling-based methods such as MCMC and therefore, this method will be
especially useful when the unnormalized posterior is expensive or time consuming
to evaluate.  This research is supported by a U.S. National Science Foundation
grant DMS-1712642.")
    (license license:lgpl2.1)))

(define-public r-minecitrus
  (package
    (name "r-minecitrus")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mineCitrus" version))
       (sha256
        (base32 "031nfhrsc5rlqls3vyrk18lx0pd4ssdk96cmfr4ifaciz0rgv8h2"))))
    (properties `((upstream-name . "mineCitrus")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=mineCitrus")
    (synopsis
     "Extract and Analyze Median Molecule Intensity from 'citrus' Output")
    (description
     "Citrus is a computational technique developed for the analysis of high
dimensional cytometry data sets.  This package extracts, statistically analyzes,
and visualizes marker expression from citrus data.  This code was used to
generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al.
âIdentification of Enhanced Interferon-Gamma Signaling in Polyarticular
Juvenile Idiopathic Arthritis with Mass Cytometryâ, JCI-Insight.  For more
information on Citrus, please see: Bruggner et al. (2014)
<doi:10.1073/pnas.1408792111>.  To download the citrus package, please see
<https://github.com/nolanlab/citrus>.")
    (license license:gpl2)))

(define-public r-mindonstats
  (package
    (name "r-mindonstats")
    (version "0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MindOnStats" version))
       (sha256
        (base32 "13995v4n0hfb53w02jk81pl7nazkvqwwv87y1sr99jr9ppzc08mz"))))
    (properties `((upstream-name . "MindOnStats")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MindOnStats")
    (synopsis "Data sets included in Utts and Heckard's Mind on Statistics")
    (description
     "66 data sets that were imported using read.table() where appropriate but more
commonly after converting to a csv file for importing via read.csv().")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mind
  (package
    (name "r-mind")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mind" version))
       (sha256
        (base32 "01ncwc1kkfa0gfmrnvl93lvzlcjl6r30a3fs65lq15b132rncgwq"))))
    (properties `((upstream-name . "mind")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-matrix
                             r-mass
                             r-jwileymisc
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=mind")
    (synopsis "Multivariate Model Based Inference for Domains")
    (description
     "Allows users to produce estimates and MSE for multivariate variables using
Linear Mixed Model.  The package follows the approach of Datta, Day and Basawa
(1999) <doi:10.1016/S0378-3758(98)00147-5>.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-minb
  (package
    (name "r-minb")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "minb" version))
       (sha256
        (base32 "1ay1l58b0jhk27sajrvj4p7kkx4hs2va0am9c76pwbng6vg264mh"))))
    (properties `((upstream-name . "minb")))
    (build-system r-build-system)
    (propagated-inputs (list r-pscl r-mass))
    (home-page "https://cran.r-project.org/package=minb")
    (synopsis "Multiple-Inflated Negative Binomial Model")
    (description
     "Count data is prevalent and informative, with widespread application in many
fields such as social psychology, personality, and public health.  Classical
statistical methods for the analysis of count outcomes are commonly variants of
the log-linear model, including Poisson regression and Negative Binomial
regression.  However, a typical problem with count data modeling is inflation,
in the sense that the counts are evidently accumulated on some integers.  Such
an inflation problem could distort the distribution of the observed counts,
further bias estimation and increase error, making the classic methods
infeasible.  Traditional inflated value selection methods based on histogram
inspection are easy to neglect true points and computationally expensive in
addition.  Therefore, we propose a multiple-inflated negative binomial model to
handle count data modeling with multiple inflated values, achieving data-driven
inflated value selection.  The proposed approach provides simultaneous
identification of important regression predictors on the target count response
as well.  More details about the proposed method are described in Li, Y., Wu,
M., Wu, M., & Ma, S. (2023) <@code{arXiv:2309.15585>}.")
    (license license:gpl3)))

(define-public r-mimsy
  (package
    (name "r-mimsy")
    (version "0.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mimsy" version))
       (sha256
        (base32 "1cxr3pcm32cbl3nqblh8s8lx6pn72sgnp63ib0gvcysr8rdx1gza"))))
    (properties `((upstream-name . "mimsy")))
    (build-system r-build-system)
    (propagated-inputs (list r-openxlsx r-magrittr r-lubridate r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/michelleckelly/mimsy")
    (synopsis
     "Calculate MIMS Dissolved Gas Concentrations Without Getting a Headache")
    (description
     "Calculate dissolved gas concentrations from raw MIMS (Membrane Inlet Mass
Spectrometer) signal data.  Use mimsy() on a formatted CSV file to return
dissolved gas concentrations (mg and @code{microMole}) of N2, O2, Ar based on
gas solubility at temperature, pressure, and salinity.  See references Benson
and Krause (1984), Garcia and Gordon (1992), Stull (1947), and Hamme and Emerson
(2004) for more information.  Easily save the output to a nicely-formatted
multi-tab Excel workbook with mimsy.save().  Supports dual-temperature standard
calibration for dual-bath MIMS setups.")
    (license license:expat)))

(define-public r-mimsunit
  (package
    (name "r-mimsunit")
    (version "0.11.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIMSunit" version))
       (sha256
        (base32 "1i0sadm4gh8yynnihhyp0lm37dq7qm7dmkl28jzdb5knls8nhcpz"))))
    (properties `((upstream-name . "MIMSunit")))
    (build-system r-build-system)
    (inputs (list libxml2 openssl))
    (propagated-inputs (list r-xts
                             r-tibble
                             r-stringr
                             r-signal
                             r-shiny
                             r-readr
                             r-rcolorbrewer
                             r-r-utils
                             r-plyr
                             r-magrittr
                             r-lubridate
                             r-ggplot2
                             r-dygraphs
                             r-dplyr
                             r-catools))
    (home-page "https://mhealthgroup.github.io/MIMSunit/")
    (synopsis
     "Algorithm to Compute Monitor Independent Movement Summary Unit (MIMS-Unit)")
    (description
     "The MIMS-unit algorithm is developed to compute Monitor Independent Movement
Summary Unit, a measurement to summarize raw accelerometer data while ensuring
harmonized results across different devices.  It also includes scripts to
reproduce results in the related publication (John, D., Tang.  Q., Albinali, F.
and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).")
    (license license:expat)))

(define-public r-mimisbm
  (package
    (name "r-mimisbm")
    (version "0.0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mimiSBM" version))
       (sha256
        (base32 "1rnpk8wxgx4pncb9axkdwjzh8cp70ykw5zixrsqj60h7drv8snhq"))))
    (properties `((upstream-name . "mimiSBM")))
    (build-system r-build-system)
    (propagated-inputs (list r-blockmodels))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mimiSBM")
    (synopsis "Mixture of Multilayer Integrator Stochastic Block Models")
    (description
     "Our approach uses a mixture of multilayer stochastic block models to group
co-membership matrices with similar information into components and to partition
observations into different clusters.  See De Santiago (2023, ISBN:
978-2-87587-088-9).")
    (license license:gpl3)))

(define-public r-mimir
  (package
    (name "r-mimir")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MiMIR" version))
       (sha256
        (base32 "18z825r866yj9jadhdc5rlp8ykxrfbkdz5bpgpazm8r9w1ypk6hx"))))
    (properties `((upstream-name . "MiMIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-shinywidgets
                             r-shinyjs
                             r-shinyfiles
                             r-shinydashboard
                             r-shinycssloaders
                             r-shiny
                             r-purrr
                             r-proc
                             r-plotly
                             r-matrixstats
                             r-heatmaply
                             r-ggplot2
                             r-fs
                             r-foreach
                             r-dt
                             r-dplyr
                             r-caret))
    (home-page "https://cran.r-project.org/package=MiMIR")
    (synopsis "Metabolomics-Based Models for Imputing Risk")
    (description
     "This package provides an intuitive framework for ad-hoc statistical analysis of
1H-NMR metabolomics by Nightingale Health.  It allows to easily explore new
metabolomics measurements assayed by Nightingale Health, comparing the
distributions with a large Consortium (BBMRI-nl); project previously published
metabolic scores [<doi:10.1016/j.ebiom.2021.103764>,
<doi:10.1161/CIRCGEN.119.002610>, <doi:10.1038/s41467-019-11311-9>,
<doi:10.7554/@code{eLife.63033>}, <doi:10.1161/CIRCULATIONAHA.114.013116>,
<doi:10.1007/s00125-019-05001-w>]; and calibrate the metabolic surrogate values
to a desired dataset.")
    (license license:gpl3)))

(define-public r-mimi
  (package
    (name "r-mimi")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mimi" version))
       (sha256
        (base32 "1ig34qd37my74nmlv3jcfrj7cnd55alxpf2flf2sssk2ws1y56xh"))))
    (properties `((upstream-name . "mimi")))
    (build-system r-build-system)
    (propagated-inputs (list r-softimpute
                             r-rarpack
                             r-glmnet
                             r-foreach
                             r-factominer
                             r-doparallel
                             r-data-table))
    (home-page "https://cran.r-project.org/package=mimi")
    (synopsis "Main Effects and Interactions in Mixed and Incomplete Data")
    (description
     "Generalized low-rank models for mixed and incomplete data frames.  The main
function may be used for dimensionality reduction of imputation of numeric,
binary and count data (simultaneously).  Main effects such as column means,
group effects, or effects of row-column side information (e.g. user/item
attributes in recommendation system) may also be modelled in addition to the
low-rank model.  GeneviÃ¨ve Robin, Olga Klopp, Julie Josse, Ãric Moulines,
Robert Tibshirani (2018) <@code{arXiv:1806.09734>}.")
    (license license:gpl3)))

(define-public r-mimer
  (package
    (name "r-mimer")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIMER" version))
       (sha256
        (base32 "1nqdjgm95dnaxnzy5j28agl7b95hj6hqjyipk5mz44yc2y2chg3z"))))
    (properties `((upstream-name . "MIMER")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-testthat
                             r-stringr
                             r-rlang
                             r-reshape2
                             r-fuzzyjoin
                             r-dplyr
                             r-data-table
                             r-amr))
    (home-page "https://cran.r-project.org/package=MIMER")
    (synopsis "Data Wrangling for Antimicrobial Resistance Studies")
    (description
     "Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC)
dataset, a repository of freely accessible electronic health records.
MIMER(MIMIC-enabled Research) package, offers a suite of data wrangling
functions tailored specifically for preparing the dataset for research purposes,
particularly in antimicrobial resistance(AMR) studies.  It simplifies complex
data manipulation tasks, allowing researchers to focus on their primary
inquiries without being bogged down by wrangling complexities.")
    (license license:gpl3+)))

(define-public r-mimdo
  (package
    (name "r-mimdo")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mimdo" version))
       (sha256
        (base32 "1p0qz2z421pw2yaglg745ccs6yyn5ww4rzspsfikdpr1js7lx4a4"))))
    (properties `((upstream-name . "mimdo")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mimdo")
    (synopsis "Multivariate Imputation by Mahalanobis Distance Optimization")
    (description
     "Imputes missing values of an incomplete data matrix by minimizing the
Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and
Tubo, B.F. (2024) <doi:10.24412/1932-2321-2024-278-115-123>].")
    (license license:gpl3)))

(define-public r-milr
  (package
    (name "r-milr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "milr" version))
       (sha256
        (base32 "03hihr1w5j0mlvi5vm9m5vb9i6nyb6lsji3xmv0chpczabd85mzy"))))
    (properties `((upstream-name . "milr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-piper
                             r-numderiv
                             r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PingYangChen/milr")
    (synopsis "Multiple-Instance Logistic Regression with LASSO Penalty")
    (description
     "The multiple instance data set consists of many independent subjects (called
bags) and each subject is composed of several components (called instances).
The outcomes of such data set are binary or categorical responses, and, we can
only observe the subject-level outcomes.  For example, in manufacturing
processes, a subject is labeled as \"defective\" if at least one of its own
components is defective, and otherwise, is labeled as \"non-defective\".  The milr
package focuses on the predictive model for the multiple instance data set with
binary outcomes and performs the maximum likelihood estimation with the
Expectation-Maximization algorithm under the framework of logistic regression.
Moreover, the LASSO penalty is attached to the likelihood function for
simultaneous parameter estimation and variable selection.")
    (license license:expat)))

(define-public r-milorgwas
  (package
    (name "r-milorgwas")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "milorGWAS" version))
       (sha256
        (base32 "052bavj7wmkjcnm6vjswpg1zkawqjn5yy3z6zi4gklxci53a1z1i"))))
    (properties `((upstream-name . "milorGWAS")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-rcppeigen r-rcpp r-gaston))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=milorGWAS")
    (synopsis
     "Mixed Logistic Regression for Genome-Wide Analysis Studies (GWAS)")
    (description
     "Fast approximate methods for mixed logistic regression in genome-wide analysis
studies (GWAS).  Two computationnally efficient methods are proposed for
obtaining effect size estimates (beta) in Mixed Logistic Regression in GWAS: the
Approximate Maximum Likelihood Estimate (AMLE), and the Offset method.  The wald
test obtained with AMLE is identical to the score test.  Data can be genotype
matrices in plink format, or dosage (VCF files).  The methods are described in
details in Milet et al (2020) <doi:10.1101/2020.01.17.910109>.")
    (license license:gpl3)))

(define-public r-milineage
  (package
    (name "r-milineage")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miLineage" version))
       (sha256
        (base32 "0660vk44ywmj0q2cw7pa2z1r039ncw693dnz7zlgc0ipwak3b0ry"))))
    (properties `((upstream-name . "miLineage")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-geepack r-data-table))
    (home-page "https://cran.r-project.org/package=miLineage")
    (synopsis "Association Tests for Microbial Lineages on a Taxonomic Tree")
    (description
     "This package provides a variety of association tests for microbiome data
analysis including Quasi-Conditional Association Tests (QCAT) described in Tang
Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated
Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen
G. (2017, submitted).")
    (license license:gpl2+)))

(define-public r-mildsvm
  (package
    (name "r-mildsvm")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mildsvm" version))
       (sha256
        (base32 "02n93ck8458xliqwp226v5b7691a9jx5i7b1q9n0nqc74xkm8h1i"))))
    (properties `((upstream-name . "mildsvm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-proc
                             r-pillar
                             r-mvtnorm
                             r-magrittr
                             r-kernlab
                             r-e1071
                             r-dplyr))
    (home-page "https://github.com/skent259/mildsvm")
    (synopsis "Multiple-Instance Learning with Support Vector Machines")
    (description
     "Weakly supervised (WS), multiple instance (MI) data lives in numerous
interesting applications such as drug discovery, object detection, and tumor
prediction on whole slide images.  The mildsvm package provides an easy way to
learn from this data by training Support Vector Machine (SVM)-based classifiers.
 It also contains helpful functions for building and printing multiple instance
data frames.  The core methods from mildsvm come from the following references:
Kent and Yu (2022) <@code{arXiv:2206.14704>}; Xiao, Liu, and Hao (2018)
<doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012)
<https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>;
Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews et al.
(2003)
<https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>.
 Many functions use the Gurobi optimization back-end to improve the optimization
problem speed; the gurobi R package and associated software can be downloaded
from <https://www.gurobi.com> after obtaining a license.")
    (license license:expat)))

(define-public r-milag
  (package
    (name "r-milag")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miLAG" version))
       (sha256
        (base32 "16bkab769ip0xcyjk4mfxb9p4481819jyarvsqd4yqkm01xbkzr8"))))
    (properties `((upstream-name . "miLAG")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-nlsmicrobio r-minpack-lm r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=miLAG")
    (synopsis
     "Calculates Microbial Lag Duration (on the Population Level) from Provided Growth Curve Data")
    (description
     "Microbial growth is often measured by growth curves i.e.  a table of population
sizes and times of measurements.  This package allows to use such growth curve
data to determine the duration of \"microbial lag phase\" i.e.  the time needed
for microbes to restart divisions.  It implements the most commonly used methods
to calculate the lag duration, these methods are discussed and described in
Opalek et.al.  2022.  Citation: \"How to determine microbial lag phase
duration?\", M. Opalek, B. Smug, D. Wloch-Salamon (2022)
<doi:10.1101/2022.11.16.516631>.")
    (license license:gpl3)))

(define-public r-mikropml
  (package
    (name "r-mikropml")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mikropml" version))
       (sha256
        (base32 "13dlzrwyl4sqklypva35qq5194yc95fcpsyclvisdrky040xgxsy"))))
    (properties `((upstream-name . "mikropml")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-rpart
                             r-rlang
                             r-randomforest
                             r-mlmetrics
                             r-kernlab
                             r-glmnet
                             r-e1071
                             r-dplyr
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://www.schlosslab.org/mikropml/")
    (synopsis
     "User-Friendly R Package for Supervised Machine Learning Pipelines")
    (description
     "An interface to build machine learning models for classification and regression
problems.  mikropml implements the ML pipeline described by TopÃ§uoÄlu et al.
(2020) <doi:10.1128/@code{mBio.00434-20>} with reasonable default options for
data preprocessing, hyperparameter tuning, cross-validation, testing, model
evaluation, and interpretation steps.  See the website
<https://www.schlosslab.org/mikropml/> for more information, documentation, and
examples.")
    (license license:expat)))

(define-public r-miivsem
  (package
    (name "r-miivsem")
    (version "0.5.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIIVsem" version))
       (sha256
        (base32 "1qb1xrxhgj6b0l24spbp80f0rm8v41bqsdachqjhsacq2lgga259"))))
    (properties `((upstream-name . "MIIVsem")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-matrix r-lavaan r-car r-boot))
    (home-page "https://github.com/zackfisher/MIIVsem")
    (synopsis
     "Model Implied Instrumental Variable (MIIV) Estimation of Structural Equation Models")
    (description
     "This package provides functions for estimating structural equation models using
instrumental variables.")
    (license license:gpl2)))

(define-public r-miivefa
  (package
    (name "r-miivefa")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIIVefa" version))
       (sha256
        (base32 "1727xhnc1hxphid54nzzd2iaz1x3ivphidxkb47r3ck0njd43rwk"))))
    (properties `((upstream-name . "MIIVefa")))
    (build-system r-build-system)
    (propagated-inputs (list r-miivsem))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lluo0/MIIVefa/")
    (synopsis
     "Exploratory Factor Analysis Using Model Implied Instrumental Variables")
    (description
     "Data-driven approach for Exploratory Factor Analysis (EFA) that uses Model
Implied Instrumental Variables (MIIVs).  The method starts with a one factor
model and arrives at a suggested model with enhanced interpretability that
allows cross-loadings and correlated errors.")
    (license license:expat)))

(define-public r-miipw
  (package
    (name "r-miipw")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIIPW" version))
       (sha256
        (base32 "043zdadny5rb1vlg1lnjaxjdjqmb755lgxha4zcdzyv9sl83ca8y"))))
    (properties `((upstream-name . "MIIPW")))
    (build-system r-build-system)
    (propagated-inputs (list r-mice r-matrix r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MIIPW")
    (synopsis "IPW and Mean Score Methods for Time-Course Missing Data")
    (description
     "This package contains functions for data analysis of Repeated measurement using
GEE. Data may contain missing value in response and covariates.  For parameter
estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability
Weighted method combining with Multiple Imputation are used when there is
missing value in covariates/response.  Reference for mean score method, inverse
probability weighted method is Wang et
al(2007)<doi:10.1093/biostatistics/kxl024>.")
    (license license:gpl3)))

(define-public r-miic
  (package
    (name "r-miic")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miic" version))
       (sha256
        (base32 "1862sdlp4bk4ih0522yi4mxkccylwnvgm272dvm8iqqd9cnzmnkd"))))
    (properties `((upstream-name . "miic")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rcpp r-ppcor))
    (home-page "https://github.com/miicTeam/miic_R_package")
    (synopsis
     "Learning Causal or Non-Causal Graphical Models Using Information Theory")
    (description
     "Multivariate Information-based Inductive Causation, better known by its acronym
MIIC, is a causal discovery method, based on information theory principles,
which learns a large class of causal or non-causal graphical models from purely
observational data, while including the effects of unobserved latent variables.
Starting from a complete graph, the method iteratively removes dispensable
edges, by uncovering significant information contributions from indirect paths,
and assesses edge-specific confidences from randomization of available data.
The remaining edges are then oriented based on the signature of causality in
observational data.  The recent more interpretable MIIC extension (@code{iMIIC})
further distinguishes genuine causes from putative and latent causal effects,
while scaling to very large datasets (hundreds of thousands of samples).  Since
the version 2.0, MIIC also includes a temporal mode (@code{tMIIC}) to learn
temporal causal graphs from stationary time series data.  MIIC has been applied
to a wide range of biological and biomedical data, such as single cell gene
expression data, genomic alterations in tumors, live-cell time-lapse imaging
data (@code{CausalXtract}), as well as medical records of patients.  MIIC brings
unique insights based on causal interpretation and could be used in a broad
range of other data science domains (technology, climatology, economy, ...).
For more information, you can refer to: Simon et al., @code{eLife} 2024,
<doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al., @code{iScience} 2024,
<doi:10.1016/j.isci.2024.109736>, Cabeli et al., @code{NeurIPS} 2021,
<https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al., Comput.  Biol.
2020, <doi:10.1371/journal.pcbi.1007866>, Li et al., @code{NeurIPS} 2019,
<https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>,
Verny et al., P@code{LoS} Comput.  Biol.  2017,
<doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015,
<https://auai.org/uai2015/proceedings/papers/293.pdf>.  Changes from the
previous 1.5.3 release on CRAN are available at
<https://github.com/@code{miicTeam/miic_R_package/blob/master/NEWS.md>}.")
    (license license:gpl2+)))

(define-public r-migui
  (package
    (name "r-migui")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "migui" version))
       (sha256
        (base32 "1y012la9vmchnlcigcciq878w9hlw8zgzp76b3h9b6fwhaa872pm"))))
    (properties `((upstream-name . "migui")))
    (build-system r-build-system)
    (propagated-inputs (list r-mi r-gwidgets2 r-arm))
    (home-page "https://cran.r-project.org/package=migui")
    (synopsis "Graphical User Interface to the 'mi' Package")
    (description
     "This GUI for the mi package walks the user through the steps of multiple
imputation and the analysis of completed data.")
    (license license:gpl2+)))

(define-public r-migrationdetectr
  (package
    (name "r-migrationdetectr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MigrationDetectR" version))
       (sha256
        (base32 "0qq5xac4j8bm1705rmjvbcxhdhrq0pfqzrlb9vgbnsfm4l0iy84w"))))
    (properties `((upstream-name . "MigrationDetectR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-lubridate
                             r-lifecycle
                             r-dplyr
                             r-assertthat))
    (home-page "https://cran.r-project.org/package=MigrationDetectR")
    (synopsis "Segment-Based Migration Detection Algorithm")
    (description
     "Detection of migration events and segments of continuous residence based on
irregular time series of location data as published in Chi et al. (2020)
<doi:10.1371/journal.pone.0239408>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-migration-indices
  (package
    (name "r-migration-indices")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "migration.indices" version))
       (sha256
        (base32 "198lxv6s1wc6j10ygbc4l2irmmv5448f9av9zsybnpijballx349"))))
    (properties `((upstream-name . "migration.indices")))
    (build-system r-build-system)
    (propagated-inputs (list r-calibrate))
    (home-page "https://github.com/daroczig/migration.indices")
    (synopsis "Migration Indices")
    (description
     "Calculate various indices, like Crude Migration Rate, different Gini indices or
the Coefficient of Variation among others, to show the (un)equality of
migration.")
    (license license:agpl3)))

(define-public r-migrate
  (package
    (name "r-migrate")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "migrate" version))
       (sha256
        (base32 "04whq58zm83s9y66bzb390q6cwj006gv4abv0b63fx1rkvfh9drw"))))
    (properties `((upstream-name . "migrate")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-glue
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ketchbrookanalytics/migrate")
    (synopsis "Create Credit State Migration (Transition) Matrices")
    (description
     "This package provides tools to help convert credit risk data at two timepoints
into traditional credit state migration (aka, \"transition\") matrices.  At a
higher level, migrate is intended to help an analyst understand how risk moved
in their credit portfolio over a time interval.  References to this methodology
include: 1.  Schuermann, T. (2008) <doi:10.1002/9780470061596.risk0409>.  2.
Perederiy, V. (2017) <doi:10.48550/@code{arXiv.1708.00062>}.")
    (license license:expat)))

(define-public r-migraph
  (package
    (name "r-migraph")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "migraph" version))
       (sha256
        (base32 "1iz2xnf8r9w0swqpjwgaais85qlk3l1n7r5qwg5pxjmv2h1bwj7k"))))
    (properties `((upstream-name . "migraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr
                             r-manynet
                             r-ggplot2
                             r-generics
                             r-future
                             r-furrr
                             r-dplyr))
    (home-page "https://stocnet.github.io/migraph/")
    (synopsis
     "Univariate and Multivariate Tests for Multimodal and Other Networks")
    (description
     "This package provides a set of tools for testing networks.  It includes
functions for univariate and multivariate conditional uniform graph and
quadratic assignment procedure testing, and network regression.  The package is
a complement to Multimodal Political Networks (2021, ISBN:9781108985000), and
includes various datasets used in the book.  Built on the manynet package, all
functions operate with matrices, edge lists, and igraph', network', and
tidygraph objects, and on one-mode and two-mode (bipartite) networks.")
    (license license:expat)))

(define-public r-migest
  (package
    (name "r-migest")
    (version "2.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "migest" version))
       (sha256
        (base32 "0gksf8f1arpzxmcid1vskkf4s2r6k4n0ss85cag9429lmjpix5h4"))))
    (properties `((upstream-name . "migest")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-mipfp
                             r-migration-indices
                             r-matrixstats
                             r-magrittr
                             r-forcats
                             r-dplyr
                             r-circlize))
    (home-page "http://guyabel.github.io/migest/")
    (synopsis "Methods for the Indirect Estimation of Bilateral Migration")
    (description
     "This package provides tools for estimating, measuring and working with migration
data.")
    (license license:gpl3)))

(define-public r-migconnectivity
  (package
    (name "r-migconnectivity")
    (version "0.4.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MigConnectivity" version))
       (sha256
        (base32 "08bzis7rkczymvr2bqiih59yn94l0fjhkn0p9lpv875pwbvhmw1j"))))
    (properties `((upstream-name . "MigConnectivity")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-vgam
                             r-terra
                             r-shape
                             r-sf
                             r-rmark
                             r-r2jags
                             r-ncf
                             r-mass
                             r-gplots
                             r-geodist
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SMBC-NZP/MigConnectivity")
    (synopsis "Estimate Migratory Connectivity for Migratory Animals")
    (description
     "Allows the user to estimate transition probabilities for migratory animals
between any two phases of the annual cycle, using a variety of different data
types.  Also quantifies the strength of migratory connectivity (MC), a
standardized metric to quantify the extent to which populations co-occur between
two phases of the annual cycle.  Includes functions to estimate MC and the more
traditional metric of migratory connectivity strength (Mantel correlation)
incorporating uncertainty from multiple sources of sampling error.  For
cross-species comparisons, methods are provided to estimate differences in
migratory connectivity strength, incorporating uncertainty.  See Cohen et al.
(2018) <doi:10.1111/2041-210X.12916>, Cohen et al. (2019)
<doi:10.1111/ecog.03974>, and Roberts et al. (2023) <doi:10.1002/eap.2788> for
details on some of these methods.")
    (license license:gpl3+)))

(define-public r-mig
  (package
    (name "r-mig")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mig" version))
       (sha256
        (base32 "1l7p2bbcz888s3m339p63i6490savjqvv5g4awznzpfbllp0zi4i"))))
    (properties `((upstream-name . "mig")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncatednormal r-statmod r-rcpparmadillo
                             r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mig")
    (synopsis "Multivariate Inverse Gaussian Distribution")
    (description
     "This package provides utilities for estimation for the multivariate inverse
Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including
random vector generation and explicit estimators of the location vector and
scale matrix.  The package implements kernel density estimators discussed in
Belzile, Desgagnes, Genest and Ouimet (2024)
<doi:10.48550/@code{arXiv.2209.04757>} for smoothing multivariate data on
half-spaces.")
    (license license:expat)))

(define-public r-mifa
  (package
    (name "r-mifa")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mifa" version))
       (sha256
        (base32 "1rj3ba746f8759gird0vpvyb4fl0z2ihccpiv3qkr3sc64wgplln"))))
    (properties `((upstream-name . "mifa")))
    (build-system r-build-system)
    (propagated-inputs (list r-mice r-dplyr r-checkmate))
    (home-page "https://github.com/teebusch/mifa")
    (synopsis "Multiple Imputation for Exploratory Factor Analysis")
    (description
     "Impute the covariance matrix of incomplete data so that factor analysis can be
performed.  Imputations are made using multiple imputation by Multivariate
Imputation with Chained Equations (MICE) and combined with Rubin's rules.
Parametric Fieller confidence intervals and nonparametric bootstrap confidence
intervals can be obtained for the variance explained by different numbers of
principal components.  The method is described in Nassiri et al. (2018)
<doi:10.3758/s13428-017-1013-4>.")
    (license license:expat)))

(define-public r-miesmuschel
  (package
    (name "r-miesmuschel")
    (version "0.0.4-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miesmuschel" version))
       (sha256
        (base32 "0pqp9rpk0jmbkfxi5vq8ij7s3y9ykk9kn0bsz9y4qw8z8j8grmv1"))))
    (properties `((upstream-name . "miesmuschel")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-paradox
                             r-mlr3misc
                             r-matrixstats
                             r-lgr
                             r-data-table
                             r-checkmate
                             r-bbotk))
    (home-page "https://github.com/mlr-org/miesmuschel")
    (synopsis "Mixed Integer Evolution Strategies")
    (description
     "Evolutionary black box optimization algorithms building on the bbotk package.
miesmuschel offers both ready-to-use optimization algorithms, as well as their
fundamental building blocks that can be used to manually construct specialized
optimization loops.  The Mixed Integer Evolution Strategies as described by Li
et al. (2013) <doi:10.1162/EVCO_a_00059> can be implemented, as well as the
multi-objective optimization algorithms NSGA-II by Deb, Pratap, Agarwal, and
Meyarivan (2002) <doi:10.1109/4235.996017>.")
    (license license:expat)))

(define-public r-midrangemcp
  (package
    (name "r-midrangemcp")
    (version "3.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midrangeMCP" version))
       (sha256
        (base32 "1yfwyglyxxk4i914m8n148cjydlcg7r16zl8acb1w8h963svirk9"))))
    (properties `((upstream-name . "midrangeMCP")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-writexl r-smr))
    (home-page "https://bendeivide.github.io/midrangeMCP/")
    (synopsis
     "Multiples Comparisons Procedures Based on Studentized Midrange and Range Distributions")
    (description
     "Apply tests of multiple comparisons based on studentized midrange and range
distributions.  The tests are: Tukey Midrange ('TM test), Student-Newman-Keuls
Midrange ('SNKM test), Means Grouping Midrange ('MGM test) and Means Grouping
Range ('MGR test).  The first two tests were published by Batista and Ferreira
(2020) <doi:10.1590/1413-7054202044008020>.  The last two are being published.")
    (license license:gpl2+)))

(define-public r-midoc
  (package
    (name "r-midoc")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midoc" version))
       (sha256
        (base32 "17ycy3wj02a88l0wj1bhpgjd45j3mn4kijxj8lz2fyzg156yn1kw"))))
    (properties `((upstream-name . "midoc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown
                             r-rlang
                             r-mice
                             r-mfp2
                             r-lifecycle
                             r-glue
                             r-dagitty
                             r-blorr
                             r-arm))
    (native-inputs (list r-knitr))
    (home-page "https://elliecurnow.github.io/midoc/")
    (synopsis "Decision-Making System for Multiple Imputation")
    (description
     "This package provides a guidance system for analysis with missing data.  It
incorporates expert, up-to-date methodology to help researchers choose the most
appropriate analysis approach when some data are missing.  You provide the
available data and the assumed causal structure, including the likely causes of
missing data.  midoc will advise which analysis approaches can be used, and how
best to perform them.  midoc follows the framework for the treatment and
reporting of missing data in observational studies (TARMOS).  Lee et al (2021).
<doi:10.1016/j.jclinepi.2021.01.008>.")
    (license license:expat)))

(define-public r-midn
  (package
    (name "r-midn")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIDN" version))
       (sha256
        (base32 "08i9zfmaywi5imp36ahjpirlkl55k01x543nz95i089w3sfrsz0g"))))
    (properties `((upstream-name . "MIDN")))
    (build-system r-build-system)
    (propagated-inputs (list r-biasedurn))
    (home-page "https://cran.r-project.org/package=MIDN")
    (synopsis
     "Nearly Exact Sample Size Calculation for Exact Powerful Nonrandomized Tests for Differences Between Binomial Proportions")
    (description
     "Implementation of the mid-n algorithms presented in Wellek S (2015)
<DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact sample
size calculation for superiority trials with binary outcome.")
    (license license:cc0)))

(define-public r-midi
  (package
    (name "r-midi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midi" version))
       (sha256
        (base32 "06dc0lazi7pkdm66isaqa9igfrl8dq4klpg06aggmilgay09fi5s"))))
    (properties `((upstream-name . "midi")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-rlang
                             r-r6
                             r-purrr
                             r-plotly
                             r-ggplot2
                             r-cli))
    (home-page "https://github.com/lmjl-alea/midi")
    (synopsis "Microstructure Information from Diffusion Imaging")
    (description
     "An implementation of a taxonomy of models of restricted diffusion in biological
tissues parametrized by the tissue geometry (axis, diameter, density, etc.).
This is primarily used in the context of diffusion magnetic resonance (MR)
imaging to model the MR signal attenuation in the presence of diffusion
gradients.  The goal is to provide tools to simulate the MR signal attenuation
predicted by these models under different experimental conditions.  The package
feeds a companion shiny app available at
<https://midi-pastrami.apps.math.cnrs.fr> that serves as a graphical interface
to the models and tools provided by the package.  Models currently available are
the ones in Neuman (1974) <doi:10.1063/1.1680931>, Van Gelderen et al. (1994)
<doi:10.1006/jmrb.1994.1038>, Stanisz et al. (1997)
<doi:10.1002/mrm.1910370115>, Soderman & Jonsson (1995)
<doi:10.1006/jmra.1995.0014> and Callaghan (1995) <doi:10.1006/jmra.1995.1055>.")
    (license license:expat)))

(define-public r-midfieldr
  (package
    (name "r-midfieldr")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midfieldr" version))
       (sha256
        (base32 "18zh3nm8k13hzaa0rih4f76q7zj16qww2aaamifhhfimgpn9iib5"))))
    (properties `((upstream-name . "midfieldr")))
    (build-system r-build-system)
    (propagated-inputs (list r-wrapr r-data-table r-checkmate))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://midfieldr.github.io/midfieldr/")
    (synopsis "Tools and Methods for Working with MIDFIELD Data in 'R'")
    (description
     "This package provides tools and demonstrates methods for working with individual
undergraduate student-level records (registrar's data) in R'.  Tools include
filters for program codes, data sufficiency, and timely completion.  Methods
include gathering blocs of records, computing quantitative metrics such as
graduation rate, and creating charts to visualize comparisons.  midfieldr
interacts with practice data provided in midfielddata', an R data package
available at <https://midfieldr.github.io/midfielddata/>.  midfieldr also
interacts with the full MIDFIELD database for users who have access.  This work
is supported by the US National Science Foundation through grant numbers 1545667
and 2142087.")
    (license license:expat)))

(define-public r-midastouch
  (package
    (name "r-midastouch")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midastouch" version))
       (sha256
        (base32 "1pjzcf0hjfhr5p0la8pz1njw7bhfrcrzpqfsdqk2z5c6dbh4awzq"))))
    (properties `((upstream-name . "midastouch")))
    (build-system r-build-system)
    (home-page
     "https://www.uni-bamberg.de/fileadmin/uni/fakultaeten/sowi_lehrstuehle/statistik/Personen/Dateien_Florian/properPMM.pdf")
    (synopsis "Multiple Imputation by Distance Aided Donor Selection")
    (description
     "This package contains the function mice.impute.midastouch().  Technically this
function is to be run from within the mice package (van Buuren et al.  2011),
type ??mice.  It substitutes the method pmm within mice by midastouch'.  The
authors have shown that midastouch is superior to default pmm'.  Many ideas are
based on Siddique / Belin 2008's MIDAS.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-midasr
  (package
    (name "r-midasr")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midasr" version))
       (sha256
        (base32 "0mzyxf8q2wmbsiczkhhgigy7jmi8zr4w4a83f97va35vfhxi217k"))))
    (properties `((upstream-name . "midasr")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-texreg
                             r-sandwich
                             r-quantreg
                             r-optimx
                             r-numderiv
                             r-matrix
                             r-mass
                             r-formula
                             r-forecast))
    (home-page "http://mpiktas.github.io/midasr/")
    (synopsis "Mixed Data Sampling Regression")
    (description
     "This package provides methods and tools for mixed frequency time series data
analysis.  Allows estimation, model selection and forecasting for MIDAS
regressions.")
    (license (list license:gpl2
                   (license:fsdg-compatible "MIT + file LICENCE")))))

(define-public r-midasml
  (package
    (name "r-midasml")
    (version "0.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midasml" version))
       (sha256
        (base32 "0jhrifvqcis2lj59vybgkzgmdmxwfqmsbxijwcz8hsprshr69xl9"))))
    (properties `((upstream-name . "midasml")))
    (build-system r-build-system)
    (propagated-inputs (list r-snow
                             r-randtoolbox
                             r-matrix
                             r-lubridate
                             r-foreach
                             r-dorng
                             r-doparallel))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=midasml")
    (synopsis
     "Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data")
    (description
     "The midasml package implements estimation and prediction methods for
high-dimensional mixed-frequency (MIDAS) time-series and panel data regression
models.  The regularized MIDAS models are estimated using orthogonal (e.g.
Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator.  For more
information on the midasml approach see Babii, Ghysels, and Striaukas (2021,
JBES forthcoming) <doi:10.1080/07350015.2021.1899933>.  The package is equipped
with the fast implementation of the sg-LASSO estimator by means of proximal
block coordinate descent.  High-dimensional mixed frequency time-series data can
also be easily manipulated with functions provided in the package.")
    (license license:gpl2+)))

(define-public r-midas2
  (package
    (name "r-midas2")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midas2" version))
       (sha256
        (base32 "0in4z1ijj9ymr27c9l5a9jripzb9k0nxk231bmk3sk3b3s7755vb"))))
    (properties `((upstream-name . "midas2")))
    (build-system r-build-system)
    (propagated-inputs (list r-r2jags r-mcmcpack r-coda))
    (home-page "https://cran.r-project.org/package=midas2")
    (synopsis
     "Bayesian Platform Design with Subgroup Efficacy Exploration(MIDAS-2)")
    (description
     "The rapid screening of effective and optimal therapies from large numbers of
candidate combinations, as well as exploring subgroup efficacy, remains
challenging, which necessitates innovative, integrated, and efficient trial
designs(Yuan, Y., et al. (2016) <doi:10.1002/sim.6971>).  MIDAS-2 package
enables quick and continuous screening of promising combination strategies and
exploration of their subgroup effects within a unified platform design
framework.  We used a regression model to characterize the efficacy pattern in
subgroups.  Information borrowing was applied through Bayesian hierarchical
model to improve trial efficiency considering the limited sample size in
subgroups(Cunanan, K. M., et al. (2019) <doi:10.1177/1740774518812779>).
MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to
accelerate immunotherapy development in an efficient, accurate, and integrated
fashion(Wathen, J. K., & Thall, P. F. (2017) <doi: 10.1177/1740774517692302>).")
    (license license:gpl3)))

(define-public r-midas
  (package
    (name "r-midas")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "midas" version))
       (sha256
        (base32 "1alqxk23651jnkg0xn4rpvk71z5rhvx3y0wjrgkw2qw95s7jjn72"))))
    (properties `((upstream-name . "midas")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-shiny))
    (home-page "https://cran.r-project.org/package=midas")
    (synopsis "Turn HTML 'Shiny'")
    (description
     "This package contains functions for converting existing HTML/@code{JavaScript}
source into equivalent shiny functions.  Bootstraps the process of making new
shiny functions by allowing us to turn HTML snippets directly into R functions.")
    (license license:gpl3)))

(define-public r-mida
  (package
    (name "r-mida")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MiDA" version))
       (sha256
        (base32 "144gxsxqljzskxsw5k0y69ix0pxlfvyyznxkjpf2ng4l47pg11z5"))))
    (properties `((upstream-name . "MiDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-sqn
                             r-proc
                             r-preprocesscore
                             r-limma
                             r-genefilter
                             r-gbm
                             r-caret))
    (home-page "https://cran.r-project.org/package=MiDA")
    (synopsis "Microarray Data Analysis")
    (description
     "Set of functions designed to simplify transcriptome analysis and identification
of marker molecules using microarrays data.  The package includes a set of
functions that allows performing full pipeline of analysis including data
normalization, summarisation, binary classification, FDR (False Discovery Rate)
multiple comparison and the definition of potential biological markers.")
    (license license:gpl3)))

(define-public r-micss
  (package
    (name "r-micss")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micss" version))
       (sha256
        (base32 "094h7jf38k8mijkr546s5cg6nm0s86qd81wizgjxxppf78k36sxf"))))
    (properties `((upstream-name . "micss")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://cran.r-project.org/package=micss")
    (synopsis "Modified Iterative Cumulative Sum of Squares Algorithm")
    (description
     "Companion package of Carrion-i-Silvestre & SansÃ³ (2023): \"Generalized Extreme
Value Approximation to the CUMSUMQ Test for Constant Unconditional Variance in
Heavy-Tailed Time Series\".  It implements the Modified Iterative Cumulative Sum
of Squares Algorithm, which is an extension of the Iterative Cumulative Sum of
Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in
the unconditional variance of a time series controlling for the tail index of
the underlying distribution.  The fourth order moment is estimated
non-parametrically to avoid the size problems when the innovations are
non-Gaussian (see, SansÃ³ et al., 2004).  Critical values and p-values are
generated using a Generalized Extreme Value distribution approach.  References
Carrion-i-Silvestre J.J & SansÃ³ A (2023)
<https://www.ub.edu/irea/working_papers/2023/202309.pdf>.  Inclan C & Tiao G.C
(1994) <doi:10.1080/01621459.1994.10476824>, SansÃ³ A & AragÃ³ V &
Carrion-i-Silvestre J.L (2004)
<https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.")
    (license license:gpl2)))

(define-public r-micsr
  (package
    (name "r-micsr")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micsr" version))
       (sha256
        (base32 "1rjny19h7h2zj2c186dws557h4yy0pcsr14dsbg25nmxplsbz0ml"))))
    (properties `((upstream-name . "micsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-sandwich
                             r-rlang
                             r-rdpack
                             r-purrr
                             r-magrittr
                             r-knitr
                             r-ggplot2
                             r-generics
                             r-formula
                             r-dplyr))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://www.r-project.org")
    (synopsis "Microeconometrics with R")
    (description
     "Functions, data sets and examples for the book: Yves Croissant (2024)
\"Microeconometrics with R\", Chapman and Hall/CRC The R Series.  The package
includes a set of estimators for models used in microeconometrics, especially
for count data and limited dependent variables.  Test functions include score
test, Hausman test, Vuong test, Sargan test and conditional moment test.  A
small subset of the data set used in the book is also included.")
    (license license:gpl2+)))

(define-public r-micsplines
  (package
    (name "r-micsplines")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MICsplines" version))
       (sha256
        (base32 "1ig3329akcaq6lk17nnid64qgz1i8spy11f1gawnbmp2x6z6y8wc"))))
    (properties `((upstream-name . "MICsplines")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MICsplines")
    (synopsis
     "The Computing of Monotonic Spline Bases and Constrained Least-Squares Estimates")
    (description
     "Providing C implementation for the computing of monotonic spline bases,
including M-splines, I-splines, and C-splines, denoted by MIC splines.  The
definitions of the spline bases are described in Meyer (2008) <doi:
10.1214/08-AOAS167>.  The package also provides the computing of constrained
least-squares estimates when a subset of or all of the regression coefficients
are constrained to be non-negative.")
    (license license:gpl2)))

(define-public r-micsim
  (package
    (name "r-micsim")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MicSim" version))
       (sha256
        (base32 "1l9dd3ina9zasnxvkqn8rbrcvnp8lwfqnzxm516adg9x0ng2bimg"))))
    (properties `((upstream-name . "MicSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall r-rlecuyer))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MicSim")
    (synopsis "Performing Continuous-Time Microsimulation")
    (description
     "This toolkit allows performing continuous-time microsimulation for a wide range
of life science (demography, social sciences, epidemiology) applications.
Individual life-courses are specified by a continuous-time multi-state model as
described in Zinn (2014) <doi:10.34196/IJM.00105>.")
    (license license:gpl2)))

(define-public r-microsynth
  (package
    (name "r-microsynth")
    (version "2.0.44")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microsynth" version))
       (sha256
        (base32 "1a8xh4aqv0177p9vp05i6c881k4pkyyha7izbq3j3ssyb9id7yy6"))))
    (properties `((upstream-name . "microsynth")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-pracma r-kernlab))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=microsynth")
    (synopsis "Synthetic Control Methods with Micro- And Meso-Level Data")
    (description
     "This package provides a generalization of the Synth package that is designed for
data at a more granular level (e.g., micro-level).  Provides functions to
construct weights (including propensity score-type weights) and run analyses for
synthetic control methods with micro- and meso-level data; see Robbins,
Saunders, and Kilmer (2017) <doi:10.1080/01621459.2016.1213634> and Robbins and
Davenport (2021) <doi:10.18637/jss.v097.i02>.")
    (license license:gpl3)))

(define-public r-microsoft365r
  (package
    (name "r-microsoft365r")
    (version "2.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Microsoft365R" version))
       (sha256
        (base32 "0vcp8nhphl9yp4rp53nvkm82zqmn1jsc9m6agxmmsqby8795p7h1"))))
    (properties `((upstream-name . "Microsoft365R")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-r6
                             r-mime
                             r-jsonlite
                             r-httr
                             r-curl
                             r-azuregraph
                             r-azureauth))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Microsoft365R")
    (synopsis "Interface to the 'Microsoft 365' Suite of Cloud Services")
    (description
     "An interface to the Microsoft 365 (formerly known as Office 365') suite of cloud
services, building on the framework supplied by the @code{AzureGraph} package.
Enables access from R to data stored in Teams', @code{SharePoint} Online and
@code{OneDrive}', including the ability to list drive folder contents, upload
and download files, send messages, and retrieve data lists.  Also provides a
full-featured Outlook email client, with the ability to send emails and manage
emails and mail folders.")
    (license license:expat)))

(define-public r-microsimulation
  (package
    (name "r-microsimulation")
    (version "1.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microsimulation" version))
       (sha256
        (base32 "0fc3ppmj7rl5dnkrvky8d1lj7w2rp7rv0ahmlwd1r5wl4qhh6lmp"))))
    (properties `((upstream-name . "microsimulation")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpparmadillo r-rcpp r-ascii))
    (home-page "https://github.com/mclements/microsimulation")
    (synopsis
     "Discrete Event Simulation in R and C++, with Tools for Cost-Effectiveness Analysis")
    (description
     "Discrete event simulation using both R and C++ (Karlsson et al 2016;
<doi:10.1109/@code{eScience.2016.7870915>}).  The C++ code is adapted from the
SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for
event-oriented simulation.  The code includes a @code{SummaryReport} class for
reporting events and costs by age and other covariates.  The C++ code is
available as a static library for linking to other packages.  A priority queue
implementation is given in C++ together with an S3 closure and a reference class
implementation.  Finally, some tools are provided for cost-effectiveness
analysis.")
    (license license:gpl3+)))

(define-public r-microseq
  (package
    (name "r-microseq")
    (version "2.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microseq" version))
       (sha256
        (base32 "1xa8r908jic06kv1qjmdidh3zzslbimjyhsy6v27xqzrh45svq4c"))))
    (properties `((upstream-name . "microseq")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-rcpp
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=microseq")
    (synopsis "Basic Biological Sequence Handling")
    (description
     "Basic functions for microbial sequence data analysis.  The idea is to use
generic R data structures as much as possible, making R data wrangling possible
also for sequence data.")
    (license license:gpl2)))

(define-public r-microsec
  (package
    (name "r-microsec")
    (version "2.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MicroSEC" version))
       (sha256
        (base32 "161m2l9ar8amfbcdj72gmlaajvbycj69g5rwfnml1xkw5rh6ywnd"))))
    (properties `((upstream-name . "MicroSEC")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rsamtools
                             r-genomeinfodb
                             r-dplyr
                             r-biostrings
                             r-biocgenerics))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MANO-B/MicroSEC/")
    (synopsis
     "Sequence Error Filter for Formalin-Fixed and Paraffin-Embedded Samples")
    (description
     "Clinical sequencing of tumor is usually performed on formalin-fixed and
paraffin-embedded samples and have many sequencing errors.  We found that the
majority of these errors are detected in chimeric read caused by single-strand
DNA with micro-homology.  Our filtering pipeline focuses on the uneven
distribution of the artifacts in each read and removes such errors in
formalin-fixed and paraffin-embedded samples without over-eliminating the true
mutations detected in fresh frozen samples.")
    (license license:expat)))

(define-public r-micropop
  (package
    (name "r-micropop")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microPop" version))
       (sha256
        (base32 "0if218csmnp5vcbcgr9zjk8d7y17al04rpfhjvp76v03xvwjizp5"))))
    (properties `((upstream-name . "microPop")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork r-testthat r-desolve))
    (native-inputs (list r-knitr))
    (home-page
     "https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12873")
    (synopsis "Process-Based Modelling of Microbial Populations")
    (description
     "Modelling interacting microbial populations - example applications include human
gut microbiota, rumen microbiota and phytoplankton.  Solves a system of ordinary
differential equations to simulate microbial growth and resource uptake over
time.  This version contains network visualisation functions.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-microplot
  (package
    (name "r-microplot")
    (version "1.0-45")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microplot" version))
       (sha256
        (base32 "0qprvn5zv9ai30lhd8qykffc5f8va886kc5qka34940lin63v389"))))
    (properties `((upstream-name . "microplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-officer
                             r-lattice
                             r-htmltools
                             r-hmisc
                             r-hh
                             r-ggplot2
                             r-flextable
                             r-cowplot))
    (home-page "https://cran.r-project.org/package=microplot")
    (synopsis "Microplots (Sparklines) in 'LaTeX', 'Word', 'HTML', 'Excel'")
    (description
     "The microplot function writes a set of R graphics files to be used as microplots
(sparklines) in tables in either @code{LaTeX}', HTML', Word', or Excel files.
For @code{LaTeX}', we provide methods for the Hmisc::latex() generic function to
construct latex tabular environments which include the graphs.  These can be
used directly with the operating system pdflatex or latex command, or by using
one of Sweave', knitr', rmarkdown', or Emacs org-mode as an intermediary.  For
MS Word', the @code{msWord}() function uses the flextable package to construct
Word tables which include the graphs.  There are several distinct approaches for
constructing HTML files.  The simplest is to use the @code{msWord}() function
with argument filetype=\"html\".  Alternatively, use either Emacs org-mode or the
@code{htmlTable::htmlTable}() function to construct an HTML file containing
tables which include the graphs.  See the documentation for our as.htmlimg()
function.  For Excel use on Windows', the file examples/@code{irisExcel.xls}
includes VBA code which brings the individual panels into individual cells in
the spreadsheet.  Examples in the examples and demo subdirectories are shown
with lattice graphics, ggplot2 graphics, and base graphics.  Examples for
@code{LaTeX} include Sweave (both @code{LaTeX'-style} and Noweb'-style), knitr',
emacs org-mode', and rmarkdown input files and their pdf output files.  Examples
for HTML include org-mode and Rmd input files and their webarchive HTML output
files.  In addition, the as.orgtable() function can display a data.frame in an
org-mode document.  The examples for MS Word (with either filetype=\"docx\" or
filetype=\"html\") work with all operating systems.  The package does not require
the installation of @code{LaTeX} or MS Word to be able to write .tex or .docx
files.")
    (license license:gpl2+)))

(define-public r-micropan
  (package
    (name "r-micropan")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micropan" version))
       (sha256
        (base32 "16zs6sqi2c5lwzhd5gbyddp8q4gx5r5sg0pbpk7924i8x4hh081h"))))
    (properties `((upstream-name . "micropan")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-microseq
                             r-igraph
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=micropan")
    (synopsis "Microbial Pan-Genome Analysis")
    (description
     "This package provides a collection of functions for computations and
visualizations of microbial pan-genomes.")
    (license license:gpl2)))

(define-public r-micronutr
  (package
    (name "r-micronutr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micronutr" version))
       (sha256
        (base32 "0gi2d8xkl1df5sncp4ndljfpd2wgkv9d634k3rk1cw4if0dp955c"))))
    (properties `((upstream-name . "micronutr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://nutriverse.io/micronutr/")
    (synopsis "Determining Vitamin and Mineral Status of Populations")
    (description
     "Vitamin and mineral deficiencies continue to be a significant public health
problem.  This is particularly critical in developing countries where
deficiencies to vitamin A, iron, iodine, and other micronutrients lead to
adverse health consequences.  Cross-sectional surveys are helpful in answering
questions related to the magnitude and distribution of deficiencies of selected
vitamins and minerals.  This package provides tools for calculating and
determining select vitamin and mineral deficiencies based on World Health
Organization (WHO) guidelines found at
<https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system>.")
    (license license:gpl3+)))

(define-public r-microniche
  (package
    (name "r-microniche")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MicroNiche" version))
       (sha256
        (base32 "166ci4crkwnnh05rhs5hyrljgm4cdf7gp0ff8kjxjmd60byw80mz"))))
    (properties `((upstream-name . "MicroNiche")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2))
    (home-page "https://cran.r-project.org/package=MicroNiche")
    (synopsis "Microbial Niche Measurements")
    (description
     "Measures niche breadth and overlap of microbial taxa from large matrices.  Niche
breadth measurements include Levins niche breadth (Bn) index, Hurlbert's Bn and
Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E.,
Poole, R.W. (1981) <doi:10.2307/1936664>).  Niche overlap measurements include
Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a
Jaccard similarity index of Feinsinger's PS values between taxa pairs, as
Proportional Overlap.")
    (license license:gpl2)))

(define-public r-micromodal
  (package
    (name "r-micromodal")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micromodal" version))
       (sha256
        (base32 "0qf5ckfcnkviilb2la8la9sjfcir7c79v4981jrpsxfy9n6y39vm"))))
    (properties `((upstream-name . "micromodal")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmltools))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/kennedymwavu/micromodal")
    (synopsis "Create Simple and Elegant Modal Dialogs in 'shiny'")
    (description
     "Enables you to create accessible modal dialogs, with confidence and with minimal
configuration.")
    (license license:expat)))

(define-public r-micromob
  (package
    (name "r-micromob")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MicroMoB" version))
       (sha256
        (base32 "0ghab143lr333v7qih30jc9n78pwcgam4nzaz7j1g4cnis5wfkbx"))))
    (properties `((upstream-name . "MicroMoB")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://dd-harp.github.io/MicroMoB/")
    (synopsis
     "Discrete Time Simulation of Mosquito-Borne Pathogen Transmission")
    (description
     "This package provides a framework based on S3 dispatch for constructing models
of mosquito-borne pathogen transmission which are constructed from submodels of
various components (i.e.  immature and adult mosquitoes, human populations).  A
consistent mathematical expression for the distribution of bites on hosts means
that different models (stochastic, deterministic, etc.) can be coherently
incorporated and updated over a discrete time step.")
    (license license:expat)))

(define-public r-micromapst
  (package
    (name "r-micromapst")
    (version "3.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micromapST" version))
       (sha256
        (base32 "0zg0wgqlk9z9k3qhcnd8y8swa7p3g8q30fvw3g0n71dd4vqxzdcl"))))
    (properties `((upstream-name . "micromapST")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-stringr
                             r-spdep
                             r-sf
                             r-rmapshaper
                             r-readxl
                             r-rcolorbrewer
                             r-labeling))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=micromapST")
    (synopsis "Linked Micromap Plots for U. S. and Other Geographic Areas")
    (description
     "This package provides the users with the ability to quickly create linked
micromap plots for a collection of geographic areas.  Linked micromap plots are
visualizations of geo-referenced data that link statistical graphics to an
organized series of small maps or graphic images.  The Help description contains
examples of how to use the @code{micromapST} function.  Contained in this
package are border group datasets to support creating linked micromap plots for
the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer
Registries, the 105 counties in the state of Kansas, the 62 counties of New
York, the 24 counties of Maryland, the 29 counties of Utah, the 32
administrative areas in China, the 218 administrative areas in the UK and
Ireland (for testing only), the 25 districts in the city of Seoul South Korea,
and the 52 counties on the Africa continent.  A border group dataset contains
the boundaries related to the data level areas, a second layer boundaries, a top
or third layer boundary, a parameter list of run options, and a cross indexing
table between area names, abbreviations, numeric identification and alias
matching strings for the specific geographic area.  By specifying a border
group, the package create linked micromap plots for any geographic region.  The
user can create and provide their own border group dataset for any area beyond
the areas contained within the package.  In version 3.0.0, the
@code{BuildBorderGroup} function was upgraded to not use the retiring maptools',
rgdal', and rgeos packages.  References: Carr and Pickle, Chapman and Hall/CRC,
Visualizing Data Patterns with Micromaps, CRC Press, 2010.  Pickle, Pearson, and
Carr (2015), @code{micromapST}: Exploring and Communicating Geospatial Patterns
in US State Data., Journal of Statistical Software, 63(3), 1-25.,
<https://www.jstatsoft.org/v63/i03/>.  Copyrighted 2013, 2014, 2015, 2016, 2022,
2023, and 2024 by Carr, Pearson and Pickle.")
    (license license:gpl2+)))

(define-public r-micromap
  (package
    (name "r-micromap")
    (version "1.9.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micromap" version))
       (sha256
        (base32 "1qbc2v3h46csca88y1n820hk2j2pc67z2gwf83f2lrsa7mim00x2"))))
    (properties `((upstream-name . "micromap")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf r-rcolorbrewer r-ggplot2))
    (home-page "<https://github.com/fawda123/micromap>")
    (synopsis "Linked Micromap Plots")
    (description
     "This group of functions simplifies the creation of linked micromap plots.
Please see <https://www.jstatsoft.org/v63/i02/> for additional details.")
    (license license:gpl2+)))

(define-public r-micromacromultilevel
  (package
    (name "r-micromacromultilevel")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MicroMacroMultilevel" version))
       (sha256
        (base32 "1q5cyqr4hdl1wp2isk6p4p084i83kg6hd406wdp1nj9lv5i0xvgw"))))
    (properties `((upstream-name . "MicroMacroMultilevel")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MicroMacroMultilevel")
    (synopsis "Micro-Macro Multilevel Modeling")
    (description
     "Most multilevel methodologies can only model macro-micro multilevel situations
in an unbiased way, wherein group-level predictors (e.g., city temperature) are
used to predict an individual-level outcome variable (e.g., citizen
personality).  In contrast, this R package enables researchers to model
micro-macro situations, wherein individual-level (micro) predictors (and other
group-level predictors) are used to predict a group-level (macro) outcome
variable in an unbiased way.")
    (license license:gpl2+)))

(define-public r-microinverterdata
  (package
    (name "r-microinverterdata")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microinverterdata" version))
       (sha256
        (base32 "0l73n1g07ckjcmzc7w8vw74k4lchli6i1xb2zbd9k813n4zlwvqs"))))
    (properties `((upstream-name . "microinverterdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-tidyr
                             r-rlang
                             r-readr
                             r-purrr
                             r-httr2
                             r-glue
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://camembr.github.io/microinverterdata/")
    (synopsis "Collect your Microinverter Data")
    (description
     "Collect and normalize local microinverter energy and power production data
through off-cloud API requests.  Currently supports APSystems microinverters.")
    (license license:expat)))

(define-public r-microhaplot
  (package
    (name "r-microhaplot")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microhaplot" version))
       (sha256
        (base32 "0g15g7d19yz11nn2g0h8h2m8k1cvmpryz72pnalgv5yi58md5l5i"))))
    (properties `((upstream-name . "microhaplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-shinywidgets
                             r-shinybs
                             r-shiny
                             r-scales
                             r-magrittr
                             r-gtools
                             r-ggplot2
                             r-ggiraph
                             r-dt
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ngthomas/microhaplot")
    (synopsis "Microhaplotype Constructor and Visualizer")
    (description
     "This package provides a downstream bioinformatics tool to construct and assist
curation of microhaplotypes from short read sequences.")
    (license license:gpl3)))

(define-public r-microeco
  (package
    (name "r-microeco")
    (version "1.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microeco" version))
       (sha256
        (base32 "0xi9idvlsyazqqd0zvvjy32ggvwnc9jm0997p7q4w1cix2p7nh31"))))
    (properties `((upstream-name . "microeco")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tibble
                             r-scales
                             r-rlang
                             r-reshape2
                             r-rcolorbrewer
                             r-r6
                             r-magrittr
                             r-igraph
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-ape))
    (home-page "https://github.com/ChiLiubio/microeco")
    (synopsis "Microbial Community Ecology Data Analysis")
    (description
     "This package provides a series of statistical and plotting approaches in
microbial community ecology based on the R6 class.  The classes are designed for
data preprocessing, taxa abundance plotting, alpha diversity analysis, beta
diversity analysis, differential abundance test, null model analysis, network
analysis, machine learning, environmental data analysis and functional analysis.")
    (license license:gpl3)))

(define-public r-microdiluter
  (package
    (name "r-microdiluter")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microdiluteR" version))
       (sha256
        (base32 "1x039ja5bwshblc4zr5pnj1mn7jn1x92fddjcjd08y75sgcbi5ng"))))
    (properties `((upstream-name . "microdiluteR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tibble
                             r-stringr
                             r-rstatix
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-ggthemes
                             r-ggplot2
                             r-ggh4x
                             r-dplyr))
    (home-page "https://silvia-eckert.github.io/microdiluteR/")
    (synopsis "Analysis of Broth Microdilution Assays")
    (description
     "This package provides a framework for analyzing broth microdilution assays in
various 96-well plate designs, visualizing results and providing descriptive and
(simple) inferential statistics (i.e.  summary statistics and sign test).  The
functions are designed to add metadata to 8 x 12 tables of absorption values,
creating a tidy data frame.  Users can choose between clean-up procedures via
function parameters (which covers most cases) or user prompts (in cases with
complex experimental designs).  Users can also choose between two validation
methods, i.e.  exclusion of absorbance values above a certain threshold or
manual exclusion of samples.  A function for visual inspection of samples with
their absorption values over time for certain group combinations helps with the
decision.  In addition, the package includes functions to subtract the
background absorption (usually at time T0) and to calculate the growth
performance compared to a baseline.  Samples can be visually inspected with
their absorption values displayed across time points for specific group
combinations.  Core functions of this package (i.e.  background subtraction,
sample validation and statistics) were inspired by the manual calculations that
were applied in Tewes and Muller (2020) <doi:10.1038/s41598-020-67600-7>.")
    (license license:gpl3+)))

(define-public r-microdatoses
  (package
    (name "r-microdatoses")
    (version "0.8.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MicroDatosEs" version))
       (sha256
        (base32 "0amclp66094g96jlr4ks85l3y88fimlh8ws6jp1p74hpfbmd9ykc"))))
    (properties `((upstream-name . "MicroDatosEs")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr))
    (home-page
     "https://www.datanalytics.com/2012/08/06/un-paseo-por-el-paquete-microdatoses-y-la-epa-de-nuevo/")
    (synopsis "Utilities for Official Spanish Microdata")
    (description
     "This package provides utilities for reading and processing microdata from
Spanish official statistics with R.")
    (license license:gpl3)))

(define-public r-microcran
  (package
    (name "r-microcran")
    (version "0.9.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microCRAN" version))
       (sha256
        (base32 "08aygif352awrqy1js8n09vng0f085p27nxbqkiibrrgk2nyk62l"))))
    (properties `((upstream-name . "microCRAN")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-rlang r-plumber r-mime r-assertthat))
    (home-page "https://cran.r-project.org/package=microCRAN")
    (synopsis "Hosting an Independent CRAN Repository")
    (description
     "Stand-alone HTTP capable R-package repository, that fully supports R's
install.packages() and available.packages().  It also contains API endpoints for
end-users to add/update packages.  This package can supplement @code{miniCRAN}',
which has functions for maintaining a local (partial) copy of CRAN'.  Current
version is bare-minimum without any access-control or much security.")
    (license license:gpl3)))

(define-public r-microcontax
  (package
    (name "r-microcontax")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microcontax" version))
       (sha256
        (base32 "004g979hyg0asbah7hxr445fidil4ibw1b8pqpjdbgmpm1qgvw7y"))))
    (properties `((upstream-name . "microcontax")))
    (build-system r-build-system)
    (propagated-inputs (list r-microseq))
    (home-page "https://cran.r-project.org/package=microcontax")
    (synopsis "The ConTax Data Package")
    (description
     "The consensus taxonomy for prokaryotes is a set of data-sets for best possible
taxonomic classification based on 16S @code{rRNA} sequence data.")
    (license license:gpl2)))

(define-public r-microclass
  (package
    (name "r-microclass")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microclass" version))
       (sha256
        (base32 "0q6mn1hbi6nk29x2x5a1wcdqp06mjggbrpglk5i8pp4gkqlzhl8q"))))
    (properties `((upstream-name . "microclass")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-microseq
                             r-microcontax
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=microclass")
    (synopsis "Methods for Taxonomic Classification of Prokaryotes")
    (description
     "This package provides functions for assigning 16S sequence data to a taxonomic
level in the tree-of-life for prokaryotes.")
    (license license:gpl2+)))

(define-public r-microbiomesurv
  (package
    (name "r-microbiomesurv")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MicrobiomeSurv" version))
       (sha256
        (base32 "00wx8vdbkxcn6cp243vbxyzx52hpcxpvxx8hq44gykc7238qcvhv"))))
    (properties `((upstream-name . "MicrobiomeSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survminer
                             r-survival
                             r-superpc
                             r-pls
                             r-microbiome
                             r-lmtest
                             r-gplots
                             r-glmnet
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/N-T-Huyen/MicrobiomeSurv")
    (synopsis
     "Biomarker Validation for Microbiome-Based Survival Classification and Prediction")
    (description
     "An approach to identify microbiome biomarker for time to event data by
discovering microbiome for predicting survival and classifying subjects into
risk groups.  Classifiers are constructed as a linear combination of important
microbiome and treatment effects if necessary.  Several methods were implemented
to estimate the microbiome risk score such as the LASSO method by Robert
Tibshirani (1998)
<doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>,
Elastic net approach by Hui Zou and Trevor Hastie (2005)
<doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis
of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised
partial least squares analysis by Inge S. Helland
<https://www.jstor.org/stable/4616159>.  Sensitivity analysis on the quantile
used for the classification can also be accessed to check the deviation of the
classification group based on the quantile specified.  Large scale cross
validation can be performed in order to investigate the mostly selected
microbiome and for internal validation.  During the evaluation process,
validation is accessed using the hazard ratios (HR) distribution of the test set
and inference is mainly based on resampling and permutations technique.")
    (license license:gpl3)))

(define-public r-microbiomemqc
  (package
    (name "r-microbiomemqc")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microbiomeMQC" version))
       (sha256
        (base32 "1wnx3j85k2hb21382kakmn6ly9wvskffwha1qm0z8kzb726v9wxi"))))
    (properties `((upstream-name . "microbiomeMQC")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-readxl))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=microbiomeMQC")
    (synopsis "Calculate 4 Key Reporting Measures")
    (description
     "Perform calculations for the WHO International Reference Reagents for the
microbiome.  Using strain, species or genera abundance tables generated through
analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a
reference reagent.  This package will calculate measures of sensitivity, False
positive relative abundance, diversity, and similarity based on mean average
abundances with respect to the reference reagent.")
    (license license:gpl3)))

(define-public r-microbial
  (package
    (name "r-microbial")
    (version "0.0.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microbial" version))
       (sha256
        (base32 "1d2sr4wwjlxcaxqr57gzf8m2pydgx4kp3h8mgyxzcm4s00i8m5bc"))))
    (properties `((upstream-name . "microbial")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tidyr
                             r-summarizedexperiment
                             r-s4vectors
                             r-rstatix
                             r-rlang
                             r-randomforest
                             r-plyr
                             r-phyloseq
                             r-phangorn
                             r-magrittr
                             r-ggpubr
                             r-ggplot2
                             r-edger
                             r-dplyr
                             r-deseq2
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=microbial")
    (synopsis "Do 16s Data Analysis and Generate Figures")
    (description
     "This package provides functions to enhance the available statistical analysis
procedures in R by providing simple functions to analysis and visualize the 16S
@code{rRNA} data.Here we present a tutorial with minimum working examples to
demonstrate usage and dependencies.")
    (license license:gpl3)))

(define-public r-microbats
  (package
    (name "r-microbats")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "microbats" version))
       (sha256
        (base32 "1mahvblaiwg1xk2s34wd1ic8ddc9lh6g0azik2pd97bsf0krkyw8"))))
    (properties `((upstream-name . "microbats")))
    (build-system r-build-system)
    (home-page "https://github.com/stathwang/microbats")
    (synopsis "An Implementation of Bat Algorithm in R")
    (description
     "This package provides a nature-inspired metaheuristic algorithm based on the
echolocation behavior of microbats that uses frequency tuning to optimize
problems in both continuous and discrete dimensions.  This R package makes it
easy to implement the standard bat algorithm on any user-supplied function.  The
algorithm was first developed by Xin-She Yang in 2010
(<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).")
    (license license:gpl2+)))

(define-public r-micoptcm
  (package
    (name "r-micoptcm")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miCoPTCM" version))
       (sha256
        (base32 "0bmb06rb53fq8ylcvz693akmz7nkjj50c6ypxsjbfp4crllpi5v9"))))
    (properties `((upstream-name . "miCoPTCM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-nleqslv r-mass r-distr))
    (home-page "https://cran.r-project.org/package=miCoPTCM")
    (synopsis "Promotion Time Cure Model with Mis-Measured Covariates")
    (description
     "Fits Semiparametric Promotion Time Cure Models, taking into account (using a
corrected score approach or the SIMEX algorithm) or not the measurement error in
the covariates, using a backfitting approach to maximize the likelihood.")
    (license license:gpl2)))

(define-public r-micompr
  (package
    (name "r-micompr")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micompr" version))
       (sha256
        (base32 "1i50zvhdfxz0zbx5vmiz9s055j6f0j0gf6a2vxhm7cw597rfp3s5"))))
    (properties `((upstream-name . "micompr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nunofachada/micompr")
    (synopsis "Multivariate Independent Comparison of Observations")
    (description
     "This package provides a procedure for comparing multivariate samples associated
with different groups.  It uses principal component analysis to convert
multivariate observations into a set of linearly uncorrelated statistical
measures, which are then compared using a number of statistical methods.  The
procedure is independent of the distributional properties of samples and
automatically selects features that best explain their differences, avoiding
manual selection of specific points or summary statistics.  It is appropriate
for comparing samples of time series, images, spectrometric measures or similar
multivariate observations.  This package is described in Fachada et al. (2016)
<doi:10.32614/RJ-2016-055>.")
    (license license:expat)))

(define-public r-miclust
  (package
    (name "r-miclust")
    (version "1.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miclust" version))
       (sha256
        (base32 "0qxbbybak0illkn99bsmmqlh923g0gw6znjh05wdmgqdsjh3fx1v"))))
    (properties `((upstream-name . "miclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixstats r-irr r-flexclust r-doby r-combinat))
    (home-page "https://cran.r-project.org/package=miclust")
    (synopsis "Multiple Imputation in Cluster Analysis")
    (description
     "Implementation of a framework for cluster analysis with selection of the final
number of clusters and an optional variable selection procedure.  The package is
designed to integrate the results of multiple imputed datasets while accounting
for the uncertainty that the imputations introduce in the final results.  In
addition, the package can also be used for a cluster analysis of the complete
cases of a single dataset.  The package also includes specific methods to
summarize and plot the results.  The methods are described in Basagana et al.
(2013) <doi:10.1093/aje/kws289>.")
    (license license:gpl3)))

(define-public r-michelrodange
  (package
    (name "r-michelrodange")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "michelRodange" version))
       (sha256
        (base32 "1pykqb9hmpvn77k0vhfh36iyhamz9h5kpswq3ql31h9nknfy3ixi"))))
    (properties `((upstream-name . "michelRodange")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/b-rodrigues/michelRodange")
    (synopsis "The Works (in Luxembourguish) of Michel Rodange")
    (description
     "Michel Rodange was a Luxembourguish writer and poet who lived in the 19th
century.  His most notable work is Rodange (1872, ISBN:1166177424), (\"Renert
oder de FuuÃ am Frack an a Ma'nsgrÃ«Ãt\"), but he also wrote many more works,
including Rodange, Tockert (1928)
<https://www.autorenlexikon.lu/page/document/361/3614/1/FRE/index.html>
(\"D'LÃ©ierchen - Dem LÃ©iweckerche sÃ¤i Lidd\") and Rodange, Welter (1929)
<https://www.autorenlexikon.lu/page/document/361/3615/1/FRE/index.html> (\"Dem
Grow Sigfrid seng Goldkuommer\").  This package contains three datasets, each
made from the plain text versions of his works available on
<https://data.public.lu/fr/datasets/the-works-in-luxembourguish-of-michel-rodange/>.")
    (license license:cc0)))

(define-public r-miceranger
  (package
    (name "r-miceranger")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miceRanger" version))
       (sha256
        (base32 "0xm8fcvjf6pcfyyk1gnwfdac44q6b33i5byja9lqxy7vygbxmgxd"))))
    (properties `((upstream-name . "miceRanger")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger
                             r-ggpubr
                             r-ggplot2
                             r-foreach
                             r-fnn
                             r-desctools
                             r-data-table
                             r-crayon
                             r-corrplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FarrellDay/miceRanger")
    (synopsis "Multiple Imputation by Chained Equations with Random Forests")
    (description
     "Multiple Imputation has been shown to be a flexible method to impute missing
values by Van Buuren (2007) <doi:10.1177/0962280206074463>.  Expanding on this,
random forests have been shown to be an accurate model by Stekhoven and Buhlmann
<@code{arXiv:1105.0828>} to impute missing values in datasets.  They have the
added benefits of returning out of bag error and variable importance estimates,
as well as being simple to run in parallel.")
    (license license:expat)))

(define-public r-micemd
  (package
    (name "r-micemd")
    (version "1.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micemd" version))
       (sha256
        (base32 "1rzn4iiv9zml6c0d5j36c4z1kdn03j05m4q03wca7z0k35icsis6"))))
    (properties `((upstream-name . "micemd")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbivnorm
                             r-nlme
                             r-mvtnorm
                             r-mvmeta
                             r-mixmeta
                             r-mice
                             r-mgcv
                             r-matrix
                             r-mass
                             r-lme4
                             r-jomo
                             r-gjrm
                             r-digest
                             r-abind))
    (home-page "https://cran.r-project.org/package=micemd")
    (synopsis "Multiple Imputation by Chained Equations with Multilevel Data")
    (description
     "Addons for the mice package to perform multiple imputation using chained
equations with two-level data.  Includes imputation methods dedicated to
sporadically and systematically missing values.  Imputation of continuous,
binary or count variables are available.  Following the recommendations of
Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation
method for each variable can be facilitated by a default choice tuned according
to the structure of the incomplete dataset.  Allows parallel calculation and
overimputation for mice'.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-micefast
  (package
    (name "r-micefast")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miceFast" version))
       (sha256
        (base32 "0n4hi9f28mzc9h41g7y3nnkr5lq7npafk6ias17xsqw28agc95ir"))))
    (properties `((upstream-name . "miceFast")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Polkas/miceFast")
    (synopsis "Fast Imputations Using 'Rcpp' and 'Armadillo'")
    (description
     "Fast imputations under the object-oriented programming paradigm.  Moreover there
are offered a few functions built to work with popular R packages such as
data.table or dplyr'.  The biggest improvement in time performance could be
achieve for a calculation where a grouping variable have to be used.  A single
evaluation of a quantitative model for the multiple imputations is another major
enhancement.  A new major improvement is one of the fastest predictive mean
matching in the R world because of presorting and binary search.")
    (license license:gpl2+)))

(define-public r-miceconsnqp
  (package
    (name "r-miceconsnqp")
    (version "0.6-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micEconSNQP" version))
       (sha256
        (base32 "0lrwa0h7xc8scb7rh1wq71srzbhnk174k2qk1jd4adqqr308bgzp"))))
    (properties `((upstream-name . "micEconSNQP")))
    (build-system r-build-system)
    (propagated-inputs (list r-systemfit r-misctools r-mass))
    (home-page "http://www.micEcon.org")
    (synopsis "Symmetric Normalized Quadratic Profit Function")
    (description
     "This package provides tools for econometric production analysis with the
Symmetric Normalized Quadratic (SNQ) profit function, e.g. estimation, imposing
convexity in prices, and calculating elasticities and shadow prices.")
    (license license:gpl2+)))

(define-public r-miceconindex
  (package
    (name "r-miceconindex")
    (version "0.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micEconIndex" version))
       (sha256
        (base32 "1gwvg0anhigd7q1x0p51qddnr678vc79mi1c3q3j5gpby0fgn3ig"))))
    (properties `((upstream-name . "micEconIndex")))
    (build-system r-build-system)
    (propagated-inputs (list r-misctools))
    (home-page "http://www.micEcon.org")
    (synopsis "Price and Quantity Indices")
    (description
     "This package provides tools for calculating Laspeyres, Paasche, and Fisher price
and quantity indices.")
    (license license:gpl2+)))

(define-public r-micecondistray
  (package
    (name "r-micecondistray")
    (version "0.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micEconDistRay" version))
       (sha256
        (base32 "1q15a2sfxk9kdmx27j98q7is1nczxl7s464bzk445a5v8s0dnvs3"))))
    (properties `((upstream-name . "micEconDistRay")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfar))
    (home-page "https://github.com/micEcon/micEconDistRay")
    (synopsis
     "Econometric Production Analysis with Ray-Based Distance Functions")
    (description
     "Econometric analysis of multiple-input-multiple-output production technologies
with ray-based input distance functions as suggested by Price and Henningsen
(2022): \"A Ray-Based Input Distance Function to Model Zero-Valued Output
Quantities: Derivation and an Empirical Application\",
<https://ideas.repec.org/p/foi/wpaper/2022_03.html>.")
    (license license:gpl2+)))

(define-public r-miceconces
  (package
    (name "r-miceconces")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micEconCES" version))
       (sha256
        (base32 "039hv274xbs3syw0nxgkvxgfyn594fpcrlgmfwd2mfv6y78hc796"))))
    (properties `((upstream-name . "micEconCES")))
    (build-system r-build-system)
    (propagated-inputs (list r-systemfit
                             r-misctools
                             r-minpack-lm
                             r-micecon
                             r-deoptim
                             r-car))
    (home-page "http://www.micEcon.org")
    (synopsis
     "Analysis with the Constant Elasticity of Substitution (CES) Function")
    (description
     "This package provides tools for econometric analysis and economic modelling with
the traditional two-input Constant Elasticity of Substitution (CES) function and
with nested CES functions with three and four inputs.  The econometric
estimation can be done by the Kmenta approximation, or non-linear least-squares
using various gradient-based or global optimisation algorithms.  Some of these
algorithms can constrain the parameters to certain ranges, e.g. economically
meaningful values.  Furthermore, the non-linear least-squares estimation can be
combined with a grid-search for the rho-parameter(s).  The estimation methods
are described in Henningsen et al. (2021) <doi:10.4337/9781788976480.00030>.")
    (license license:gpl2+)))

(define-public r-miceconaids
  (package
    (name "r-miceconaids")
    (version "0.6-20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micEconAids" version))
       (sha256
        (base32 "0myy9ny2v90g9lgg8mi7ylfz6iyz7jqbs1xskhj9hqdps90rm0k1"))))
    (properties `((upstream-name . "micEconAids")))
    (build-system r-build-system)
    (propagated-inputs (list r-systemfit r-misctools r-micecon r-lmtest))
    (home-page "http://www.micEcon.org")
    (synopsis "Demand Analysis with the Almost Ideal Demand System (AIDS)")
    (description
     "This package provides functions and tools for analysing consumer demand with the
Almost Ideal Demand System (AIDS) suggested by Deaton and Muellbauer (1980).")
    (license license:gpl2+)))

(define-public r-micecon
  (package
    (name "r-micecon")
    (version "0.6-18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micEcon" version))
       (sha256
        (base32 "1vdxl93d6fpfp6xfzrypnkwfry9ajfpivarj4glnx3w2sydmi7ql"))))
    (properties `((upstream-name . "micEcon")))
    (build-system r-build-system)
    (propagated-inputs (list r-plm r-misctools))
    (home-page "http://www.micEcon.org")
    (synopsis "Microeconomic Analysis and Modelling")
    (description
     "Various tools for microeconomic analysis and microeconomic modelling, e.g.
estimating quadratic, Cobb-Douglas and Translog functions, calculating partial
derivatives and elasticities of these functions, and calculating Hessian
matrices, checking curvature and preparing restrictions for imposing
monotonicity of Translog functions.")
    (license license:gpl2+)))

(define-public r-miceafter
  (package
    (name "r-miceafter")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miceafter" version))
       (sha256
        (base32 "192widdnj4ahv2rp09jzzz68n0ycdplv24p50kqd3i6zqnr1cl0s"))))
    (properties `((upstream-name . "miceafter")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-stringr
                             r-rms
                             r-rlang
                             r-purrr
                             r-proc
                             r-mitools
                             r-mitml
                             r-mice
                             r-magrittr
                             r-dplyr
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://mwheymans.github.io/miceafter/")
    (synopsis "Data and Statistical Analyses after Multiple Imputation")
    (description
     "Statistical Analyses and Pooling after Multiple Imputation.  A large variety of
repeated statistical analysis can be performed and finally pooled.  Statistical
analysis that are available are, among others, Levene's test, Odds and Risk
Ratios, One sample proportions, difference between proportions and linear and
logistic regression models.  Functions can also be used in combination with the
Pipe operator.  More and more statistical analyses and pooling functions will be
added over time.  Heymans (2007) <doi:10.1186/1471-2288-7-33>.  Eekhout (2017)
<doi:10.1186/s12874-017-0404-7>.  Wiel (2009)
<doi:10.1093/biostatistics/kxp011>.  Marshall (2009)
<doi:10.1186/1471-2288-9-57>.  Sidi (2021) <doi:10.1080/00031305.2021.1898468>.
Lott (2018) <doi:10.1080/00031305.2018.1473796>.  Grund (2021)
<doi:10.31234/osf.io/d459g>.")
    (license license:gpl2+)))

(define-public r-miceadds
  (package
    (name "r-miceadds")
    (version "3.17-44")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "miceadds" version))
       (sha256
        (base32 "0r91rjl44sxdwfdb0z10hnjq7yfiqlcwfm6aa0a7lm1vmk8rvxkf"))))
    (properties `((upstream-name . "miceadds")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mitools r-mice))
    (home-page "https://github.com/alexanderrobitzsch/miceadds")
    (synopsis
     "Some Additional Multiple Imputation Functions, Especially for 'mice'")
    (description
     "This package contains functions for multiple imputation which complements
existing functionality in R. In particular, several imputation methods for the
mice package (van Buuren & Groothuis-Oudshoorn, 2011,
<doi:10.18637/jss.v045.i03>) are implemented.  Main features of the miceadds
package include plausible value imputation (Mislevy, 1991,
<doi:10.1007/BF02294457>), multilevel imputation for variables at any level or
with any number of hierarchical and non-hierarchical levels (Grund, Luedtke &
Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7,
<doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for
high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple
imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model
compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>),
and features for the generation of synthetic datasets (Reiter, 2005,
<doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016,
<doi:10.18637/jss.v074.i11>).")
    (license license:gpl2+)))

(define-public r-micd
  (package
    (name "r-micd")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micd" version))
       (sha256
        (base32 "0scihw884m8cj3c8kg2g83w36wks44s3s7nfbhifqc89nv5q0p8a"))))
    (properties `((upstream-name . "micd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-rbgl r-pcalg r-mice))
    (home-page "https://github.com/bips-hb/micd")
    (synopsis "Multiple Imputation in Causal Graph Discovery")
    (description
     "Modified functions of the package pcalg and some additional functions to run the
PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal
discovery in incomplete and multiply imputed datasets.  Foraita R, Friemel J,
GÃ¼nther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020)
<doi:10.1111/rssa.12565>; Andrews RM, Foraita R, Didelez V, Witte J (2021)
<@code{arXiv:2108.13395>}; Witte J, Foraita R, Didelez V (2022)
<doi:10.1002/sim.9535>.")
    (license license:gpl3+)))

(define-public r-micar
  (package
    (name "r-micar")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "micar" version))
       (sha256
        (base32 "1zx2x6r4m0ihwhjd2n4rmk9da7p9bpadk65h7mbb0rgvwrspkzf8"))))
    (properties `((upstream-name . "micar")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=micar")
    (synopsis "'Mica' Data Web Portal Client")
    (description
     "Mica is a server application used to create data web portals for large-scale
epidemiological studies or multiple-study consortia.  Mica helps studies to
provide scientifically robust data visibility and web presence without
significant information technology effort.  Mica provides a structured
description of consortia, studies, annotated and searchable data dictionaries,
and data access request management.  This Mica client allows to perform data
extraction for reporting purposes.")
    (license license:gpl3)))

(define-public r-miamaxent
  (package
    (name "r-miamaxent")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MIAmaxent" version))
       (sha256
        (base32 "0jshi9p0q8j5j0s65s4n14b5r6gpzsigygdibmhnvvqygflyzzqa"))))
    (properties `((upstream-name . "MIAmaxent")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rlang r-e1071 r-dplyr))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/julienvollering/MIAmaxent")
    (synopsis
     "Modular, Integrated Approach to Maximum Entropy Distribution Modeling")
    (description
     "This package provides tools for training, selecting, and evaluating maximum
entropy (and standard logistic regression) distribution models.  This package
provides tools for user-controlled transformation of explanatory variables,
selection of variables by nested model comparison, and flexible model evaluation
and projection.  It follows principles based on the maximum- likelihood
interpretation of maximum entropy modeling, and uses infinitely- weighted
logistic regression for model fitting.  The package is described in Vollering et
al. (2019; <doi:10.1002/ece3.5654>).")
    (license license:expat)))

(define-public r-mi4p
  (package
    (name "r-mi4p")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mi4p" version))
       (sha256
        (base32 "0xjbjjfm9ypa8qz1d2aq0zlkwdrr2c9642476i2d63p4aqipx2kb"))))
    (properties `((upstream-name . "mi4p")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-mice
                             r-limma
                             r-impute
                             r-imp4p
                             r-foreach
                             r-emmeans))
    (native-inputs (list r-knitr))
    (home-page "https://mariechion.github.io/mi4p/")
    (synopsis "Multiple Imputation for Proteomics")
    (description
     "This package provides a framework for multiple imputation for proteomics is
proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021)
<doi:10.1371/journal.pcbi.1010420>.  It is dedicated to dealing with multiple
imputation for proteomics.")
    (license license:gpl2+)))

(define-public r-mhurdle
  (package
    (name "r-mhurdle")
    (version "1.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mhurdle" version))
       (sha256
        (base32 "0dlajmfwd0bz3npa7zy66pa5hxs08l89axnc77mp8q67mp6xwa7y"))))
    (properties `((upstream-name . "mhurdle")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncreg
                             r-survival
                             r-sandwich
                             r-rdpack
                             r-prediction
                             r-numderiv
                             r-nonnest2
                             r-maxlik
                             r-margins
                             r-generics
                             r-formula
                             r-compquadform))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://www.R-project.org")
    (synopsis "Multiple Hurdle Tobit Models")
    (description
     "Estimation of models with dependent variable left-censored at zero.  Null values
may be caused by a selection process Cragg (1971) <doi:10.2307/1909582>,
insufficient resources Tobin (1958) <doi:10.2307/1907382>, or infrequency of
purchase Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.")
    (license license:gpl2+)))

(define-public r-mhtrajectoryr
  (package
    (name "r-mhtrajectoryr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MHTrajectoryR" version))
       (sha256
        (base32 "13idcjx7pjpwvr4c52938yqhhaj1gprb8hjhaim3jx4062wf9pla"))))
    (properties `((upstream-name . "MHTrajectoryR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv))
    (home-page "https://cran.r-project.org/package=MHTrajectoryR")
    (synopsis
     "Bayesian Model Selection in Logistic Regression for the Detection of Adverse Drug Reactions")
    (description
     "Spontaneous adverse event reports have a high potential for detecting adverse
drug reactions.  However, due to their dimension, the analysis of such databases
requires statistical methods.  We propose to use a logistic regression whose
sparsity is viewed as a model selection challenge.  Since the model space is
huge, a Metropolis-Hastings algorithm carries out the model selection by
maximizing the BIC criterion.")
    (license license:gpl2+)))

(define-public r-mhtmult
  (package
    (name "r-mhtmult")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MHTmult" version))
       (sha256
        (base32 "1y3vh2kab6nfkiz4nzdhrpy9h6drk1ibfd2h62hpr3y09z9a2yld"))))
    (properties `((upstream-name . "MHTmult")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MHTmult")
    (synopsis
     "Multiple Hypotheses Testing for Multiple Families/Groups Structure")
    (description
     "This package provides a Comprehensive tool for almost all existing multiple
testing methods for multiple families.  The package summarizes the existing
methods for multiple families multiple testing procedures (MTPs) such as double
FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling
procedure.  The package also provides some novel multiple testing procedures
using selective inference idea.")
    (license license:gpl2+)))

(define-public r-mhtdiscrete
  (package
    (name "r-mhtdiscrete")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MHTdiscrete" version))
       (sha256
        (base32 "0czpsk4jiwbzd6g2dwssmggsdhwchikmc1skv48d9j4xvglns7yw"))))
    (properties `((upstream-name . "MHTdiscrete")))
    (build-system r-build-system)
    (home-page "https://allen.shinyapps.io/MTPs/")
    (synopsis "Multiple Hypotheses Testing for Discrete Data")
    (description
     "This package provides a comprehensive tool for almost all existing multiple
testing methods for discrete data.  The package also provides some novel
multiple testing procedures controlling FWER/FDR for discrete data.  Given
discrete p-values and their domains, the [method].p.adjust function returns
adjusted p-values, which can be used to compare with the nominal significant
level alpha and make decisions.  For users convenience, the functions also
provide the output option for printing decision rules.")
    (license license:gpl2+)))

(define-public r-mhtboot
  (package
    (name "r-mhtboot")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mhtboot" version))
       (sha256
        (base32 "1z0p8ny9lpnrshgas7cad2lv7j0blw0n6ihiimw600n70h73jwrn"))))
    (properties `((upstream-name . "mhtboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mhtboot")
    (synopsis "Multiple Hypothesis Test Based on Distribution of p Values")
    (description
     "This package provides a framework for multiple hypothesis testing based on
distribution of p values.  It is well known that the p values come from
different distribution for null and alternatives, in this package we provide
functions to detect that change.  We provide a method for using the change in
distribution of p values as a way to detect the true signals in the data.")
    (license license:gpl3)))

(define-public r-mhorseshoe
  (package
    (name "r-mhorseshoe")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mhorseshoe" version))
       (sha256
        (base32 "064wyih65sqy66v5yr5cvvdm2crgbfkq8pzzcxvbp74vr8gxljdp"))))
    (properties `((upstream-name . "Mhorseshoe")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Mhorseshoe")
    (synopsis "Approximate Algorithm for Horseshoe Prior")
    (description
     "This package provides exact and approximate algorithms for the horseshoe prior
in linear regression models, which were proposed by Johndrow et al. (2020)
<https://www.jmlr.org/papers/v21/19-536.html>.")
    (license license:expat)))

(define-public r-mhmmbayes
  (package
    (name "r-mhmmbayes")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mHMMbayes" version))
       (sha256
        (base32 "1z13zzim90akaz82dbp8s81zyhcd3xmx3wmqvkcaq5wqh2p64s7a"))))
    (properties `((upstream-name . "mHMMbayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpp r-mvtnorm r-mcmcpack))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=mHMMbayes")
    (synopsis "Multilevel Hidden Markov Models Using Bayesian Estimation")
    (description
     "An implementation of the multilevel (also known as mixed or random effects)
hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov
model (HMM) is a generalization of the well-known hidden Markov model, for the
latter see Rabiner (1989) <doi:10.1109/5.18626>.  The multilevel HMM is tailored
to accommodate (intense) longitudinal data of multiple individuals
simultaneously, see e.g., de Haan-Rietdijk et al.
<doi:10.1080/00273171.2017.1370364>.  Using a multilevel framework, we allow for
heterogeneity in the model parameters (transition probability matrix and
conditional distribution), while estimating one overall HMM. The model can be
fitted on multivariate data with either a categorical, normal, or Poisson
distribution, and include individual level covariates (allowing for e.g., group
comparisons on model parameters).  Parameters are estimated using Bayesian
estimation utilizing the forward-backward recursion within a hybrid Metropolis
within Gibbs sampler.  Missing data (NA) in the dependent variables is
accommodated assuming MAR. The package also includes various visualization
options, a function to simulate data, and a function to obtain the most likely
hidden state sequence for each individual using the Viterbi algorithm.")
    (license license:gpl3)))

(define-public r-mhda
  (package
    (name "r-mhda")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MHDA" version))
       (sha256
        (base32 "0l0ifw2slk5wx9nv6i6xqj79ccpgirk7qzzssy7mvhd249d9ybzb"))))
    (properties `((upstream-name . "MHDA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MHDA")
    (synopsis "Massive Hierarchically Data Analysis")
    (description
     "Three main functions about analyzing massive data (missing observations are
allowed) considered from multiple layers of categories.")
    (license license:gpl2)))

(define-public r-mhd
  (package
    (name "r-mhd")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MHD" version))
       (sha256
        (base32 "0ghl1ah8b1k1czj01dfqms2k2gvzp09mn3qrl8qv3q3mwa8xs4i8"))))
    (properties `((upstream-name . "MHD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-plyr r-nloptr r-manifold r-distory))
    (home-page "https://cran.r-project.org/package=MHD")
    (synopsis "Metric Halfspace Depth")
    (description
     "Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean
data.  Implementing the method described in Dai and Lopez-Pintado (2022)
<doi:10.1080/01621459.2021.2011298>.")
    (license license:gpl2+)))

(define-public r-mhctools
  (package
    (name "r-mhctools")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MHCtools" version))
       (sha256
        (base32 "0y41m6727ls56sqvqir9n3lja6vmxk9bwnsy45jpjfav286fqlgd"))))
    (properties `((upstream-name . "MHCtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-openxlsx r-mgcv))
    (home-page "https://cran.r-project.org/package=MHCtools")
    (synopsis "Analysis of MHC Data in Non-Model Species")
    (description
     "Fifteen tools for bioinformatics processing and analysis of major
histocompatibility complex (MHC) data.  The functions are tailored for amplicon
data sets that have been filtered using the dada2 method (for more information
on dada2, visit <https://benjjneb.github.io/dada2/> ), but even other types of
data sets can be analyzed.  The @code{ReplMatch}() function matches replicates
in data sets in order to evaluate genotyping success.  The @code{GetReplTable}()
and @code{GetReplStats}() functions perform such an evaluation.  The
@code{CreateFas}() function creates a fasta file with all the sequences in the
data set.  The @code{CreateSamplesFas}() function creates individual fasta files
for each sample in the data set.  The @code{DistCalc}() function calculates
Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in
a data set, and mean distances of all pairwise comparisons within each sample in
a data set.  The function additionally outputs five tables with physico-chemical
z-descriptor values (based on Sandberg et al.  1998) for each amino acid
position in all sequences in the data set.  These tables may be useful for
further downstream analyses, such as estimation of MHC supertypes.  The
@code{BootKmeans}() function is a wrapper for the kmeans() function of the stats
package, which allows for bootstrapping.  Bootstrapping k-estimates may be
desirable in data sets, where e.g. BIC- vs. k-values do not produce clear
inflection points (\"elbows\"). @code{BootKmeans}() performs multiple runs of
kmeans() and estimates optimal k-values based on a user-defined threshold of BIC
reduction.  The method is an automated and bootstrapped version of visually
inspecting elbow plots of BIC- vs. k-values.  The @code{ClusterMatch}() function
is a tool for evaluating whether different k-means() clustering models identify
similar clusters, and summarize bootstrap model stats as means for different
estimated values of k.  It is designed to take files produced by the
@code{BootKmeans}() function as input, but other data can be analysed if the
descriptions of the required data formats are observed carefully.  The
@code{PapaDiv}() function compares parent pairs in the data set and calculate
their joint MHC diversity, taking into account sequence variants that occur in
both parents.  The @code{HpltFind}() function infers putative haplotypes from
families in the data set.  The @code{GetHpltTable}() and @code{GetHpltStats}()
functions evaluate the accuracy of the haplotype inference.  The
@code{CreateHpltOccTable}() function creates a binary (logical)
haplotype-sequence occurrence matrix from the output of @code{HpltFind}(), for
easy overview of which sequences are present in which haplotypes.  The
@code{HpltMatch}() function compares haplotypes to help identify overlapping and
potentially identical types.  The @code{NestTablesXL}() function translates the
output from @code{HpltFind}() to an Excel workbook, that provides a convenient
overview for evaluation and curating of the inferred putative haplotypes.")
    (license license:expat)))

(define-public r-mhcnuggetsr
  (package
    (name "r-mhcnuggetsr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mhcnuggetsr" version))
       (sha256
        (base32 "0fq0niigf4lb6jqcy0w4mkfy65pxrw4brf3pg2nvmgnv3c91lgsb"))))
    (properties `((upstream-name . "mhcnuggetsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr r-reticulate r-rappdirs))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/richelbilderbeek/mhcnuggetsr/")
    (synopsis "Call MHCnuggets")
    (description
     "MHCnuggets (<https://github.com/@code{KarchinLab/mhcnuggets>}) is a Python tool
to predict MHC class I and MHC class II epitopes.  This package allows one to
call MHCnuggets from R.")
    (license license:gpl3)))

(define-public r-mhazard
  (package
    (name "r-mhazard")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mhazard" version))
       (sha256
        (base32 "030r0l11qplmypwfk45mj31gba96xda7nxzgiyjsgrb20jmcsja5"))))
    (properties `((upstream-name . "mhazard")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rootsolve
                             r-rcpparmadillo
                             r-rcpp
                             r-plot3d
                             r-boot))
    (home-page "https://cran.r-project.org/package=mhazard")
    (synopsis
     "Nonparametric and Semiparametric Methods for Multivariate Failure Time Data")
    (description
     "Nonparametric survival function estimates and semiparametric regression for the
multivariate failure time data with right-censoring.  For nonparametric survival
function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for
bivariate failure time data may be computed as well as the Dabrowska estimate
for the trivariate failure time data.  Bivariate marginal hazard rate regression
can be fitted for the bivariate failure time data.  Functions are also provided
to compute (bootstrap) confidence intervals and plot the estimates of the
bivariate survival function.  For details, see \"The Statistical Analysis of
Multivariate Failure Time Data: A Marginal Modeling Approach\", Prentice, R.,
Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.")
    (license license:gpl3)))

(define-public r-mgwrsar
  (package
    (name "r-mgwrsar")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgwrsar" version))
       (sha256
        (base32 "0xhxx1x625igm54n5wpl4aavcqg9dx9nmazr330538p334df3z8h"))))
    (properties `((upstream-name . "mgwrsar")))
    (build-system r-build-system)
    (propagated-inputs (list r-spgwr
                             r-sp
                             r-sf
                             r-rlang
                             r-rcppeigen
                             r-rcpp
                             r-nabor
                             r-microbenchmark
                             r-mgcv
                             r-mboost
                             r-matrix
                             r-mapview
                             r-leaflet
                             r-knitr
                             r-htmltools
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mgwrsar")
    (synopsis "GWR and MGWR with Spatial Autocorrelation")
    (description
     "This package provides functions for computing (Mixed) Geographically Weighted
Regression with spatial autocorrelation, Geniaux and Martinetti (2017)
<doi:10.1016/j.regsciurbeco.2017.04.001>.")
    (license license:gpl2+)))

(define-public r-mgwrhw
  (package
    (name "r-mgwrhw")
    (version "1.1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgwrhw" version))
       (sha256
        (base32 "0nk07rl4g4l9ng4fq9xh1smxz705akn25svadjwn17wvm9wjg7dk"))))
    (properties `((upstream-name . "mgwrhw")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-spgwr
                             r-sf
                             r-psych
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mgwrhw")
    (synopsis
     "Displays GWR (Geographically Weighted Regression) and Mixed GWR Output and Map")
    (description
     "Display processing results using the GWR (Geographically Weighted Regression)
method, display maps, and show the results of the Mixed GWR (Mixed
Geographically Weighted Regression) model which automatically selects global
variables based on variability between regions.  This function refers to Yasin,
& Purhadi. (2012). \"Mixed Geographically Weighted Regression Model (Case Study
the Percentage of Poor Households in Mojokerto 2008)\".  European Journal of
Scientific Research, 188-196.
<https://www.researchgate.net/profile/Hasbi-Yasin-2/publication/289689583_Mixed_geographically_weighted_regression_model_case_study_The_percentage_of_poor_households_in_Mojokerto_2008/links/58e46aa40f7e9bbe9c94d641/Mixed-geographically-weighted-regression-model-case-study-The-percentage-of-poor-households-in-Mojokerto-2008.pdf>.")
    (license license:gpl3)))

(define-public r-mgwnbr
  (package
    (name "r-mgwnbr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgwnbr" version))
       (sha256
        (base32 "0vmpvfnnq1ylsivizz888akaq74ybz890nspj40zx5wr7dv2rkz7"))))
    (properties `((upstream-name . "mgwnbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp))
    (home-page "https://cran.r-project.org/package=mgwnbr")
    (synopsis
     "Multiscale Geographically Weighted Negative Binomial Regression")
    (description
     "Fits a geographically weighted regression model with different scales for each
covariate.  Uses the negative binomial distribution as default, but also accepts
the normal, Poisson, or logistic distributions.  Can fit the global versions of
each regression and also the geographically weighted alternatives with only one
scale, since they are all particular cases of the multiscale approach.  Hanchen
Yu (2024). \"Exploring Multiscale Geographically Weighted Negative Binomial
Regression\", Annals of the American Association of Geographers
<doi:10.1080/24694452.2023.2289986>.  Fotheringham AS, Yang W, Kang W (2017).
\"Multiscale Geographically Weighted Regression (MGWR)\", Annals of the American
Association of Geographers <doi:10.1080/24694452.2017.1352480>.  Da Silva AR,
Rodrigues TCV (2014). \"Geographically Weighted Negative Binomial Regression -
incorporating overdispersion\", Statistics and Computing
<doi:10.1007/s11222-013-9401-9>.")
    (license license:gpl3)))

(define-public r-mgss
  (package
    (name "r-mgss")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgss" version))
       (sha256
        (base32 "0czzr5hc9sr8yc362q9xag3vphwk1gfahczpya2mh1l6fsa74fbn"))))
    (properties `((upstream-name . "mgss")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-rcpp r-matrix r-combinat))
    (home-page "https://cran.r-project.org/package=mgss")
    (synopsis "Matrix-Free Multigrid Preconditioner for Spline Smoothing")
    (description
     "Data smoothing with penalized splines is a popular method and is well
established for one- or two-dimensional covariates.  The extension to multiple
covariates is straightforward but suffers from exponentially increasing memory
requirements and computational complexity.  This toolbox provides a matrix-free
implementation of a conjugate gradient (CG) method for the regularized least
squares problem resulting from tensor product B-spline smoothing with
multivariate and scattered data.  It further provides matrix-free preconditioned
versions of the CG-algorithm where the user can choose between a simpler
diagonal preconditioner and an advanced geometric multigrid preconditioner.  The
main advantage is that all algorithms are performed matrix-free and therefore
require only a small amount of memory.  For further detail see Siebenborn &
Wagner (2021).")
    (license license:expat)))

(define-public r-mgsda
  (package
    (name "r-mgsda")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGSDA" version))
       (sha256
        (base32 "03pr294fddh2dyn2q4fvai12rmpf0wfanjd3m0wimfvmr8idbw9a"))))
    (properties `((upstream-name . "MGSDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MGSDA")
    (synopsis "Multi-Group Sparse Discriminant Analysis")
    (description
     "This package implements Multi-Group Sparse Discriminant Analysis proposal of
I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of
canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.")
    (license license:gpl2+)))

(define-public r-mgpstreamingsdk
  (package
    (name "r-mgpstreamingsdk")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgpStreamingSDK" version))
       (sha256
        (base32 "1fgcca0jca0jw6gpcqjb5wrcl9x3r2hfhf1xwiwv2w1m7bhc3mkp"))))
    (properties `((upstream-name . "mgpStreamingSDK")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mgpStreamingSDK")
    (synopsis "Interact with the Maxar MGP Streaming API")
    (description
     "This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming
API. It can search for images using the WFS method.  It can Download images
using WMS WMTS. It can also Download a full resolution image.")
    (license license:asl2.0)))

(define-public r-mgpsdk
  (package
    (name "r-mgpsdk")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGPSDK" version))
       (sha256
        (base32 "07c92bmx572j0bda25d5rmygnq1mmp9pb8hyp387bqfm8yg7v9zi"))))
    (properties `((upstream-name . "MGPSDK")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MGPSDK")
    (synopsis
     "Interact with the Maxar 'MGP' Application Programming Interfaces")
    (description
     "This package provides an interface to the Maxar Geospatial Platform (MGP)
Application Programming Interface.
<https://www.maxar.com/maxar-geospatial-platform> It facilitates imagery
searches using the MGP Streaming Application Programming Interface via the Web
Feature Service (WFS) method, and supports image downloads through Web Map
Service (WMS) and Web Map Tile Service (WMTS) Open Geospatial Consortium (OGC)
methods.  Additionally, it integrates with the Maxar Geospatial Platform
Basemaps Application Programming Interface for accessing Maxar basemaps imagery
and seamlines.  The package also offers seamless integration with the Maxar
Geospatial Platform Discovery Application Programming Interface, allowing users
to search, filter, and sort Maxar content, while retrieving detailed metadata in
formats like @code{SpatioTemporal} Asset Catalog (STAC) and @code{GeoJSON}.")
    (license license:expat)))

(define-public r-mgms2
  (package
    (name "r-mgms2")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGMS2" version))
       (sha256
        (base32 "04f1ng4s163x7gvy1iaf0j8pzs0pm2r2saji0srbp2jyhrj68d7z"))))
    (properties `((upstream-name . "MGMS2")))
    (build-system r-build-system)
    (propagated-inputs (list r-maldiquantforeign r-maldiquant))
    (home-page "https://cran.r-project.org/package=MGMS2")
    (synopsis "'MGMS2' for Polymicrobial Samples")
    (description
     "This package provides a glycolipid mass spectrometry technology has the
potential to accurately identify individual bacterial species from polymicrobial
samples.  To develop bacterial identification algorithms (e.g. machine learning)
using this glycolipid technology, it is necessary to generate a large number of
various in-silico polymicrobial mass spectra that are similar to real mass
spectra.  MGMS2 (Membrane Glycolipid Mass Spectrum Simulator) generates such
in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and
variances of intensity values, occasions of missing signature ions, and noise
peaks.  It estimates summary statistics of monomicrobial mass spectra for each
strain or species and simulates polymicrobial glycolipid mass spectra using the
summary statistics of monomicrobial mass spectra.  References: Ryu, S.Y., Wendt,
G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019)
<doi:10.1021/acs.analchem.9b03340> \"Model-based Spectral Library Approach for
Bacterial Identification via Membrane Glycolipids.\" Gibb, S. and Strimmer, K.
(2012) <doi:10.1093/bioinformatics/bts447> \"MALDIquant: a versatile R package
for the analysis of mass spectrometry data.\".")
    (license license:gpl3)))

(define-public r-mgmm
  (package
    (name "r-mgmm")
    (version "1.0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGMM" version))
       (sha256
        (base32 "090qzgy8fdi3asri8kyin52whf3w02yi9713gawbrl8gdiqp4l0b"))))
    (properties `((upstream-name . "MGMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-plyr r-mvnfast r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MGMM")
    (synopsis "Missingness Aware Gaussian Mixture Models")
    (description
     "Parameter estimation and classification for Gaussian Mixture Models (GMMs) in
the presence of missing data.  This package complements existing implementations
by allowing for both missing elements in the input vectors and full (as opposed
to strictly diagonal) covariance matrices.  Estimation is performed using an
expectation conditional maximization algorithm that accounts for missingness of
both the cluster assignments and the vector components.  The output includes the
marginal cluster membership probabilities; the mean and covariance of each
cluster; the posterior probabilities of cluster membership; and a completed
version of the input data, with missing values imputed to their posterior
expectations.  For additional details, please see @code{McCaw} ZR, Julienne H,
Aschard H. \"Fitting Gaussian mixture models on incomplete data.\"
<doi:10.1186/s12859-022-04740-9>.")
    (license license:gpl3)))

(define-public r-mgm
  (package
    (name "r-mgm")
    (version "1.2-14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgm" version))
       (sha256
        (base32 "1j5hvf168vzanhc5ck5rxz5arjq3b0gs552kf3l1f8kqdmd8kpzw"))))
    (properties `((upstream-name . "mgm")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-qgraph r-hmisc r-gtools r-glmnet))
    (home-page "https://www.jstatsoft.org/article/view/v093i08")
    (synopsis "Estimating Time-Varying k-Order Mixed Graphical Models")
    (description
     "Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p)
models via elastic-net regularized neighborhood regression.  For details see
Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.")
    (license license:gpl2+)))

(define-public r-mglmn
  (package
    (name "r-mglmn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mglmn" version))
       (sha256
        (base32 "1g642hhs28q6x34xgrfrmmsb2jsn9zy4jxad0x5ai3px48zzdb4r"))))
    (properties `((upstream-name . "mglmn")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall r-mvabund))
    (home-page "https://github.com/mattocci27/mglmn")
    (synopsis "Model Averaging for Multivariate GLM with Null Models")
    (description
     "This package provides tools for univariate and multivariate generalized linear
models with model averaging and null model technique.")
    (license license:expat)))

(define-public r-mglm
  (package
    (name "r-mglm")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGLM" version))
       (sha256
        (base32 "0fkdknr954cglvj4pgwphr1d5apx45v0l8m2blghz8vagzk01ndf"))))
    (properties `((upstream-name . "MGLM")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MGLM")
    (synopsis "Multivariate Response Generalized Linear Models")
    (description
     "This package provides functions that (1) fit multivariate discrete
distributions, (2) generate random numbers from multivariate discrete
distributions, and (3) run regression and penalized regression on the
multivariate categorical response data.  Implemented models include: multinomial
logit model, Dirichlet multinomial model, generalized Dirichlet multinomial
model, and negative multinomial model.  Making the best of the
minorization-maximization (MM) algorithm and Newton-Raphson method, we derive
and implement stable and efficient algorithms to find the maximum likelihood
estimates.  On a multi-core machine, multi-threading is supported.")
    (license license:gpl2+)))

(define-public r-mglasso
  (package
    (name "r-mglasso")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mglasso" version))
       (sha256
        (base32 "1896fg1gdgah8vmikz2br6v0rslhhh2frffkjiy6fnw4qxa5zsv9"))))
    (properties `((upstream-name . "mglasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi
                             r-reticulate
                             r-r-utils
                             r-matrix
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://desanou.github.io/mglasso/")
    (synopsis "Multiscale Graphical Lasso")
    (description
     "Inference of Multiscale graphical models with neighborhood selection approach.
The method is based on solving a convex optimization problem combining a Lasso
and fused-group Lasso penalties.  This allows to infer simultaneously a
conditional independence graph and a clustering partition.  The optimization is
based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding
Algorithm solver (Hadj-Selem et al.  2018) <doi:10.1109/TMI.2018.2829802>
implemented in python.")
    (license license:expat)))

(define-public r-mgl
  (package
    (name "r-mgl")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGL" version))
       (sha256
        (base32 "03q453swnangxyc4nvjhzw2zpcy5vp1wpilap5rwymnf0c0xi4j7"))))
    (properties `((upstream-name . "MGL")))
    (build-system r-build-system)
    (home-page "https://sites.google.com/a/cs.washington.edu/mgl/")
    (synopsis "Module Graphical Lasso")
    (description
     "An aggressive dimensionality reduction and network estimation technique for a
high-dimensional Gaussian graphical model (GGM).  Please refer to: Efficient
Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik,
Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference
on Machine Learning, 2014, p.  1953--1961.")
    (license license:gpl2+)))

(define-public r-mgi-report-reader
  (package
    (name "r-mgi-report-reader")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgi.report.reader" version))
       (sha256
        (base32 "0r0s8ji2mdi9hkls7ly645sa06hmcl903v9ys6cxzadbiprwnbnv"))))
    (properties `((upstream-name . "mgi.report.reader")))
    (build-system r-build-system)
    (propagated-inputs (list r-vroom
                             r-tibble
                             r-stringr
                             r-rlang
                             r-memoise
                             r-httr2
                             r-dplyr
                             r-data-table))
    (home-page "https://www.pattern.institute/mgi.report.reader/")
    (synopsis "Read Mouse Genome Informatics Reports")
    (description
     "This package provides readers for easy and consistent importing of Mouse Genome
Informatics (MGI) report files:
<https://www.informatics.jax.org/downloads/reports/index.html>.  These data are
provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ, Mouse
Genome Informatics Group (2024) <doi:10.1093/genetics/iyae031>.")
    (license license:expat)))

(define-public r-mggd
  (package
    (name "r-mggd")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mggd" version))
       (sha256
        (base32 "07ajrhbsxr608688qppg6p2q7i3bh67fr6q9hvl9cs12av4bdznq"))))
    (properties `((upstream-name . "mggd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-mass r-lifecycle r-data-table))
    (home-page "https://forgemia.inra.fr/imhorphen/mggd")
    (synopsis
     "Multivariate Generalised Gaussian Distribution; Kullback-Leibler Divergence")
    (description
     "Distance between multivariate generalised Gaussian distributions, as presented
by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>.  Manipulation
of multivariate generalised Gaussian distributions (methods presented by Gomez,
Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal,
Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).")
    (license license:gpl3+)))

(define-public r-mgee2
  (package
    (name "r-mgee2")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgee2" version))
       (sha256
        (base32 "0p4601z2dyg2yjyjdcl0ry7fda89rjl6kilk8977z8p4n7d1kynj"))))
    (properties `((upstream-name . "mgee2")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-ggplot2))
    (home-page "https://cran.r-project.org/package=mgee2")
    (synopsis "Marginal Analysis of Misclassified Longitudinal Ordinal Data")
    (description
     "Three estimating equation methods are provided in this package for marginal
analysis of longitudinal ordinal data with misclassified responses and
covariates.  The naive analysis which is solely based on the observed data
without adjustment may lead to bias.  The corrected generalized estimating
equations (GEE2) method which is unbiased requires the misclassification
parameters to be known beforehand.  The corrected generalized estimating
equations (GEE2) with validation subsample method estimates the
misclassification parameters based on a given validation set.  This package is
an implementation of Chen (2013) <doi:10.1002/bimj.201200195>.")
    (license license:gpl2+)))

(define-public r-mgdrive2
  (package
    (name "r-mgdrive2")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGDrivE2" version))
       (sha256
        (base32 "1n7kmn65v6fb372jyqcsqnn01xvwyascqn881avd2iclrajr6h7p"))))
    (properties `((upstream-name . "MGDrivE2")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-matrix r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://marshalllab.github.io/MGDrivE/docs_v2/index.html")
    (synopsis "Mosquito Gene Drive Explorer 2")
    (description
     "This package provides a simulation modeling framework which significantly
extends capabilities from the MG@code{DrivE} simulation package via a new
mathematical and computational framework based on stochastic Petri nets.  For
more information about MG@code{DrivE}', see our publication: SÃ¡nchez et al.
(2019) <doi:10.1111/2041-210X.13318> Some of the notable capabilities of
MG@code{DrivE2} include: incorporation of human populations, epidemiological
dynamics, time-varying parameters, and a continuous-time simulation framework
with various sampling algorithms for both deterministic and stochastic
interpretations.  MG@code{DrivE2} relies on the genetic inheritance structures
provided in package MG@code{DrivE}', so we suggest installing that package
initially.")
    (license license:gpl3)))

(define-public r-mgdrive
  (package
    (name "r-mgdrive")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGDrivE" version))
       (sha256
        (base32 "0ynn7iwxi5yfpjyrvfr6ym4ns7h8qfy8c10clab7i72pv90jqqlr"))))
    (properties `((upstream-name . "MGDrivE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpp r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://marshalllab.github.io/MGDrivE/")
    (synopsis "Mosquito Gene Drive Explorer")
    (description
     "This package provides a model designed to be a reliable testbed where various
gene drive interventions for mosquito-borne diseases control.  It is being
developed to accommodate the use of various mosquito-specific gene drive systems
within a population dynamics framework that allows migration of individuals
between patches in landscape.  Previous work developing the population dynamics
can be found in Deredec et al. (2001) <doi:10.1073/pnas.1110717108> and Hancock
& Godfray (2007) <doi:10.1186/1475-2875-6-98>, and extensions to accommodate
CRISPR homing dynamics in Marshall et al. (2017)
<doi:10.1038/s41598-017-02744-7>.")
    (license license:gpl3)))

(define-public r-mgcviz
  (package
    (name "r-mgcviz")
    (version "0.1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgcViz" version))
       (sha256
        (base32 "14n9l10rcr9nar71lyw1qmk9xar2zixcx4s9rdnz5dr4jr7330y3"))))
    (properties `((upstream-name . "mgcViz")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-shiny
                             r-qgam
                             r-plyr
                             r-miniui
                             r-mgcv
                             r-matrixstats
                             r-kernsmooth
                             r-gridextra
                             r-ggplot2
                             r-ggally
                             r-gamm4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mfasiolo/mgcViz")
    (synopsis "Visualisations for Generalized Additive Models")
    (description
     "Extension of the mgcv package, providing visual tools for Generalized Additive
Models that exploit the additive structure of such models, scale to large data
sets and can be used in conjunction with a wide range of response distributions.
 The focus is providing visual methods for better understanding the model output
and for aiding model checking and development beyond simple exponential family
regression.  The graphical framework is based on the layering system provided by
ggplot2'.")
    (license license:gpl3)))

(define-public r-mgc
  (package
    (name "r-mgc")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgc" version))
       (sha256
        (base32 "1l9qs4nz1rn56qp569nh26yz3c7i4zh3r43c209zh7kvizpagx0i"))))
    (properties `((upstream-name . "mgc")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster r-mass r-energy r-boot r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/neurodata/r-mgc")
    (synopsis "Multiscale Graph Correlation")
    (description
     "Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al.
(2019) <DOI:10.7554/@code{eLife.41690>} that extends global correlation
procedures to be multiscale; consequently, MGC tests typically require far fewer
samples than existing methods for a wide variety of dependence structures and
dimensionalities, while maintaining computational efficiency.  Moreover, MGC
provides a simple and elegant multiscale characterization of the potentially
complex latent geometry underlying the relationship.")
    (license license:gpl2)))

(define-public r-mgbt
  (package
    (name "r-mgbt")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MGBT" version))
       (sha256
        (base32 "0wrw5yjaw3sgsw0l8q5gq95i5q1wxwgcffkxkxaa1cygblrrz12y"))))
    (properties `((upstream-name . "MGBT")))
    (build-system r-build-system)
    (home-page "https://doi.org/10.5066/P9CW9EF0")
    (synopsis "Multiple Grubbs-Beck Low-Outlier Test")
    (description
     "Compute the multiple Grubbs-Beck low-outlier test on positively distributed data
and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow
data processing discussed in Cohn et al. (2013) <doi:10.1002/wrcr.20392> and
England et al. (2017) <doi:10.3133/tm4B5>.")
    (license license:cc0)))

(define-public r-mgarchbekk
  (package
    (name "r-mgarchbekk")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mgarchBEKK" version))
       (sha256
        (base32 "0i5f8xghdch9d1cvfgiq2pscpq53ms7ik7jk16gss8i0cx2d3m94"))))
    (properties `((upstream-name . "mgarchBEKK")))
    (build-system r-build-system)
    (propagated-inputs (list r-tseries r-mvtnorm))
    (home-page "https://github.com/vst/mgarchBEKK/")
    (synopsis
     "Simulating, Estimating and Diagnosing MGARCH (BEKK and mGJR) Processes")
    (description
     "Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and
multivariate GJR (bivariate asymmetric GARCH model) specification.")
    (license license:gpl3)))

(define-public r-mg1stationaryprobability
  (package
    (name "r-mg1stationaryprobability")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MG1StationaryProbability" version))
       (sha256
        (base32 "151ygjpykc9jccfh6jhgywg82j006a8yqba6nvzhd1v9qb60yd4a"))))
    (properties `((upstream-name . "MG1StationaryProbability")))
    (build-system r-build-system)
    (propagated-inputs (list r-memoise r-foreach r-doparallel))
    (home-page "https://github.com/MashroomMole/MG1StationaryProbability")
    (synopsis "Computes Stationary Distribution for M/G/1 Queuing System")
    (description
     "The idea of a computational algorithm described in the article by Andronov M. et
al. (2022) <https://link.springer.com/chapter/10.1007/978-3-030-92507-9_13>.
The purpose of this package is to automate computations for a Markov-Modulated
M/G/1 queuing system with alternating Poisson flow of arrivals.  It offers a set
of functions to calculate various mean indices of the system, including mean
flow intensity, mean service busy and idle times, and the system's stationary
probability.")
    (license license:expat)))

(define-public r-mfx
  (package
    (name "r-mfx")
    (version "1.2-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mfx" version))
       (sha256
        (base32 "04pwp67i4sn4rf497pgy6qifwvrcmwyxn8x5sn96fy8qyrdzjfhj"))))
    (properties `((upstream-name . "mfx")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-mass r-lmtest r-betareg))
    (home-page "https://cran.r-project.org/package=mfx")
    (synopsis
     "Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs")
    (description
     "Estimates probit, logit, Poisson, negative binomial, and beta regression models,
returning their marginal effects, odds ratios, or incidence rate ratios as an
output.  Greene (2008, pp.  780-7) provides a textbook introduction to this
topic.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mfusampler
  (package
    (name "r-mfusampler")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MfUSampler" version))
       (sha256
        (base32 "0jzql2hs335gd4kwj18yi8ygsi3190wacfs3mgrmshdjb2frkh15"))))
    (properties `((upstream-name . "MfUSampler")))
    (build-system r-build-system)
    (propagated-inputs (list r-dlm r-coda r-ars))
    (home-page "https://cran.r-project.org/package=MfUSampler")
    (synopsis "Multivariate-from-Univariate (MfU) MCMC Sampler")
    (description
     "Convenience functions for multivariate MCMC using univariate samplers including:
slice sampler with stepout and shrinkage (Neal (2003)
<DOI:10.1214/aos/1056562461>), adaptive rejection sampler (Gilks and Wild (1992)
<DOI:10.2307/2347565>), adaptive rejection Metropolis (Gilks et al (1995)
<DOI:10.2307/2986138>), and univariate Metropolis with Gaussian proposal.")
    (license license:gpl2+)))

(define-public r-mft
  (package
    (name "r-mft")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MFT" version))
       (sha256
        (base32 "0n1xws3dw0650037qyqgp600p8cf098qa5hkbncdfdbl0w34qamy"))))
    (properties `((upstream-name . "MFT")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MFT")
    (synopsis "The Multiple Filter Test for Change Point Detection")
    (description
     "This package provides statistical tests and algorithms for the detection of
change points in time series and point processes - particularly for changes in
the mean in time series and for changes in the rate and in the variance in point
processes.  References - Michael Messer, Marietta Kirchner, Julia Schiemann,
Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test
for the detection of rate changes in renewal processes with varying variance
<doi:10.1214/14-AOAS782>.  Stefan Albert, Michael Messer, Julia Schiemann,
Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes
in renewal processes in the presence of rate change points
<doi:10.1111/jtsa.12254>.  Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby
Schneider (2017), Multi-scale detection of rate changes in spike trains with
weak dependencies <doi:10.1007/s10827-016-0635-3>.  Michael Messer, Stefan
Albert and Gaby Schneider (2018), The multiple filter test for change point
detection in time series <doi:10.1007/s00184-018-0672-1>.  Michael Messer,
Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in
time series.")
    (license license:gpl3)))

(define-public r-mfsis
  (package
    (name "r-mfsis")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MFSIS" version))
       (sha256
        (base32 "02bwnsyy5jrjmh50rd2cql8k9djkw6r0vi9rakfp86n853ir73fj"))))
    (properties `((upstream-name . "MFSIS")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-survival
                             r-reticulate
                             r-mass
                             r-foreach
                             r-dr
                             r-doparallel
                             r-crayon
                             r-cli
                             r-ball))
    (home-page "https://cran.r-project.org/package=MFSIS")
    (synopsis "Model-Free Sure Independent Screening Procedures")
    (description
     "An implementation of popular screening methods that are commonly employed in
ultra-high and high dimensional data.  Through this publicly available package,
we provide a unified framework to carry out model-free screening procedures
including SIS (Fan and Lv (2008) <doi:10.1111/j.1467-9868.2008.00674.x>),
SIRS(Zhu et al. (2011)<doi:10.1198/jasa.2011.tm10563>), DC-SIS (Li et al. (2012)
<doi:10.1080/01621459.2012.695654>), MDC-SIS(Shao and Zhang (2014)
<doi:10.1080/01621459.2014.887012>), Bcor-SIS (Pan et al. (2019)
<doi:10.1080/01621459.2018.1462709>), PC-Screen (Liu et al. (2020)
<doi:10.1080/01621459.2020.1783274>), WLS (Zhong et al.(2021)
<doi:10.1080/01621459.2021.1918554>), Kfilter (Mai and Zou (2015)
<doi:10.1214/14-AOS1303>), MVSIS (Cui et al. (2015)
<doi:10.1080/01621459.2014.920256>), PSIS (Pan et al. (2016)
<doi:10.1080/01621459.2014.998760>), CAS (Xie et al. (2020)
<doi:10.1080/01621459.2019.1573734>), CI-SIS (Cheng and Wang. (2023)
<doi:10.1016/j.cmpb.2022.107269>) and CSIS (Cheng et al. (2023)
<doi:10.1007/s00180-023-01399-5>).")
    (license license:gpl2+)))

(define-public r-mfpp
  (package
    (name "r-mfpp")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mfpp" version))
       (sha256
        (base32 "1z250grk06cmclyz4v3qnxdy43dm5vcrcq1rglpps3cfrg09i2g1"))))
    (properties `((upstream-name . "mfpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast
                             r-reshape2
                             r-pracma
                             r-nsga2r
                             r-igraph
                             r-ggplot2
                             r-genalg))
    (home-page "https://github.com/kzst/mfpp")
    (synopsis "'Matrix-Based Flexible Project Planning'")
    (description
     "Matrix-Based Flexible Project Planning.  This package models, plans, and
schedules flexible, such as agile, extreme, and hybrid project plans.  The
package contains project planning, scheduling, and risk assessment functions.
Kosztyan (2022) <doi:10.1016/j.softx.2022.100973>.")
    (license license:gpl2+)))

(define-public r-mfpca
  (package
    (name "r-mfpca")
    (version "1.3-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MFPCA" version))
       (sha256
        (base32 "0qpmy9cxskz4ii3qa5kvrdka4gbvlnch2blis6jjr4vic86a5mgi"))))
    (properties `((upstream-name . "MFPCA")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-plyr
                             r-mgcv
                             r-matrix
                             r-irlba
                             r-fundata
                             r-foreach
                             r-abind))
    (native-inputs (list pkg-config))
    (home-page "https://github.com/ClaraHapp/MFPCA")
    (synopsis
     "Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains")
    (description
     "Calculate a multivariate functional principal component analysis for data
observed on different dimensional domains.  The estimation algorithm relies on
univariate basis expansions for each element of the multivariate functional data
(Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>.  Multivariate and
univariate functional data objects are represented by S4 classes for this type
of data implemented in the package @code{funData}'.  For more details on the
general concepts of both packages and a case study, see Happ-Kurz (2020)
<doi:10.18637/jss.v093.i05>.")
    (license license:gpl2)))

(define-public r-mfp2
  (package
    (name "r-mfp2")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mfp2" version))
       (sha256
        (base32 "03xv7zjdcsnyrawwpjgfw5g59h7miwh68wkmbvfkd8wxnfcdmmmv"))))
    (properties `((upstream-name . "mfp2")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EdwinKipruto/mfp2")
    (synopsis "Multivariable Fractional Polynomial Models with Extensions")
    (description
     "Multivariable fractional polynomial algorithm simultaneously selects variables
and functional forms in both generalized linear models and Cox proportional
hazard models.  Key references for this algorithm are Royston and Altman
(1994)<doi:10.2307/2986270> and Sauerbrei and Royston (2008,
ISBN:978-0-470-02842-1).  In addition, it can model a sigmoid relationship
between variable x and an outcome variable y using the approximate cumulative
distribution transformation proposed by Royston (2014)
<doi:10.1177/1536867X1401400206>.  This feature distinguishes it from a standard
fractional polynomial function, which lacks the ability to achieve such
modeling.")
    (license license:gpl3)))

(define-public r-mfp
  (package
    (name "r-mfp")
    (version "1.5.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mfp" version))
       (sha256
        (base32 "17sww972ymnddbi54575hdalbshfq81m849795pmi38j3mrhz25m"))))
    (properties `((upstream-name . "mfp")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-numderiv))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mfp")
    (synopsis "Multivariable Fractional Polynomials")
    (description
     "Fractional polynomials are used to represent curvature in regression models.  A
key reference is Royston and Altman, 1994.")
    (license license:gpl2+)))

(define-public r-mfo
  (package
    (name "r-mfo")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MFO" version))
       (sha256
        (base32 "0i8zkdwmngdq21f1rmn1fmgsr525xfzyjd7nxwy8p7fns6hsq31r"))))
    (properties `((upstream-name . "MFO")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-readxl
                             r-openxlsx
                             r-minpack-lm
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/JorgeDelro/MFO")
    (synopsis "Maximal Fat Oxidation and Kinetics Calculation")
    (description
     "Calculate the maximal fat oxidation, the exercise intensity that elicits the
maximal fat oxidation and the SIN model to represent the fat oxidation kinetics.
 Three variables can be obtained from the SIN model: dilatation, symmetry and
translation.  Examples of these methods can be found in Montes de Oca et al
(2021) <doi:10.1080/17461391.2020.1788650> and CheneviÃ¨re et al. (2009)
<doi:10.1249/MSS.0b013e31819e2f91>.")
    (license license:expat)))

(define-public r-mflica
  (package
    (name "r-mflica")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mFLICA" version))
       (sha256
        (base32 "02h53v124j38scl816ci8z5wfcrmh9p6m08fk2hhw973h7c6xbmy"))))
    (properties `((upstream-name . "mFLICA")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/DarkEyes/mFLICA")
    (synopsis "Leadership-Inference Framework for Multivariate Time Series")
    (description
     "This package provides a leadership-inference framework for multivariate time
series.  The framework for multiple-faction-leadership inference from
coordinated activities or @code{mFLICA} uses a notion of a leader as an
individual who initiates collective patterns that everyone in a group follows.
Given a set of time series of individual activities, our goal is to identify
periods of coordinated activity, find factions of coordination if more than one
exist, as well as identify leaders of each faction.  For each time step, the
framework infers following relations between individual time series, then
identifying a leader of each faction whom many individuals follow but it follows
no one.  A faction is defined as a group of individuals that everyone follows
the same leader. @code{mFLICA} reports following relations, leaders of factions,
and members of each faction for each time step.  Please see Chainarong
Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62>
for methodology and Chainarong Amornbunchornvej (2021)
<doi:10.1016/j.softx.2021.100781> for software when referring to this package in
publications.")
    (license license:gpl3)))

(define-public r-mfilter
  (package
    (name "r-mfilter")
    (version "0.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mFilter" version))
       (sha256
        (base32 "0anc93mciq8j64knd0i38sfgv10w1gd375wnfzn1fq8sj3lby7kb"))))
    (properties `((upstream-name . "mFilter")))
    (build-system r-build-system)
    (home-page "http://www.mbalcilar.net")
    (synopsis "Miscellaneous Time Series Filters")
    (description
     "The @code{mFilter} package implements several time series filters useful for
smoothing and extracting trend and cyclical components of a time series.  The
routines are commonly used in economics and finance, however they should also be
interest to other areas.  Currently, Christiano-Fitzgerald, Baxter-King,
Hodrick-Prescott, Butterworth, and trigonometric regression filters are included
in the package.")
    (license license:gpl2+)))

(define-public r-mfgarch
  (package
    (name "r-mfgarch")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mfGARCH" version))
       (sha256
        (base32 "1pcabijwhdxhdlrdjvi2zl8la939igsdy3j1yv0ic8wvkpw1sh4x"))))
    (properties `((upstream-name . "mfGARCH")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-rcpp r-numderiv r-maxlik))
    (home-page "https://github.com/onnokleen/mfGARCH/")
    (synopsis "Mixed-Frequency GARCH Models")
    (description
     "Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013,
<doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the
paper \"Two are better than one: Volatility forecasting using multiplicative
component GARCH models\" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>).  The
GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns
into a short- and long-term component, where the latter may depend on an
exogenous covariate sampled at a lower frequency.")
    (license license:expat)))

(define-public r-mfdp
  (package
    (name "r-mfdp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mFDP" version))
       (sha256
        (base32 "0mvqg3qpv1zcj5lsk36dw4qs75i66cmv4qazpmsilhfh0v46p2dx"))))
    (properties `((upstream-name . "mFDP")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mFDP")
    (synopsis "Flexible Control of the mFDP")
    (description
     "Computes bounds for the median of the false discovery proportion (@code{mFDP}).
These 50 percent confidence bounds for the FDP are simultaneously valid.  The
method takes a vector of p-values as input.  Also provides @code{mFDP-adjusted}
p-values.  Can be used for flexible @code{mFDP} control.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mfdfa
  (package
    (name "r-mfdfa")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MFDFA" version))
       (sha256
        (base32 "0nr78p6y4jpg1hrl0h7b7hyvwbr61paf85y3f2dx815x5hscmdhy"))))
    (properties `((upstream-name . "MFDFA")))
    (build-system r-build-system)
    (propagated-inputs (list r-numbers))
    (home-page "https://mlaib.github.io")
    (synopsis "MultiFractal Detrended Fluctuation Analysis")
    (description
     "This package contains the @code{MultiFractal} Detrended Fluctuation Analysis
(MFDFA), @code{MultiFractal} Detrended Cross-Correlation Analysis (MFXDFA), and
the Multiscale Multifractal Analysis (MMA).  The MFDFA() function proposed in
this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and
<doi:10.1063/1.5022737>).  See references for more information.  Interested
users can find a parallel version of the MFDFA() function on @code{GitHub}.")
    (license license:gpl3)))

(define-public r-mfdb
  (package
    (name "r-mfdb")
    (version "7.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mfdb" version))
       (sha256
        (base32 "1l7bvacmvh53rq91jqdb6zqjn3974sy838zfvgfb6nam54r080y4"))))
    (properties `((upstream-name . "mfdb")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite
                             r-rpostgres
                             r-rlang
                             r-logging
                             r-getpass
                             r-duckdb
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mfdb")
    (synopsis "MareFrame DB Querying Library")
    (description
     "This package creates and manages a @code{PostgreSQL} database suitable for
storing fisheries data and aggregating ready for use within a Gadget
<https://gadget-framework.github.io/gadget2/> model.  See
<https://mareframe.github.io/mfdb/> for more information.")
    (license license:gpl3)))

(define-public r-mfd
  (package
    (name "r-mfd")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mFD" version))
       (sha256
        (base32 "1jr5d5669mz4ifacaghnj7109ypfrikagjgxi2a33iqj0g63x3hp"))))
    (properties `((upstream-name . "mFD")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-rstatix
                             r-reshape2
                             r-patchwork
                             r-hmisc
                             r-ggrepel
                             r-ggplot2
                             r-geometry
                             r-gawdis
                             r-factominer
                             r-dendextend
                             r-cluster
                             r-betapart
                             r-ape
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://cmlmagneville.github.io/mFD/")
    (synopsis
     "Compute and Illustrate the Multiple Facets of Functional Diversity")
    (description
     "Computing functional traits-based distances between pairs of species for species
gathered in assemblages allowing to build several functional spaces.  The
package allows to compute functional diversity indices assessing the
distribution of species (and of their dominance) in a given functional space for
each assemblage and the overlap between assemblages in a given functional space,
see: Chao et al. (2018) <doi:10.1002/ecm.1343>, Maire et al. (2015)
<doi:10.1111/geb.12299>, Mouillot et al. (2013)
<doi:10.1016/j.tree.2012.10.004>, Mouillot et al. (2014)
<doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009)
<doi:10.1016/j.tpb.2009.10.001>.  Graphical outputs are included.  Visit the
@code{mFD} website for more information, documentation and examples.")
    (license license:gpl2)))

(define-public r-mfag
  (package
    (name "r-mfag")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MFAg" version))
       (sha256
        (base32 "1lciq1drbd2p71k1svn4a4q7p0j3am9nsd4nmvmwikpihyqswgfv"))))
    (properties `((upstream-name . "MFAg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MFAg")
    (synopsis "Multiple Factor Analysis (MFA)")
    (description
     "This package performs Multiple Factor Analysis method for quantitative,
categorical, frequency and mixed data, in addition to generating a lot of
graphics, also has other useful functions.")
    (license license:gpl3)))

(define-public r-mfaces
  (package
    (name "r-mfaces")
    (version "0.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mfaces" version))
       (sha256
        (base32 "0svvnpka2y1p21gvdyc7csw6m0g3kziszw2ll9bq5gpcnl69qk4n"))))
    (properties `((upstream-name . "mfaces")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-matrixcalc r-matrix r-face))
    (home-page "https://cran.r-project.org/package=mfaces")
    (synopsis
     "Fast Covariance Estimation for Multivariate Sparse Functional Data")
    (description
     "Multivariate functional principal component analysis via fast covariance
estimation for multivariate sparse functional data or longitudinal data proposed
by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.")
    (license license:gpl3)))

(define-public r-mf-beta4
  (package
    (name "r-mf-beta4")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MF.beta4" version))
       (sha256
        (base32 "13k9jqspg42hgis0svad76yvzb8bh5cm4qynkpn0ga0an2lww6gs"))))
    (properties `((upstream-name . "MF.beta4")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tidyr
                             r-purrr
                             r-patchwork
                             r-lmertest
                             r-lme4
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-devtools
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AnneChao/MF.beta4")
    (synopsis "Measuring Ecosystem Multi-Functionality and Its Decomposition")
    (description
     "Provide simple functions to (i) compute a class of multi-functionality measures
for a single ecosystem for given function weights, (ii) decompose gamma
multi-functionality for multiple ecosystems into a within-ecosystem component
(alpha multi-functionality) and an among-ecosystem component (beta
multi-functionality).  In each case, the correlation between functions can be
corrected for.  Based on biodiversity and ecosystem function data, this software
also facilitates graphics for assessing biodiversity-ecosystem functioning
relationships across scales.")
    (license license:gpl3+)))

(define-public r-mexplorer
  (package
    (name "r-mexplorer")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mExplorer" version))
       (sha256
        (base32 "1563d9r7f25vw5vsph21z3dwz9n40dpaiyx28ay1bbp5i1x2l3rb"))))
    (properties `((upstream-name . "mExplorer")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet))
    (home-page "https://cran.r-project.org/package=mExplorer")
    (synopsis
     "Identifying Master Gene Regulators from Gene Expression and DNA-Binding Data")
    (description
     "The method m:Explorer associates a given list of target genes (e.g. those
involved in a biological process) to gene regulators such as transcription
factors.  Transcription factors that bind DNA near significantly many target
genes or correlate with target genes in transcriptional (microarray or RNAseq
data) are selected.  Selection of candidate master regulators is carried out
using multinomial regression models, likelihood ratio tests and multiple testing
correction.  Reference: m:Explorer: multinomial regression models reveal
positive and negative regulators of longevity in yeast quiescence.  Juri
Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M
Luscombe.  Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.")
    (license license:gpl2+)))

(define-public r-mexicolors
  (package
    (name "r-mexicolors")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mexicolors" version))
       (sha256
        (base32 "0vbhwaq19hqd63qjybssgqxkawjr6vnar6k0nrb583hh1q9hjyg2"))))
    (properties `((upstream-name . "mexicolors")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mexicolors")
    (synopsis "Mexican Politics-Inspired Color Palette Generator")
    (description
     "This package provides a color palette generator inspired by Mexican politics,
with colors ranging from red on the left to gray in the middle and green on the
right.  Palette options range from only a few colors to several colors, but with
discrete and continuous options to offer greatest flexibility to the user.  This
package allows for a range of applications, from mapping brief discrete scales
(e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays
including dozens of shades graded from red to green.")
    (license license:gpl2+)))

(define-public r-mexhaz
  (package
    (name "r-mexhaz")
    (version "2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mexhaz" version))
       (sha256
        (base32 "160ilpb6zydjpj82nhbh0wha71cmmvyw7wmhqfvj3jbli22vyn8q"))))
    (properties `((upstream-name . "mexhaz")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-statmod r-numderiv r-mass r-lamw))
    (home-page "https://cran.r-project.org/package=mexhaz")
    (synopsis "Mixed Effect Excess Hazard Models")
    (description
     "Fit flexible (excess) hazard regression models with the possibility of including
non-proportional effects of covariables and of adding a random effect at the
cluster level (corresponding to a shared frailty).  A detailed description of
the package functionalities is provided in Charvat and Belot (2021) <doi:
10.18637/jss.v098.i14>.")
    (license license:gpl2+)))

(define-public r-mexbrewer
  (package
    (name "r-mexbrewer")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MexBrewer" version))
       (sha256
        (base32 "04r1hylym4pxlxy7ldd185bczd398f8a757a9yy6f86lg4vrqyhq"))))
    (properties `((upstream-name . "MexBrewer")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/paezha/MexBrewer")
    (synopsis
     "Color Palettes Inspired by Works of Mexican Painters and Muralists")
    (description
     "Color palettes inspired by the works of Mexican painters and muralists.  The
package includes functions that return vectors of colors and also functions to
use color and fill scales in ggplot2 visualizations.")
    (license license:expat)))

(define-public r-mewavg
  (package
    (name "r-mewavg")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mewAvg" version))
       (sha256
        (base32 "1pnjzfq4rdjjlqp1mxxlpkhkfgq1nym8pgaqrvpm76zw4xnx7p7m"))))
    (properties `((upstream-name . "mewAvg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mewAvg")
    (synopsis "Fixed Memeory Moving Expanding Window Average")
    (description
     "Compute the average of a sequence of random vectors in a moving expanding window
using a fixed amount of memory.")
    (license license:gpl2+)))

(define-public r-mevr
  (package
    (name "r-mevr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mevr" version))
       (sha256
        (base32 "107gp0lbgy4hfi38x07h4z8sf1phkywqk58hfr2d97dil4mnwlz0"))))
    (properties `((upstream-name . "mevr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-mgcv
                             r-foreach
                             r-envstats
                             r-dplyr
                             r-doparallel
                             r-bamlss))
    (home-page "https://cran.r-project.org/package=mevr")
    (synopsis "Fitting the Metastatistical Extreme Value Distribution MEVD")
    (description
     "Extreme value analysis with the metastatistical extreme value distribution MEVD
(Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of
its variants.  In particular, analysis can be performed with the simplified
metastatistical extreme value distribution SMEV (Marra et al., 2019,
<doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme
value distribution TMEV (Falkensteiner et al., 2023,
<doi:10.1016/j.wace.2023.100601>).  Parameters can be estimated with probability
weighted moments, maximum likelihood and least squares.  The data can also be
left-censored prior to a fit.  Density, distribution function, quantile function
and random generation for the MEVD, SMEV and TMEV are included.  In addition,
functions for the calculation of return levels including confidence intervals
are provided.  For a description of use cases please see the provided
references.")
    (license license:gpl3)))

(define-public r-mev
  (package
    (name "r-mev")
    (version "1.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mev" version))
       (sha256
        (base32 "0gzi48kwp6hb9p28km43kf76r55cbqgmjkfn8nylhkah7zx1gvxc"))))
    (properties `((upstream-name . "mev")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-rcpparmadillo r-rcpp r-nleqslv
                             r-alabama))
    (native-inputs (list r-knitr))
    (home-page "https://lbelzile.github.io/mev/")
    (synopsis "Modelling of Extreme Values")
    (description
     "Various tools for the analysis of univariate, multivariate and functional
extremes.  Exact simulation from max-stable processes [Dombry, Engelke and
Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various
parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014,
<doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015,
<doi:10.1093/biomet/asv045>).  Threshold selection methods, including Wadsworth
(2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014)
<doi:10.1007/s10687-014-0183-z>.  Multivariate extreme diagnostics.  Estimation
and likelihoods for univariate extremes, e.g., Coles (2001)
<doi:10.1007/978-1-4471-3675-0>.")
    (license license:gpl3)))

(define-public r-metsyn
  (package
    (name "r-metsyn")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metsyn" version))
       (sha256
        (base32 "0iwp0nz07yd33qd93fjblmvik1l1xkv6rkccn3054zs2bkrzjq4f"))))
    (properties `((upstream-name . "metsyn")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr r-readr r-foreach))
    (home-page "https://github.com/paulponcet/metsyn")
    (synopsis "Interface with the Meteo France Synop Data API")
    (description
     "This package provides an interface with the Meteo France Synop data API (see
<https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32>
for more information).  The Meteo France Synop data are made of meteorological
data recorded every three hours on 62 French meteorological stations.")
    (license license:expat)))

(define-public r-metsizer
  (package
    (name "r-metsizer")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetSizeR" version))
       (sha256
        (base32 "0blfdcd8z6qbaz47q08arb94wh3cp689w7vsc3jvn3cqq7r35byl"))))
    (properties `((upstream-name . "MetSizeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vroom
                             r-shinythemes
                             r-shiny
                             r-rfast
                             r-metabolanalyze
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MetSizeR")
    (synopsis
     "Shiny App for Sample Size Estimation in Metabolomic Experiments")
    (description
     "This package provides a Shiny application to estimate the sample size required
for a metabolomic experiment to achieve a desired statistical power.  Estimation
is possible with or without available data from a pilot study.")
    (license license:gpl3+)))

(define-public r-mets
  (package
    (name "r-mets")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mets" version))
       (sha256
        (base32 "0yisa6v0apxxw5ycjhc3hcghlmvzzaq431z8nzsrw5jc2g13c0ca"))))
    (properties `((upstream-name . "mets")))
    (build-system r-build-system)
    (propagated-inputs (list r-timereg
                             r-survival
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-mvtnorm
                             r-lava))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://kkholst.github.io/mets/")
    (synopsis "Analysis of Multivariate Event Times")
    (description
     "Implementation of various statistical models for multivariate event history data
<doi:10.1007/s10985-013-9244-x>.  Including multivariate cumulative incidence
models <doi:10.1002/sim.6016>, and bivariate random effects probit models
(Liability models) <doi:10.1016/j.csda.2015.01.014>.  Modern methods for
survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin,
Binomial regression) with fast computation of influence functions.")
    (license license:gpl2+)))

(define-public r-metropolis
  (package
    (name "r-metropolis")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metropolis" version))
       (sha256
        (base32 "031fcy0v13bvy7gg26988fvpqdsfndx2rynkhb1jvirnrf0fngy4"))))
    (properties `((upstream-name . "metropolis")))
    (build-system r-build-system)
    (propagated-inputs (list r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metropolis")
    (synopsis "The Metropolis Algorithm")
    (description
     "Learning and using the Metropolis algorithm for Bayesian fitting of a
generalized linear model.  The package vignette includes examples of hand-coding
a logistic model using several variants of the Metropolis algorithm.  The
package also contains R functions for simulating posterior distributions of
Bayesian generalized linear model parameters using guided, adaptive,
guided-adaptive and random walk Metropolis algorithms.  The random walk
Metropolis algorithm was originally described in Metropolis et al (1953);
<doi:10.1063/1.1699114>.")
    (license license:gpl2+)))

(define-public r-metrology
  (package
    (name "r-metrology")
    (version "0.9-28-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metRology" version))
       (sha256
        (base32 "1syjwblyd18myxrs0hx4m91fgb6zs3r4g7w701j2f2pw6j9mvz0y"))))
    (properties `((upstream-name . "metRology")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-numderiv r-mass))
    (home-page "https://r-forge.r-project.org/projects/metrology/")
    (synopsis "Support for Metrological Applications")
    (description
     "This package provides classes and calculation and plotting functions for
metrology applications, including measurement uncertainty estimation and
inter-laboratory metrology comparison studies.")
    (license license:gpl2+)))

(define-public r-metro
  (package
    (name "r-metro")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metro" version))
       (sha256
        (base32 "1dm759z7rb86fnqmrhzbnljhq9v60w8gspy09zkzjdfjzn06q17d"))))
    (properties `((upstream-name . "metro")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-jsonlite r-httr r-hms r-geodist))
    (home-page "https://k5cents.github.io/metro/")
    (synopsis "Washington Metropolitan Area Transit Authority API")
    (description
     "The Washington Metropolitan Area Transit Authority is a government agency
operating light rail and passenger buses in the Washington D.C. area.  With a
free developer account, access their Metro Transparent Data Sets API
<https://developer.wmata.com/> to return data frames of transit data for easy
analysis.")
    (license license:gpl3+)))

(define-public r-metrix
  (package
    (name "r-metrix")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metrix" version))
       (sha256
        (base32 "0ixp868pag05a6h4l8q4x55w0dg4xki4q6klfi9dzx82knzb7s9l"))))
    (properties `((upstream-name . "metrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-stringr r-matrix))
    (home-page "https://cran.r-project.org/package=metrix")
    (synopsis "Water Quality Metrics Calculator")
    (description
     "Calculate different metrics based on aquatic macroinvertebrate density data
(individuals per square meter) to assess water quality (Prat N et al.  2009).")
    (license license:gpl3+)))

(define-public r-metricsweighted
  (package
    (name "r-metricsweighted")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetricsWeighted" version))
       (sha256
        (base32 "084zvfh66daz9g7cwn4bzx3cvydzwf83r2lzy6xga7qd4d89q0j6"))))
    (properties `((upstream-name . "MetricsWeighted")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mayer79/MetricsWeighted")
    (synopsis "Weighted Metrics and Performance Measures for Machine Learning")
    (description
     "This package provides weighted versions of several metrics and performance
measures used in machine learning, including average unit deviances of the
Bernoulli, Tweedie, Poisson, and Gamma distributions, see Jorgensen B. (1997,
ISBN: 978-0412997112).  The package also contains a weighted version of
generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236).
Furthermore, dplyr chains are supported.")
    (license license:gpl2+)))

(define-public r-metricminer
  (package
    (name "r-metricminer")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metricminer" version))
       (sha256
        (base32 "156fzmcwgabi95qdjy7pq415l505y5mzcc99l77ykbf56w9fa9qs"))))
    (properties `((upstream-name . "metricminer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-purrr
                             r-openssl
                             r-lubridate
                             r-jsonlite
                             r-janitor
                             r-httr
                             r-googlesheets4
                             r-googledrive
                             r-gh
                             r-getpass
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fhdsl/metricminer")
    (synopsis "Mine Metrics from Common Places on the Web")
    (description
     "Mine metrics on common places on the web through the power of their APIs
(application programming interfaces).  It also helps make the data in a format
that is easily used for a dashboard or other purposes.  There is an associated
dashboard template and tutorials that are underdevelopment that help you fully
utilize metricminer'.")
    (license license:gpl3)))

(define-public r-metricgraph
  (package
    (name "r-metricgraph")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetricGraph" version))
       (sha256
        (base32 "1pd10pxqvdvqh1jml6rpn2j5w6j0hw87wd9f272yfwd4q9d2c29r"))))
    (properties `((upstream-name . "MetricGraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-sp
                             r-sf
                             r-rspde
                             r-rcppeigen
                             r-rcpp
                             r-r6
                             r-matrix
                             r-magrittr
                             r-lifecycle
                             r-igraph
                             r-ggplot2
                             r-ggnewscale
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://davidbolin.github.io/MetricGraph/")
    (synopsis "Random Fields on Metric Graphs")
    (description
     "Facilitates creation and manipulation of metric graphs, such as street or river
networks.  Further facilitates operations and visualizations of data on metric
graphs, and the creation of a large class of random fields and stochastic
partial differential equations on such spaces.  These random fields can be used
for simulation, prediction and inference.  In particular, linear mixed effects
models including random field components can be fitted to data based on
computationally efficient sparse matrix representations.  Interfaces to the R
packages INLA and inlabru are also provided, which facilitate working with
Bayesian statistical models on metric graphs.  The main references for the
methods are Bolin, Simas and Wallin (2024) <doi:10.3150/23-BEJ1647>, Bolin,
Kovacs, Kumar and Simas (2023) <doi:10.1090/mcom/3929> and Bolin, Simas and
Wallin (2023) <doi:10.48550/@code{arXiv.2304.03190>} and
<doi:10.48550/@code{arXiv.2304.10372>}.")
    (license license:gpl2+)))

(define-public r-metrica
  (package
    (name "r-metrica")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metrica" version))
       (sha256
        (base32 "1hb7m0adz3a31c0rqjfpz8lincsclcpn02jhn0idpbppp86h6pck"))))
    (properties `((upstream-name . "metrica")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rsqlite
                             r-rlang
                             r-minerva
                             r-ggpp
                             r-ggplot2
                             r-energy
                             r-dplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://adriancorrendo.github.io/metrica/")
    (synopsis "Prediction Performance Metrics")
    (description
     "This package provides a compilation of more than 80 functions designed to
quantitatively and visually evaluate prediction performance of regression
(continuous variables) and classification (categorical variables) of
point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning).
For regression, it includes functions to generate plots (scatter, tiles,
density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE,
RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency
(e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g.
r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric
regression coefficients (intercept, slope), and mean absolute scaled error
(MASE) for time series predictions.  For classification (binomial and
multinomial), it offers functions to generate and plot confusion matrices, and
to estimate performance metrics such as accuracy, precision, recall,
specificity, F-score, Cohen's Kappa, G-mean, and many more.  For more details
visit the vignettes <https://adriancorrendo.github.io/metrica/>.")
    (license license:expat)))

(define-public r-metr
  (package
    (name "r-metr")
    (version "0.16.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metR" version))
       (sha256
        (base32 "0pimgvgxq494qlh339hq0v38y444i9syc46vl4r071bv15nar5dp"))))
    (properties `((upstream-name . "metR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sf
                             r-scales
                             r-purrr
                             r-plyr
                             r-memoise
                             r-lubridate
                             r-isoband
                             r-gtable
                             r-ggplot2
                             r-formula-tools
                             r-formula
                             r-digest
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://eliocamp.github.io/metR/")
    (synopsis "Tools for Easier Analysis of Meteorological Fields")
    (description
     "Many useful functions and extensions for dealing with meteorological data in the
tidy data framework.  Extends ggplot2 for better plotting of scalar and vector
fields and provides commonly used analysis methods in the atmospheric sciences.")
    (license license:gpl3)))

(define-public r-metproc
  (package
    (name "r-metproc")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetProc" version))
       (sha256
        (base32 "1z6538pij1rq5wv9s8rjcn0sck15y26asv4vamav8a6s4135ia3q"))))
    (properties `((upstream-name . "MetProc")))
    (build-system r-build-system)
    (propagated-inputs (list r-gplots r-fastcluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MetProc")
    (synopsis
     "Separate Metabolites into Likely Measurement Artifacts and True Metabolites")
    (description
     "Split an untargeted metabolomics data set into a set of likely true metabolites
and a set of likely measurement artifacts.  This process involves comparing
missing rates of pooled plasma samples and biological samples.  The functions
assume a fixed injection order of samples where biological samples are
randomized and processed between intermittent pooled plasma samples.  By
comparing patterns of missing data across injection order, metabolites that
appear in blocks and are likely artifacts can be separated from metabolites that
seem to have random dispersion of missing data.  The two main metrics used are:
1.  the number of consecutive blocks of samples with present data and 2.  the
correlation of missing rates between biological samples and flanking pooled
plasma samples.")
    (license license:gpl2+)))

(define-public r-metools
  (package
    (name "r-metools")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metools" version))
       (sha256
        (base32 "05l49ifx2nnlbdxh2qhdb0islf95fc67720rm5in7mrlida4khnq"))))
    (properties `((upstream-name . "metools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-scales
                             r-lubridate
                             r-ggplot2))
    (home-page "https://metoolsr.wordpress.com")
    (synopsis "Macroeconomics Tools")
    (description
     "This package provides a number of functions to facilitate the handling and
production of reports using time series data.  The package was developed to be
understandable for beginners, so some functions aim to transform processes that
would be complex into functions with a few lines.  The main advantage of using
the metools package is the ease of producing reports and working with time
series using a few lines of code, so the code is clean and easy to
understand/maintain.  Learn more about the metools at
<https://metoolsr.wordpress.com>.")
    (license license:gpl3)))

(define-public r-meto
  (package
    (name "r-meto")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MeTo" version))
       (sha256
        (base32 "0zn63qljsw3bnxpj823lrkcl3rnxksjsaxzn6dbddw41j80mmfa4"))))
    (properties `((upstream-name . "MeTo")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate))
    (home-page "https://cran.r-project.org/package=MeTo")
    (synopsis "Meteorological Tools")
    (description
     "Meteorological Tools following the FAO56 irrigation paper of Allen et al. (1998)
[1].  Functions for calculating: reference evapotranspiration (ETref),
extraterrestrial radiation (Ra), net radiation (Rn), saturation vapor pressure
(@code{satVP}), global radiation (Rs), soil heat flux (G), daylight hours, and
more. [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998).  Crop
evapotranspiration-Guidelines for computing crop water requirements-FAO
Irrigation and drainage paper 56.  FAO, Rome, 300(9).")
    (license license:gpl2+)))

(define-public r-methodopt
  (package
    (name "r-methodopt")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MethodOpt" version))
       (sha256
        (base32 "09v4ixrfjk7bz1zm29g1fzsdq5xl1q9jc73kyk5f4dh4k6936686"))))
    (properties `((upstream-name . "MethodOpt")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-zip
                             r-tibble
                             r-shinyjs
                             r-shinyfeedback
                             r-shinybs
                             r-shinyalert
                             r-shiny
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-htmltools
                             r-gtools
                             r-glue
                             r-ggplot2
                             r-frf2
                             r-dt
                             r-dplyr
                             r-doe-wrapper))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MethodOpt")
    (synopsis
     "Advanced Method Optimization for Spectra-Generating Sampling and Analysis Instrumentation")
    (description
     "This package provides a graphical user interface to apply an advanced method
optimization algorithm to various sampling and analysis instruments.  This
includes generating experimental designs, uploading and viewing data, and
performing various analyses to determine the optimal method.  Details of the
techniques used in this package are published in Gamble, Granger, & Mannion
(2024) <doi:10.1021/acs.analchem.3c05763>.")
    (license license:gpl3+)))

(define-public r-methodcompare
  (package
    (name "r-methodcompare")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MethodCompare" version))
       (sha256
        (base32 "10i9bxjsd7gdcdmrlyl1qymrhaikcb9jpzqi2hnnwcjaf0p1hfkr"))))
    (properties `((upstream-name . "MethodCompare")))
    (build-system r-build-system)
    (propagated-inputs (list r-rockchalk r-mfp r-matrix r-lme4 r-estimatr))
    (home-page "https://github.com/UBERLULU/MethodCompare")
    (synopsis "Evaluating Bias and Precision in Method Comparison Studies")
    (description
     "Evaluate bias and precision in method comparison studies.  One provides
measurements for each method and it takes care of the estimates.  Multiple plots
to evaluate bias, precision and compare methods.")
    (license license:gpl3+)))

(define-public r-methevolsim
  (package
    (name "r-methevolsim")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MethEvolSIM" version))
       (sha256
        (base32 "18sgbmbkhk4q2wf95j80sgbn9zcs665y2r2wmghyisnvyf5i1cqj"))))
    (properties `((upstream-name . "MethEvolSIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MethEvolSIM")
    (synopsis
     "Simulate DNA Methylation Dynamics on Different Genomic Structures along Genealogies")
    (description
     "DNA methylation is an epigenetic modification involved in genomic stability,
gene regulation, development and disease.  DNA methylation occurs mainly through
the addition of a methyl group to cytosines, for example to cytosines in a
@code{CpG} dinucleotide context (@code{CpG} stands for a cytosine followed by a
guanine).  Tissue-specific methylation patterns lead to genomic regions with
different characteristic methylation levels.  E.g.  in vertebrates @code{CpG}
islands (regions with high @code{CpG} content) that are associated to promoter
regions of expressed genes tend to be unmethylated. @code{MethEvolSIM} is a
model-based simulation software for the generation and modification of cytosine
methylation patterns along a given tree, which can be a genealogy of cells
within an organism, a coalescent tree of DNA sequences sampled from a
population, or a species tree.  The simulations are based on an extension of the
model of Grosser & Metzler (2020) <doi:10.1186/s12859-020-3438-5> and allows for
changes of the methylation states at single cytosine positions as well as
simultaneous changes of methylation frequencies in genomic structures like
@code{CpG} islands.")
    (license license:gpl3+)))

(define-public r-methcon5
  (package
    (name "r-methcon5")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "methcon5" version))
       (sha256
        (base32 "08xsi6j65izxqcrlh37f2d7m3pwna1v6zvdv96mqylsjqgqmbk5x"))))
    (properties `((upstream-name . "methcon5")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-purrr r-magrittr r-dplyr))
    (home-page "https://github.com/EmilHvitfeldt/methcon5")
    (synopsis
     "Identify and Rank CpG DNA Methylation Conservation Along the Human Genome")
    (description
     "Identify and rank @code{CpG} DNA methylation conservation along the human
genome.  Specifically it includes bootstrapping methods to provide ranking which
should adjust for the differences in length as without it short regions tend to
get higher conservation scores.")
    (license license:expat)))

(define-public r-methcomp
  (package
    (name "r-methcomp")
    (version "1.30.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MethComp" version))
       (sha256
        (base32 "1vmwb4zw4gm2x0j2nzm2vjfj7x19ws8njr6dcglzjpbbha7nxjp6"))))
    (properties `((upstream-name . "MethComp")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags r-nlme r-coda))
    (home-page "http://BendixCarstensen.com/MethComp/")
    (synopsis "Analysis of Agreement in Method Comparison Studies")
    (description
     "This package provides methods (standard and advanced) for analysis of agreement
between measurement methods.  These cover Bland-Altman plots, Deming regression,
Lin's Total deviation index, and difference-on-average regression.  See
Carstensen B. (2010) \"Comparing Clinical Measurement Methods: A Practical Guide
(Statistics in Practice)\" <doi:10.1002/9780470683019> for more information.")
    (license license:gpl2+)))

(define-public r-metgen
  (package
    (name "r-metgen")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetGen" version))
       (sha256
        (base32 "00yzaicrpwfnxi5mdzwx1ai721vczkkf2yl50q9wwpxhmfx0xbbh"))))
    (properties `((upstream-name . "MetGen")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-glmnet r-chron))
    (home-page "www.r-project.org")
    (synopsis "Stochastic Weather Generator")
    (description
     "An adaptation of the multi-variable stochastic weather generator proposed in
Rglimclim to perform gap-filling and temporal extension at sub-daily resolution.
 Simulation is performed based on large scale variables and climatic observation
data that could be generated from different gauged stations having geographical
proximity.  SWG relies on reanalyses.  Multi-variable dependence is taking into
account by using the decomposition of the product rule (in statistics) into
conditional probabilities.  See <https://hal.archives-ouvertes.fr/hal-02554676>.")
    (license license:gpl2+)))

(define-public r-metevalue
  (package
    (name "r-metevalue")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metevalue" version))
       (sha256
        (base32 "1iazpldafzq1rzg1hbp9yyqwngq00mbxmfahidlj3ybl1y459lah"))))
    (properties `((upstream-name . "metevalue")))
    (build-system r-build-system)
    (propagated-inputs (list r-sqldf r-psych r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metevalue")
    (synopsis "E-Value in the Omics Data Association Studies")
    (description
     "In the omics data association studies, it is common to conduct the p-value
corrections to control the false significance.  Beyond the P-value corrections,
E-value is recently studied to facilitate multiple testing correction based on
V. Vovk and R. Wang (2021) <doi:10.1214/20-AOS2020>.  This package provides
E-value calculation for DNA methylation data and RNA-seq data.  Currently, five
data formats are supported: DNA methylation levels using DMR detection tools
(@code{BiSeq}, DMRfinder, @code{MethylKit}, Metilene and other DNA methylation
tools) and RNA-seq data.  The relevant references are listed below: Katja
Hebestreit and Hans-Ulrich Klein (2022) <doi:10.18129/B9.bioc.@code{BiSeq>};
Altuna Akalin et.al (2012) <doi:10.18129/B9.bioc.@code{methylKit>}.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-meter
  (package
    (name "r-meter")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteR" version))
       (sha256
        (base32 "02637d3dnfq0jv9d74y99x7ms89y3jcmkpazc44g44sa7jx4i510"))))
    (properties `((upstream-name . "meteR")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv r-distr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cmerow/meteR")
    (synopsis
     "Fitting and Plotting Tools for the Maximum Entropy Theory of Ecology (METE)")
    (description
     "Fit and plot macroecological patterns predicted by the Maximum Entropy Theory of
Ecology (METE).")
    (license license:gpl2)))

(define-public r-meteospain
  (package
    (name "r-meteospain")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteospain" version))
       (sha256
        (base32 "1bbnq9b03r2njqphdkg43zfgf2hv2qy7zrfzy2nbzj784f599z3h"))))
    (properties `((upstream-name . "meteospain")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-vctrs
                             r-units
                             r-tidyr
                             r-stringr
                             r-sf
                             r-rlang
                             r-purrr
                             r-memoise
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-glue
                             r-dplyr
                             r-curl
                             r-cli
                             r-cachem
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://emf-creaf.github.io/meteospain/")
    (synopsis "Access to Spanish Meteorological Stations Services")
    (description
     "Access to different Spanish meteorological stations data services and APIs
(AEMET, SMC, MG, Meteoclimatic...).")
    (license license:expat)))

(define-public r-meteorits
  (package
    (name "r-meteorits")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteorits" version))
       (sha256
        (base32 "1vk8pbhfc5mbkbpw7ml81gx9i8187xp3vx36pbyyhjbrpy6gqabx"))))
    (properties `((upstream-name . "meteorits")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-pracma r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fchamroukhi/MEteorits")
    (synopsis
     "Mixture-of-Experts Modeling for Complex Non-Normal Distributions")
    (description
     "This package provides a unified mixture-of-experts (ME) modeling and estimation
framework with several original and flexible ME models to model, cluster and
classify heterogeneous data in many complex situations where the data are
distributed according to non-normal, possibly skewed distributions, and when
they might be corrupted by atypical observations.  Mixtures-of-Experts models
for complex and non-normal distributions ('meteorits') are originally introduced
and written in Matlab by Faicel Chamroukhi.  The references are mainly the
following ones.  The references are mainly the following ones.  Chamroukhi F.,
Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>.
Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-@code{PhD.pdf>}.
Chamroukhi F. (2015) <@code{arXiv:1506.06707>}.  Chamroukhi F. (2015)
<https://chamroukhi.com/FChamroukhi-HDR.pdf>.  Chamroukhi F. (2016)
<doi:10.1109/IJCNN.2016.7727580>.  Chamroukhi F. (2016)
<doi:10.1016/j.neunet.2016.03.002>.  Chamroukhi F. (2017)
<doi:10.1016/j.neucom.2017.05.044>.")
    (license license:gpl3+)))

(define-public r-meteor
  (package
    (name "r-meteor")
    (version "0.4-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteor" version))
       (sha256
        (base32 "1kcgfi4r3gd0n64hb0pa3bkabh16572r9ff5vdmbgmj3hkv8v8wi"))))
    (properties `((upstream-name . "meteor")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=meteor")
    (synopsis "Meteorological Data Manipulation")
    (description
     "This package provides a set of functions for weather and climate data
manipulation, and other helper functions, to support dynamic ecological
modeling, particularly crop and crop disease modeling.")
    (license license:gpl3)))

(define-public r-meteoland
  (package
    (name "r-meteoland")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteoland" version))
       (sha256
        (base32 "1p08bm1hp6m5086g41wpri85j1y8z3lq2bb6xs3aamp85rjja70z"))))
    (properties `((upstream-name . "meteoland")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-tidyr
                             r-stars
                             r-sf
                             r-rlang
                             r-rcpp
                             r-purrr
                             r-ncmeta
                             r-ncdfgeom
                             r-lubridate
                             r-lifecycle
                             r-dplyr
                             r-cubelyr
                             r-cli
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://emf-creaf.github.io/meteoland/")
    (synopsis "Landscape Meteorology Tools")
    (description
     "This package provides functions to estimate weather variables at any position of
a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].")
    (license license:gpl2+)))

(define-public r-meteoforecast
  (package
    (name "r-meteoforecast")
    (version "0.56")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteoForecast" version))
       (sha256
        (base32 "0nbjl4x06ni6jk8ji6n3cgd07iszw87lwg52rp8avvsz3kh19p5c"))))
    (properties `((upstream-name . "meteoForecast")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xml r-sp r-raster r-ncdf4))
    (home-page "https://github.com/oscarperpinan/meteoForecast")
    (synopsis "Numerical Weather Predictions")
    (description
     "Access to several Numerical Weather Prediction services both in raster format
and as a time series for a location.  Currently it works with GFS
<https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast>,
@code{MeteoGalicia}
<https://www.meteogalicia.gal/web/modelos/@code{threddsIndex.action>}, NAM
<https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale>,
and RAP
<https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update>.")
    (license license:gpl3)))

(define-public r-meteoevt
  (package
    (name "r-meteoevt")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteoEVT" version))
       (sha256
        (base32 "1i3zi1cgzv6lymmd7m92rmhmnxmdvi5nl367hnfbgxzy44van8l8"))))
    (properties `((upstream-name . "meteoEVT")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-ncdf4))
    (home-page "https://github.com/noctiluc3nt/meteoEVT")
    (synopsis
     "Computation and Visualization of Energetic and Vortical Atmospheric Quantities")
    (description
     "Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in
the atmosphere by combining conserved quantities from hydrodynamics and
thermodynamics.  The package @code{meteoEVT} provides functions to calculate
many energetic and vortical quantities, like potential vorticity, Bernoulli
function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008,
<doi:10.1111/j.1600-0870.2007.00272.x>], for given gridded data, like ERA5
reanalyses.  These quantities can be studied directly or can be used for many
applications in meteorology, e.g., the objective identification of atmospheric
fronts.  For this purpose, separate function are provided that allow the
detection of fronts based on the thermic front parameter [Hewson, 1998,
<doi:10.1017/S1350482798000553>], the F diagnostic [Parfitt et al., 2017,
<doi:10.1002/2017GL073662>] and the DSI [Mack et al., 2022,
<@code{arXiv:2208.11438>}].")
    (license license:gpl2+)))

(define-public r-meteo
  (package
    (name "r-meteo")
    (version "2.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meteo" version))
       (sha256
        (base32 "1l3656yh0ggjd7kyvkknn6a6ad1z45qpxrndgkp52cph2607n324"))))
    (properties `((upstream-name . "meteo")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-terra
                             r-spacetime
                             r-sp
                             r-snowfall
                             r-sftime
                             r-sf
                             r-raster
                             r-ranger
                             r-plyr
                             r-nabor
                             r-jsonlite
                             r-gstat
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-desctools
                             r-data-table
                             r-cast
                             r-caret))
    (home-page "https://www.r-pkg.org/pkg/meteo")
    (synopsis
     "RFSI & STRK Interpolation for Meteo and Environmental Variables")
    (description
     "Random Forest Spatial Interpolation (RFSI, SekuliÄ et al. (2020)
<doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal
regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al.
(2014) <doi:10.1002/2013JD020803>, SekuliÄ et al. (2020)
<doi:10.1007/s00704-019-03077-3>) and other environmental variables.  Contains
global spatio-temporal models calculated using publicly available data.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file LICENCE")))))

(define-public r-metchem
  (package
    (name "r-metchem")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetChem" version))
       (sha256
        (base32 "179634a8chsqfrq44il4dpyxrvadwdn1a7zm2h4ywim63w1dj02b"))))
    (properties `((upstream-name . "MetChem")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-rcdk r-kodama r-httr r-fingerprint))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MetChem")
    (synopsis "Chemical Structural Similarity Analysis")
    (description
     "This package provides a new pipeline to explore chemical structural similarity
across metabolite.  It allows to classify metabolites in structurally-related
modules and identify common shared functional groups.  KODAMA algorithm is used
to highlight structural similarity between metabolites.  See Cacciatore S,
Tenori L, Luchinat C, Bennett PR, @code{MacIntyre} DA. (2017) Bioinformatics
<doi:10.1093/bioinformatics/btw705>, Cacciatore S, Luchinat C, Tenori L. (2014)
Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>, and Abdel-Shafy EA, Melak
T, @code{MacIntyre} DA, Zadra G, Zerbini LF, Piazza S, Cacciatore S. (2023)
Bioinformatics Advances <doi:10.1093/bioadv/vbad053>.")
    (license license:gpl2+)))

(define-public r-metbrewer
  (package
    (name "r-metbrewer")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetBrewer" version))
       (sha256
        (base32 "0x51g5z9cdl043rna9qh4cj16qxahb6ccj9d8rr9d43d860jigp7"))))
    (properties `((upstream-name . "MetBrewer")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=MetBrewer")
    (synopsis
     "Color Palettes Inspired by Works at the Metropolitan Museum of Art")
    (description
     "Palettes Inspired by Works at the Metropolitan Museum of Art in New York.
Currently contains over 50 color schemes and checks for colorblind-friendliness
of palettes.  Colorblind accessibility checked using the {colorblindcheck}
package by Jakub Nowosad'<https://jakubnowosad.com/colorblindcheck/>.")
    (license license:cc0)))

(define-public r-metawho
  (package
    (name "r-metawho")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metawho" version))
       (sha256
        (base32 "1mflkrlnb1wk9vbbj621cy64s78q677d8fcnsi1ybh49k7sqmw20"))))
    (properties `((upstream-name . "metawho")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-purrr
                             r-metafor
                             r-magrittr
                             r-forestmodel
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ShixiangWang/metawho")
    (synopsis
     "Meta-Analytical Implementation to Identify Who Benefits Most from Treatments")
    (description
     "This package provides a tool for implementing so called deft approach (see
Fisher, David J., et al. (2017) <DOI:10.1136/bmj.j573>) and model visualization.")
    (license license:gpl3)))

(define-public r-metaviz
  (package
    (name "r-metaviz")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaviz" version))
       (sha256
        (base32 "0jvv49rw66v7saj2y1q88rp6fyzq7473af5d3k77ymvjbpqyrbyz"))))
    (properties `((upstream-name . "metaviz")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-nullabor
                             r-metafor
                             r-gridextra
                             r-ggpubr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Mkossmeier/metaviz")
    (synopsis
     "Forest Plots, Funnel Plots, and Visual Funnel Plot Inference for Meta-Analysis")
    (description
     "This package provides a compilation of functions to create visually appealing
and information-rich plots of meta-analytic data using ggplot2'.  Currently
allows to create forest plots, funnel plots, and many of their variants, such as
rainforest plots, thick forest plots, additional evidence contour funnel plots,
and sunset funnel plots.  In addition, functionalities for visual inference with
the funnel plot in the context of meta-analysis are provided.")
    (license license:gpl2)))

(define-public r-metavcov
  (package
    (name "r-metavcov")
    (version "2.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metavcov" version))
       (sha256
        (base32 "1md0ib23qzx9gk1xdz0382b1lcm40izkyxyb207787lnwmyivy1r"))))
    (properties `((upstream-name . "metavcov")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/luminwin/metavcov")
    (synopsis
     "Computing Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis")
    (description
     "Collection of functions to compute within-study covariances for different effect
sizes, data visualization, and single and multiple imputations for missing data.
 Effect sizes include correlation (r), mean difference (MD), standardized mean
difference (SMD), log odds ratio (@code{logOR}), log risk ratio (@code{logRR}),
and risk difference (RD).")
    (license license:gpl2+)))

(define-public r-metautility
  (package
    (name "r-metautility")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaUtility" version))
       (sha256
        (base32 "0pddp4h7jk2y140yiw4j5661011v1ym1dnb4ds9ai6i3iic23373"))))
    (properties `((upstream-name . "MetaUtility")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-metafor
                             r-metadat
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=MetaUtility")
    (synopsis
     "Utility Functions for Conducting and Interpreting Meta-Analyses")
    (description
     "This package contains functions to estimate the proportion of effects stronger
than a threshold of scientific importance (function prop_stronger), to
nonparametrically characterize the distribution of effects in a meta-analysis
(calib_ests, pct_pval), to make effect size conversions (r_to_d, r_to_z, z_to_r,
d_to_@code{logRR}), to compute and format inference in a meta-analysis
(format_CI, format_stat, tau_CI), to scrape results from existing meta-analyses
for re-analysis (scrape_meta, parse_CI_string, ci_to_var).")
    (license license:gpl2)))

(define-public r-metaumbrella
  (package
    (name "r-metaumbrella")
    (version "1.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaumbrella" version))
       (sha256
        (base32 "02x7f26c4flcj07i5jj2xpnx2y8zlvna7zxyillkq33pvh0yw95v"))))
    (properties `((upstream-name . "metaumbrella")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-writexl
                             r-withr
                             r-readxl
                             r-pwr
                             r-powersurvepi
                             r-meta))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metaumbrella")
    (synopsis "Umbrella Review Package for R")
    (description
     "This package provides a comprehensive range of facilities to perform umbrella
reviews with stratification of the evidence in R. The package accomplishes this
aim by building on three core functions that: (i) automatically perform all
required calculations in an umbrella review (including but not limited to
meta-analyses), (ii) stratify evidence according to various classification
criteria, and (iii) generate a visual representation of the results.  Note that
if you are not familiar with R, the core features of this package are available
from a web browser (<https://www.metaumbrella.org/>).")
    (license license:gpl3)))

(define-public r-metatools
  (package
    (name "r-metatools")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metatools" version))
       (sha256
        (base32 "09rr7hfzkwk2ddc9cs9x8yvqbizk0bxfflkn9vdlf1b9kvrpknh9"))))
    (properties `((upstream-name . "metatools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-metacore
                             r-magrittr
                             r-dplyr))
    (home-page "https://github.com/pharmaverse/metatools")
    (synopsis "Enable the Use of 'metacore' to Help Create and Check Dataset")
    (description
     "Uses the metadata information stored in metacore objects to check and build
metadata associated columns.")
    (license license:expat)))

(define-public r-metathis
  (package
    (name "r-metathis")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metathis" version))
       (sha256
        (base32 "1hlmz9yh6nxyi24plg1z4ln003vb1ja5flgyql8wfl1pzsc1sf28"))))
    (properties `((upstream-name . "metathis")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-magrittr r-knitr r-htmltools))
    (home-page "https://pkg.garrickadenbuie.com/metathis/")
    (synopsis "HTML Metadata Tags for 'R Markdown' and 'Shiny'")
    (description
     "Create meta tags for R Markdown HTML documents and Shiny apps for customized
social media cards, for accessibility, and quality search engine indexing.
metathis currently supports HTML documents created with rmarkdown', shiny',
xaringan', pagedown', bookdown', and flexdashboard'.")
    (license license:expat)))

(define-public r-metatest
  (package
    (name "r-metatest")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metatest" version))
       (sha256
        (base32 "1h3dcs1m7606b3a41yw2lak3lrqmsbpnx67qv24wvq003apz1sfd"))))
    (properties `((upstream-name . "metatest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=metatest")
    (synopsis "Fit and Test Metaregression Models")
    (description
     "Fits and tests meta regression models and generates a number of useful test
statistics: next to t- and z-tests, the likelihood ratio, bartlett corrected
likelihood ratio and permutation tests are performed on the model coefficients.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-metasurvival
  (package
    (name "r-metasurvival")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaSurvival" version))
       (sha256
        (base32 "05f103fi287y64gys73a55snw54bnph69q8dykfii10dykd44g0y"))))
    (properties `((upstream-name . "metaSurvival")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm))
    (home-page "https://github.com/shubhrampandey/metaSurvival")
    (synopsis "Meta-Analysis of a Single Survival Curve")
    (description
     "To assess a summary survival curve from survival probabilities and number of
at-risk patients collected at various points in time in various studies, and to
test the between-strata heterogeneity.")
    (license license:expat)))

(define-public r-metasubtract
  (package
    (name "r-metasubtract")
    (version "1.60")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaSubtract" version))
       (sha256
        (base32 "19xg95g2qgyi90pz3733l596gzdj6dmb0k9i4qmhaq4yck69rrhj"))))
    (properties `((upstream-name . "MetaSubtract")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MetaSubtract")
    (synopsis
     "Subtracting Summary Statistics of One or more Cohorts from Meta-GWAS Results")
    (description
     "If results from a meta-GWAS are used for validation in one of the cohorts that
was included in the meta-analysis, this will yield biased (i.e.  too optimistic)
results.  The validation cohort needs to be independent from the
meta-Genome-Wide-Association-Study (meta-GWAS) results. @code{MetaSubtract} will
subtract the results of the respective cohort from the meta-GWAS results
analytically without having to redo the meta-GWAS analysis using the
leave-one-out methodology.  It can handle different meta-analyses methods and
takes into account if single or double genomic control correction was applied to
the original meta-analysis.  It can also handle different meta-analysis methods.
 It can be used for whole GWAS, but also for a limited set of genetic markers.
See for application: Nolte I.M. et al. (2017); <doi: 10.1038/ejhg.2017.50>.")
    (license license:gpl3+)))

(define-public r-metastan
  (package
    (name "r-metastan")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaStan" version))
       (sha256
        (base32 "00rj11q20apw7f5s6vw8fh7qxgfksndyiks1w907zzjlxhy9b4pj"))))
    (properties `((upstream-name . "MetaStan")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-metafor
                             r-loo
                             r-hdinterval
                             r-forestplot
                             r-coda
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gunhanb/MetaStan")
    (synopsis "Bayesian Meta-Analysis via 'Stan'")
    (description
     "This package performs Bayesian meta-analysis, meta-regression and model-based
meta-analysis using Stan'.  Includes binomial-normal hierarchical models and
option to use weakly informative priors for the heterogeneity parameter and the
treatment effect parameter which are described in Guenhan, Roever, and Friede
(2020) <doi:10.1002/jrsm.1370>.")
    (license license:gpl3+)))

(define-public r-metasens
  (package
    (name "r-metasens")
    (version "1.5-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metasens" version))
       (sha256
        (base32 "0jd5adkyi14blbppmv6lny4pnj0ld2wkq6xpv7dhvh0xhgw5n49y"))))
    (properties `((upstream-name . "metasens")))
    (build-system r-build-system)
    (propagated-inputs (list r-meta))
    (home-page "https://cran.r-project.org/package=metasens")
    (synopsis "Statistical Methods for Sensitivity Analysis in Meta-Analysis")
    (description
     "The following methods are implemented to evaluate how sensitive the results of a
meta-analysis are to potential bias in meta-analysis and to support Schwarzer et
al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 Small-Study Effects in
Meta-Analysis': - Copas selection model described in Copas & Shi (2001)
<DOI:10.1177/096228020101000402>; - limit meta-analysis by RÃ¼cker et al. (2011)
<DOI:10.1093/biostatistics/kxq046>; - upper bound for outcome reporting bias by
Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>; - imputation
methods for missing binary data by Gamble & Hollis (2005)
<DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008)
<DOI:10.1177/1740774508091600>; - LFK index test and Doi plot by Furuya-Kanamori
et al. (2018) <DOI:10.1097/XEB.0000000000000141>.")
    (license license:gpl2+)))

(define-public r-metasem
  (package
    (name "r-metasem")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaSEM" version))
       (sha256
        (base32 "1dhlpi67m0mjmid8s2zma1y4p2g7fisnxfm5ff6i30qxrphryv9m"))))
    (properties `((upstream-name . "metaSEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-openmx
                             r-numderiv
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-lavaan
                             r-ellipse))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/mikewlcheung/metasem")
    (synopsis "Meta-Analysis using Structural Equation Modeling")
    (description
     "This package provides a collection of functions for conducting meta-analysis
using a structural equation modeling (SEM) approach via the @code{OpenMx} and
lavaan packages.  It also implements various procedures to perform meta-analytic
structural equation modeling on the correlation and covariance matrices, see
Cheung (2015) <doi:10.3389/fpsyg.2014.01521>.")
    (license license:gpl2+)))

(define-public r-metasdtreg
  (package
    (name "r-metasdtreg")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaSDTreg" version))
       (sha256
        (base32 "00lwfx083a449yppnh3zai2hyzc0g53v8nr8r4ippdv088612iq6"))))
    (properties `((upstream-name . "metaSDTreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-ordinal r-maxlik r-matrix))
    (home-page "https://cran.r-project.org/package=metaSDTreg")
    (synopsis "Regression Models for Meta Signal Detection Theory")
    (description
     "Regression methods for the meta-SDT model.  The package implements methods for
cognitive experiments of metacognition as described in Kristensen, S. B.,
Sandberg, K., & Bibby, B. M. (2020).  Regression methods for metacognitive
sensitivity.  Journal of Mathematical Psychology, 94.
<doi:10.1016/j.jmp.2019.102297>.")
    (license license:gpl3)))

(define-public r-metarnaseq
  (package
    (name "r-metarnaseq")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaRNASeq" version))
       (sha256
        (base32 "1c9qxac7439wxnmr1jah37pmski5wyap1vd4nb8gvq4indbdiaz2"))))
    (properties `((upstream-name . "metaRNASeq")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=metaRNASeq")
    (synopsis "Meta-Analysis of RNA-Seq Data")
    (description
     "Implementation of two p-value combination techniques (inverse normal and Fisher
methods).  A vignette is provided to explain how to perform a meta-analysis from
two independent RNA-seq experiments.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-metarep
  (package
    (name "r-metarep")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metarep" version))
       (sha256
        (base32 "04xfvdn1yng0bnq91qa9hfsa2y37h6r3ffzvfip27wq2wy0q9sn9"))))
    (properties `((upstream-name . "metarep")))
    (build-system r-build-system)
    (propagated-inputs (list r-meta))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/IJaljuli/metarep")
    (synopsis "Replicability-Analysis Tools for Meta-Analysis")
    (description
     "User-friendly package for reporting replicability-analysis methods, affixed to
meta-analyses summary.  The replicability-analysis output provides an assessment
of the investigated intervention, where it offers quantification of effect
replicability and assessment of the consistency of findings. -
Replicability-analysis for fixed-effects and random-effect meta analysis: -
r(u)-value; - lower bounds on the number of studies with replicated positive
and\\or negative effect; - Allows detecting inconsistency of signals; - forest
plots with the summary of replicability analysis results; - Allows
Replicability-analysis with or without the common-effect assumption.")
    (license license:gpl2+)))

(define-public r-metarange
  (package
    (name "r-metarange")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaRange" version))
       (sha256
        (base32 "05x7rs4ah42326xmpm4spm2ysmlw2c6cs1fxgngy9ndz6k2dmb64"))))
    (properties `((upstream-name . "metaRange")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rcpparmadillo r-rcpp r-r6 r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://metaRange.github.io/metaRange/")
    (synopsis
     "Framework to Build Mechanistic and Metabolic Constrained Species Distribution Models")
    (description
     "Build spatially and temporally explicit process-based species distribution
models, that can include an arbitrary number of environmental factors, species
and processes including metabolic constraints and species interactions.  The
focus of the package is simulating populations of one or multiple species in a
grid-based landscape and studying the meta-population dynamics and emergent
patterns that arise from the interaction of species under complex environmental
conditions.  It provides functions for common ecological processes such as
negative exponential, kernel-based dispersal (see Nathan et al. (2012)
<doi:10.1093/acprof:oso/9780199608898.003.0015>), calculation of the
environmental suitability based on cardinal values ( Yin et al. (1995)
<doi:10.1016/0168-1923(95)02236-Q>, simplified by Yan and Hunt (1999)
<doi:10.1006/anbo.1999.0955> see eq: 4), reproduction in form of an Ricker model
(see Ricker (1954) <doi:10.1139/f54-039> and Cabral and Schurr (2010)
<doi:10.1111/j.1466-8238.2009.00492.x>), as well as metabolic scaling based on
the metabolic theory of ecology (see Brown et al. (2004) <doi:10.1890/03-9000>
and Brown, Sibly and Kodric-Brown (2012) <doi:10.1002/9781119968535.ch>).")
    (license license:gpl3)))

(define-public r-metaprotr
  (package
    (name "r-metaprotr")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaprotr" version))
       (sha256
        (base32 "1iz5aqd8ha0h19sa58s7xd4h26lbqp68dxy93pxnx9bj3bv615b3"))))
    (properties `((upstream-name . "metaprotr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-stringr
                             r-reshape2
                             r-ggrepel
                             r-ggforce
                             r-dplyr
                             r-dendextend
                             r-ade4))
    (home-page "https://forgemia.inra.fr/pappso/metaprotr")
    (synopsis "Metaproteomics Post-Processing Analysis")
    (description
     "Set of tools for descriptive analysis of metaproteomics data generated from
high-throughput mass spectrometry instruments.  These tools allow to cluster
peptides and proteins abundance, expressed as spectral counts, and to manipulate
them in groups of metaproteins.  This information can be represented using
multiple visualization functions to portray the global metaproteome landscape
and to differentiate samples or conditions, in terms of abundance of
metaproteins, taxonomic levels and/or functional annotation.  The provided tools
allow to implement flexible analytical pipelines that can be easily applied to
studies interested in metaproteomics analysis.")
    (license license:gpl3)))

(define-public r-metapro
  (package
    (name "r-metapro")
    (version "1.5.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metapro" version))
       (sha256
        (base32 "1630fyw3mb2fv0v5nmrp9xc50ac98j135c391rr3qq5rkn89r5f2"))))
    (properties `((upstream-name . "metapro")))
    (build-system r-build-system)
    (propagated-inputs (list r-metap))
    (home-page "https://cran.r-project.org/package=metapro")
    (synopsis "Robust P-Value Combination Methods")
    (description
     "The meta-analysis is performed to increase the statistical power by integrating
the results from several experiments.  The p-values are often combined in
meta-analysis when the effect sizes are not available.  The metapro R package
provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9),
Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949,
ISSN:00063444)), but also new method named weighted Fisherâs method we
developed.  While the (weighted) Z-method is suitable for finding features
effective in most experiments, (weighted) Fisherâs method is useful for
detecting partially associated features.  Thus, the users can choose the
function based on their purpose.  Yoon et al. (2021) \"Powerful p-value
combination methods to detect incomplete association\"
<doi:10.1038/s41598-021-86465-y>.")
    (license license:gpl2+)))

(define-public r-metapower
  (package
    (name "r-metapower")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metapower" version))
       (sha256
        (base32 "1r6mmdxxi83isjjyfzblwpzfgqdmfhbqmq49zm2b4jd0zvc0lfcx"))))
    (properties `((upstream-name . "metapower")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-testthat
                             r-rlang
                             r-magrittr
                             r-knitr
                             r-ggplot2
                             r-dplyr
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metapower")
    (synopsis "Power Analysis for Meta-Analysis")
    (description
     "This package provides a simple and effective tool for computing and visualizing
statistical power for meta-analysis, including power analysis of main effects
(Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, test of homogeneity (Pigott,
2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, and categorical
moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.")
    (license license:gpl2)))

(define-public r-metapost
  (package
    (name "r-metapost")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metapost" version))
       (sha256
        (base32 "098f7sifg814mc4sb1nb4d6dlr1xms5hwyp08ksxg0i4ayf9jf3c"))))
    (properties `((upstream-name . "metapost")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-gridbezier))
    (home-page "https://github.com/pmur002/metapost")
    (synopsis "Interface to 'MetaPost'")
    (description
     "This package provides an interface to @code{MetaPost} (Hobby, 1998)
<http://www.tug.org/docs/metapost/mpman.pdf>.  There are functions to generate
an R description of a @code{MetaPost} curve, functions to generate
@code{MetaPost} code from an R description, functions to process @code{MetaPost}
code, and functions to read solved @code{MetaPost} paths back into R.")
    (license license:gpl2+)))

(define-public r-metaplus
  (package
    (name "r-metaplus")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaplus" version))
       (sha256
        (base32 "0cknvgnkvqdw00600akpxrrdbg7h0sdyv15vmhs9dsfdf1ag6z51"))))
    (properties `((upstream-name . "metaplus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast
                             r-numderiv
                             r-metafor
                             r-mass
                             r-lme4
                             r-foreach
                             r-fastghquad
                             r-dorng
                             r-doparallel
                             r-boot
                             r-bbmle))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=metaplus")
    (synopsis "Robust Meta-Analysis and Meta-Regression")
    (description
     "This package performs meta-analysis and meta-regression using standard and
robust methods with confidence intervals based on the profile likelihood.
Robust methods are based on alternative distributions for the random effect,
either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or
Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals
(Beath, 2014 <doi:10.1002/jrsm.1114>).")
    (license license:gpl2+)))

(define-public r-metaplot
  (package
    (name "r-metaplot")
    (version "0.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaplot" version))
       (sha256
        (base32 "1ywr26sqbj0gqcyn4nqj0xi9018dzxwvm663xdrm31wnchh8iivg"))))
    (properties `((upstream-name . "metaplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-rlang
                             r-magrittr
                             r-lattice
                             r-gtable
                             r-gridextra
                             r-ggplot2
                             r-encode
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=metaplot")
    (synopsis "Data-Driven Plot Design")
    (description
     "Designs plots in terms of core structure.  See example(metaplot)'.  Primary
arguments are (unquoted) column names; order and type (numeric or not) dictate
the resulting plot.  Specify any y variables, x variable, any groups variable,
and any conditioning variables to metaplot() to generate density plots,
boxplots, mosaic plots, scatterplots, scatterplot matrices, or conditioned
plots.  Use multiplot() to arrange plots in grids.  Wherever present, scalar
column attributes label and guide are honored, producing fully annotated plots
with minimal effort.  Attribute guide is typically units, but may be encoded()
to provide interpretations of categorical values (see ?encode').  Utility
unpack() transforms scalar column attributes to row values and pack() does the
reverse, supporting tool-neutral storage of metadata along with primary data.
The package supports customizable aesthetics such as such as reference lines,
unity lines, smooths, log transformation, and linear fits.  The user may choose
between trellis and ggplot output.  Compact syntax and integrated metadata
promote workflow scalability.")
    (license license:gpl3)))

(define-public r-metapack
  (package
    (name "r-metapack")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metapack" version))
       (sha256
        (base32 "17j3xivpyq17is5vzmca9zck6dryzfnrzddvppik4ml3xq122br9"))))
    (properties `((upstream-name . "metapack")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-gridextra
                             r-ggplot2
                             r-formula
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://events.stat.uconn.edu/metapack/")
    (synopsis "Bayesian Meta-Analysis and Network Meta-Analysis")
    (description
     "This package contains functions performing Bayesian inference for meta-analytic
and network meta-analytic models through Markov chain Monte Carlo algorithm.
Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G.
Ibrahim, Arvind K. Shah, and Jianxin Lin (2015)
<doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen,
Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021)
<doi:10.1002/sim.8983>.  For maximal computational efficiency, the Markov chain
Monte Carlo samplers for each model, written in C++, are fine-tuned.  This
software has been developed under the auspices of the National Institutes of
Health and Merck & Co., Inc., Kenilworth, NJ, USA.")
    (license license:gpl3+)))

(define-public r-metansue
  (package
    (name "r-metansue")
    (version "2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metansue" version))
       (sha256
        (base32 "0mi688060xqxb63wnszpwvag08p1czzggapmy15rzrzysz5j0dsz"))))
    (properties `((upstream-name . "metansue")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=metansue")
    (synopsis
     "Meta-Analysis of Studies with Non-Statistically Significant Unreported Effects")
    (description
     "Novel method to unbiasedly include studies with Non-statistically Significant
Unreported Effects (NSUEs) in a meta-analysis.  First, the function calculates
the interval where the unreported effects (e.g., t-values) should be according
to the threshold of statistical significance used in each study.  Afterward, the
method uses maximum likelihood techniques to impute the expected effect size of
each study with NSUEs, accounting for between-study heterogeneity and potential
covariates.  Multiple imputations of the NSUEs are then randomly created based
on the expected value, variance, and statistical significance bounds.  Finally,
it conducts a restricted-maximum likelihood random-effects meta-analysis
separately for each set of imputations, and it performs estimations from these
meta-analyses.  Please read the reference in metansue for details of the
procedure.")
    (license license:gpl3)))

(define-public r-metanlp
  (package
    (name "r-metanlp")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaNLP" version))
       (sha256
        (base32 "0ya8hi7bpxnz1a8vjni5sg68yf86bn72wh8676x6xqf6z1qvha18"))))
    (properties `((upstream-name . "MetaNLP")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm r-textstem r-lexicon r-glmnet))
    (home-page "https://github.com/imbi-heidelberg/MetaNLP")
    (synopsis "Natural Language Processing for Meta Analysis")
    (description
     "Given a CSV file with titles and abstracts, the package creates a word count
matrix that is lemmatized and stemmed and can directly be used to train machine
learning methods for automatic title-abstract screening in the preparation of a
meta analysis.")
    (license license:expat)))

(define-public r-metanetwork
  (package
    (name "r-metanetwork")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metanetwork" version))
       (sha256
        (base32 "0rlnalmfmiih154zq6rk4sq12y7nr8fn1ws8mrlryy04gznz6igw"))))
    (properties `((upstream-name . "metanetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-sna
                             r-rlang
                             r-rcolorbrewer
                             r-network
                             r-matrix
                             r-magrittr
                             r-intergraph
                             r-igraph
                             r-ggplot2
                             r-ggimage
                             r-ggally
                             r-dplyr))
    (home-page "https://github.com/MarcOhlmann/metanetwork")
    (synopsis "Handling and Representing Trophic Networks in Space and Time")
    (description
     "This package provides a toolbox to handle and represent trophic networks in
space or time across aggregation levels.  This package contains a layout
algorithm specifically designed for trophic networks, using dimension reduction
on a diffusion graph kernel and trophic levels.  Importantly, this package
provides a layout method applicable for large trophic networks.")
    (license license:gpl3)))

(define-public r-metanet
  (package
    (name "r-metanet")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaNet" version))
       (sha256
        (base32 "0r3id0ap91g948a10yka1lz82jib3ww91n24vlk4pbik7flxbqpk"))))
    (properties `((upstream-name . "MetaNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-reshape2
                             r-rcolorbrewer
                             r-pcutils
                             r-magrittr
                             r-igraph
                             r-ggrepel
                             r-ggplot2
                             r-ggnewscale
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Asa12138/MetaNet")
    (synopsis "Network Analysis for Omics Data")
    (description
     "Comprehensive network analysis package.  Calculate correlation network fastly,
accelerate lots of analysis by parallel computing.  Support for multi-omics
data, search sub-nets fluently.  Handle bigger data, more than 10,000 nodes in
each omics.  Offer various layout method for multi-omics network and some
interfaces to other software ('Gephi', Cytoscape', ggplot2'), easy to visualize.
 Provide comprehensive topology indexes calculation, including ecological
network stability.")
    (license license:gpl3)))

(define-public r-metan
  (package
    (name "r-metan")
    (version "1.18.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metan" version))
       (sha256
        (base32 "0m7czvd7djv80c6kly8ckgs9m5ld0rqnd92y76n181d2xk9l6ypc"))))
    (properties `((upstream-name . "metan")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-patchwork
                             r-mathjaxr
                             r-magrittr
                             r-lmertest
                             r-lme4
                             r-ggrepel
                             r-ggplot2
                             r-ggforce
                             r-ggally
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/TiagoOlivoto/metan")
    (synopsis "Multi Environment Trials Analysis")
    (description
     "This package performs stability analysis of multi-environment trial data using
parametric and non-parametric methods.  Parametric methods includes Additive
Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013)
<doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965), Genotype plus
Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003)
<doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri
(2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart &
Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic
confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power
law residuals (POLAR) statistics by Doring et al. (2015)
<doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by
Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, stability variance by
Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by
Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait
stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>.
Non-parametric methods includes superiority index by Lin & Binns (1988)
<doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by
Huehn (1990) <https://link.springer.com/article/10.1007/BF00024241>, TOP third
statistic by Fox et al. (1990) <doi:10.1007/BF00040364>.  Functions for
computing biometrical analysis such as path analysis, canonical correlation,
partial correlation, clustering analysis, and tools for inspecting,
manipulating, summarizing and plotting typical multi-environment trial data are
also provided.")
    (license license:gpl3)))

(define-public r-metamisc
  (package
    (name "r-metamisc")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metamisc" version))
       (sha256
        (base32 "12ydvkpni7a7v0x8lfknsy4c6hmgjqri3l33m4fwsai3rhnq6hsa"))))
    (properties `((upstream-name . "metamisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-proc
                             r-plyr
                             r-mvtnorm
                             r-metafor
                             r-lme4
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/smartdata-analysis-and-statistics/metamisc")
    (synopsis "Meta-Analysis of Diagnosis and Prognosis Research Studies")
    (description
     "Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis
research studies.  It includes functions to summarize multiple estimates of
prediction model discrimination and calibration performance (Debray et al.,
2019) <doi:10.1177/0962280218785504>.  It also includes functions to evaluate
funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>.  Finally,
the package provides functions for developing multivariable prediction models
from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.")
    (license license:gpl3)))

(define-public r-metamicrobiomer
  (package
    (name "r-metamicrobiomer")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metamicrobiomeR" version))
       (sha256
        (base32 "0fgdjy2s47pqmb9x79a4sf0kn4hbc49rd41jnzw0d3ll1l9kwy7f"))))
    (properties `((upstream-name . "metamicrobiomeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zcompositions
                             r-tidyr
                             r-plyr
                             r-meta
                             r-matrixstats
                             r-lmertest
                             r-lme4
                             r-gridextra
                             r-ggplot2
                             r-gdata
                             r-gamlss
                             r-dplyr
                             r-compositions))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nhanhocu/metamicrobiomeR")
    (synopsis
     "Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI & Random Effects")
    (description
     "Generalized Additive Model for Location, Scale and Shape (GAMLSS) with zero
inflated beta (BEZI) family for analysis of microbiome relative abundance data
(with various options for data transformation/normalization to address
compositional effects) and random effects meta-analysis models for meta-analysis
pooling estimates across microbiome studies are implemented.  Random Forest
model to predict microbiome age based on relative abundances of shared bacterial
genera with the Bangladesh data (Subramanian et al 2014), comparison of multiple
diversity indexes using linear/linear mixed effect models and some data
display/visualization are also implemented.  The reference paper is published by
Ho NT, Li F, Wang S, Kuhn L (2019) <doi:10.1186/s12859-019-2744-2> .")
    (license license:gpl2)))

(define-public r-metamer
  (package
    (name "r-metamer")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metamer" version))
       (sha256
        (base32 "0b427cbasy1irks9jy5kjg3ay5xa01dcalhl8g14smhkdv5054wz"))))
    (properties `((upstream-name . "metamer")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress r-fnn))
    (home-page "https://eliocamp.github.io/metamer/")
    (synopsis "Create Data with Identical Statistics")
    (description
     "This package creates data with identical statistics (metamers) using an
iterative algorithm proposed by Matejka & Fitzmaurice (2017)
<DOI:10.1145/3025453.3025912>.")
    (license license:gpl3)))

(define-public r-metamedian
  (package
    (name "r-metamedian")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metamedian" version))
       (sha256
        (base32 "17p8npnaddsdikii9c8zhvbwhkpwgncksipzsslrs7d4pz6wlfvf"))))
    (properties `((upstream-name . "metamedian")))
    (build-system r-build-system)
    (propagated-inputs (list r-metafor r-metablue r-hmisc r-estmeansd))
    (home-page "https://github.com/stmcg/metamedian")
    (synopsis "Meta-Analysis of Medians")
    (description
     "This package implements several methods to meta-analyze studies that report the
sample median of the outcome.  When the primary studies are one-group studies,
the methods of @code{McGrath} et al. (2019) <doi:10.1002/sim.8013> and Ozturk
and Balakrishnan (2020) <doi:10.1002/sim.8738> can be applied to estimate the
pooled median.  In the two-group context, the methods of @code{McGrath} et al.
(2020a) <doi:10.1002/bimj.201900036> can be applied to estimate the pooled
difference of medians across groups.  Additionally, a number of methods (e.g.,
@code{McGrath} et al. (2020b) <doi:10.1177/0962280219889080>, Cai et al. (2021)
<doi:10.1177/09622802211047348>, and @code{McGrath} et al. (2023)
<doi:10.1177/09622802221139233>) are implemented to estimate study-specific
(difference of) means and their standard errors in order to estimate the pooled
(difference of) means.  See @code{McGrath} et al. (in press)
<doi:10.1002/jrsm.1686> for a detailed guide on using the package.")
    (license license:gpl3+)))

(define-public r-metama
  (package
    (name "r-metama")
    (version "3.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaMA" version))
       (sha256
        (base32 "13imp5d1ggw4qc7win4d2nvwvl92s9visb6bjm09h69lp18h631s"))))
    (properties `((upstream-name . "metaMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-smvar r-limma))
    (home-page "https://cran.r-project.org/package=metaMA")
    (synopsis "Meta-Analysis for MicroArrays")
    (description
     "Combination of either p-values or modified effect sizes from different studies
to find differentially expressed genes.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-metalyzer
  (package
    (name "r-metalyzer")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetAlyzer" version))
       (sha256
        (base32 "0b5kznmqq7kda5qan5jyxy5s41y367dmq2fxk7mihjnmn9fp282n"))))
    (properties `((upstream-name . "MetAlyzer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-summarizedexperiment
                             r-stringr
                             r-s4vectors
                             r-rlang
                             r-qvalue
                             r-openxlsx
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-agricolae))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nilsmechtel/MetAlyzer")
    (synopsis "Read and Analyze 'MetIDQ&trade;' Software Output Files")
    (description
     "The @code{MetAlyzer} S4 object provides methods to read and reformat
metabolomics data for convenient data handling, statistics and downstream
analysis.  The resulting format corresponds to input data of the Shiny app
@code{MetaboExtract}
(<https://www.metaboextract.shiny.dkfz.de/@code{MetaboExtract/>}).")
    (license license:gpl3)))

(define-public r-metalonda
  (package
    (name "r-metalonda")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaLonDA" version))
       (sha256
        (base32 "0nalkmsii2anw07r16zcpgafb01l87hgpfr1mwc7nggvhdfp3w2g"))))
    (properties `((upstream-name . "MetaLonDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-pracma
                             r-plyr
                             r-metagenomeseq
                             r-gss
                             r-ggplot2
                             r-edger
                             r-doparallel
                             r-deseq2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/aametwally/MetaLonDA")
    (synopsis "Metagenomics Longitudinal Differential Abundance Method")
    (description
     "Identify time intervals of differentially abundant metagenomic features in
longitudinal studies (Metwally AA, et al., Microbiome, 2018
<doi:10.1186/s40168-018-0402-y>).")
    (license license:expat)))

(define-public r-metalite-table1
  (package
    (name "r-metalite-table1")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metalite.table1" version))
       (sha256
        (base32 "1vg6mprn2c633qr5v1rfgbg5bwc621chy69c8k1yz700rhzapcfa"))))
    (properties `((upstream-name . "metalite.table1")))
    (build-system r-build-system)
    (propagated-inputs (list r-reactable r-r2rtf r-metalite r-jsonlite
                             r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metalite.table1")
    (synopsis "Interactive Table of Descriptive Statistics in HTML")
    (description
     "Create an interactive table of descriptive statistics in HTML. This table is
typically used for exploratory analysis in a clinical study (referred to as
Table 1').")
    (license license:gpl3+)))

(define-public r-metalite-ae
  (package
    (name "r-metalite-ae")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metalite.ae" version))
       (sha256
        (base32 "0vf8v6nf4f7lagfzs4dr6q5gz2dh18wal74xmlfaw7cmw42z8nsh"))))
    (properties `((upstream-name . "metalite.ae")))
    (build-system r-build-system)
    (propagated-inputs (list r-r2rtf r-metalite r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/metalite.ae/")
    (synopsis "Adverse Events Analysis Using 'metalite'")
    (description
     "Analyzes adverse events in clinical trials using the metalite data structure.
The package simplifies the workflow to create production-ready tables, listings,
and figures discussed in the adverse events analysis chapters of \"R for Clinical
Study Reports and Submission\" by Zhang et al. (2022) <https://r4csr.org/>.")
    (license license:gpl3)))

(define-public r-metalite
  (package
    (name "r-metalite")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metalite" version))
       (sha256
        (base32 "1l6n31l5qb4x15k88jpanrhd20i8cbm3rllgkzww7b85wib8vy1h"))))
    (properties `((upstream-name . "metalite")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/metalite/")
    (synopsis "ADaM Metadata Structure")
    (description
     "This package provides a metadata structure for clinical data analysis and
reporting based on Analysis Data Model (A@code{DaM}) datasets.  The package
simplifies clinical analysis and reporting tool development by defining
standardized inputs, outputs, and workflow.  The package can be used to create
analysis and reporting planning grid, mock table, and validated analysis and
reporting results based on consistent inputs.")
    (license license:gpl3)))

(define-public r-metalik
  (package
    (name "r-metalik")
    (version "0.43.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaLik" version))
       (sha256
        (base32 "1li40pgd9z00nrph9njwn6wysb1i9dkpqzcp6fzds6asvcxlqqfl"))))
    (properties `((upstream-name . "metaLik")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=metaLik")
    (synopsis
     "Likelihood Inference in Meta-Analysis and Meta-Regression Models")
    (description
     "First- and higher-order likelihood inference in meta-analysis and
meta-regression models.")
    (license license:gpl2+)))

(define-public r-metalandsim
  (package
    (name "r-metalandsim")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaLandSim" version))
       (sha256
        (base32 "0dx8vya0w8n9f53g7ppjmz3a9vhs3h6fb7z4fb2c1bhp4yjkbl23"))))
    (properties `((upstream-name . "MetaLandSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr
                             r-terra
                             r-spatstat-random
                             r-spatstat-geom
                             r-sp
                             r-minpack-lm
                             r-knitr
                             r-igraph
                             r-googlevis
                             r-e1071
                             r-coda))
    (home-page "https://cran.r-project.org/package=MetaLandSim")
    (synopsis "Landscape and Range Expansion Simulation")
    (description
     "This package provides tools to generate random landscape graphs, evaluate
species occurrence in dynamic landscapes, simulate future landscape occupation
and evaluate range expansion when new empty patches are available (e.g. as a
result of climate change).  References: Mestre, F., Canovas, F., Pita, R., Mira,
A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., Risk, B.,
Mira, A., Beja, P., Pita, R. (2017) <doi:10.1016/j.ecolmodel.2017.06.013>;
Mestre, F., Pita, R., Mira, A., Beja, P. (2020) <doi:10.1186/s12898-019-0273-5>.")
    (license license:gpl2+)))

(define-public r-metajam
  (package
    (name "r-metajam")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metajam" version))
       (sha256
        (base32 "1sqxybxzi198a6b1za33xyb42ii3v8d5p5bx6i393r9qkniry69s"))))
    (properties `((upstream-name . "metajam")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xml
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-readr
                             r-purrr
                             r-lubridate
                             r-emld
                             r-eml
                             r-dplyr
                             r-dataone))
    (native-inputs (list r-knitr))
    (home-page "https://nceas.github.io/metajam/")
    (synopsis "Easily Download Data and Metadata from 'DataONE'")
    (description
     "This package provides a set of tools to foster the development of reproducible
analytical workflow by simplifying the download of data and metadata from
@code{DataONE} (<https://www.dataone.org>) and easily importing this information
into R.")
    (license license:asl2.0)))

(define-public r-metaintegration
  (package
    (name "r-metaintegration")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaIntegration" version))
       (sha256
        (base32 "0dbmpls6gipd81c79lvxqgyhzl3nmi0ma4szv8fcrh50jbwfvsql"))))
    (properties `((upstream-name . "MetaIntegration")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-mass r-knitr r-corpcor))
    (home-page "https://github.com/umich-biostatistics/MetaIntegration")
    (synopsis "Ensemble Meta-Prediction Framework")
    (description
     "An ensemble meta-prediction framework to integrate multiple regression models
into a current study.  Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020)
<@code{arXiv:2010.09971>}.  A meta-analysis framework along with two weighted
estimators as the ensemble of empirical Bayes estimators, which combines the
estimates from the different external models.  The proposed framework is
flexible and robust in the ways that (i) it is capable of incorporating external
models that use a slightly different set of covariates; (ii) it is able to
identify the most relevant external information and diminish the influence of
information that is less compatible with the internal data; and (iii) it nicely
balances the bias-variance trade-off while preserving the most efficiency gain.
The proposed estimators are more efficient than the naive analysis of the
internal data and other naive combinations of external estimators.")
    (license license:gpl2)))

(define-public r-metainc
  (package
    (name "r-metainc")
    (version "0.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metainc" version))
       (sha256
        (base32 "12kli07sh2b2ynpaasqmx2vfxnvv50brfcqnrgsk9abapy8ckfag"))))
    (properties `((upstream-name . "metainc")))
    (build-system r-build-system)
    (propagated-inputs (list r-meta r-ggplot2 r-confintr))
    (home-page "https://cran.r-project.org/package=metainc")
    (synopsis
     "Assessment of Inconsistency in Meta-Analysis using Decision Thresholds")
    (description
     "Assessment of inconsistency in meta-analysis by calculating the Decision
Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index.
These indices quantify inconsistency taking into account outcome-level decision
thresholds.")
    (license license:gpl2+)))

(define-public r-metaheuristicopt
  (package
    (name "r-metaheuristicopt")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaheuristicOpt" version))
       (sha256
        (base32 "0ra4hl9l1jlzymj218m2i6xrxfacyx4aaln5pk7q4di2ks0nq11m"))))
    (properties `((upstream-name . "metaheuristicOpt")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=metaheuristicOpt")
    (synopsis "Metaheuristic for Optimization")
    (description
     "An implementation of metaheuristic algorithms for continuous optimization.
Currently, the package contains the implementations of 21 algorithms, as
follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion
optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf
optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>),
dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly
algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm
(Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi
et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm
(Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer
(Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm
(Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>), whale optimization
algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>),
clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>),
differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff,
Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial
bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi &
Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012),
gravitational based search (Rashedi et al., 2009) and black hole optimization
(Hatamlou, 2013).")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-metahelper
  (package
    (name "r-metahelper")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaHelper" version))
       (sha256
        (base32 "0nsrsaklxbr4clg3ka8rrpp162bl9qcxkffqkmdz5ha4dfs028vm"))))
    (properties `((upstream-name . "metaHelper")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-confintr))
    (home-page "https://github.com/RobertEmprechtinger/metaHelper")
    (synopsis
     "Transforms Statistical Measures Commonly Used for Meta-Analysis")
    (description
     "Helps calculate statistical values commonly used in meta-analysis.  It provides
several methods to compute different forms of standardized mean differences, as
well as other values such as standard errors and standard deviations.  The
methods used in this package are described in the following references: Altman D
G, Bland J M. (2011) <doi:10.1136/bmj.d2090> Borenstein, M., Hedges, L.V.,
Higgins, J.P.T. and Rothstein, H.R. (2009) <doi:10.1002/9780470743386.ch4> Chinn
S. (2000) <doi:10.1002/1097-0258(20001130)19:22%3C3127::aid-sim784%3E3.0.co;2-m>
Cochrane Handbook (2011) <https://handbook-5-1.cochrane.org/front_page.htm>
Cooper, H., Hedges, L. V., & Valentine, J. C. (2009)
<https://psycnet.apa.org/record/2009-05060-000> Cohen, J. (1977)
<https://psycnet.apa.org/record/1987-98267-000> Ellis, P.D. (2009)
<https://www.psychometrica.de/effect_size.html> Goulet-Pelletier, J.-C., &
Cousineau, D. (2018) <doi:10.20982/tqmp.14.4.p242> Hedges, L. V. (1981)
<doi:10.2307/1164588> Hedges L. V., Olkin I. (1985)
<doi:10.1016/C2009-0-03396-0> Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et
al. (2023) <doi:10.1136/bmj-2022-073141> Mayer M (2023)
<https://search.r-project.org/CRAN/refmans/confintr/html/ci_proportion.html>
Stackoverflow (2014)
<https://stats.stackexchange.com/questions/82720/confidence-interval-around-binomial-estimate-of-0-or-1>
Stackoverflow (2018) <https://stats.stackexchange.com/q/338043>.")
    (license license:expat)))

(define-public r-metahd
  (package
    (name "r-metahd")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaHD" version))
       (sha256
        (base32 "05a4m2byc5d16l3nv1b1j3hzhxw2pxyg5zr6sdsvyin4vqzsk7c7"))))
    (properties `((upstream-name . "MetaHD")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rcpparmadillo
                             r-rcpp
                             r-nloptr
                             r-metafor
                             r-matrixcalc
                             r-matrix
                             r-dplyr
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=MetaHD")
    (synopsis
     "Multivariate Meta-Analysis Model for High-Dimensional Metabolomics Data")
    (description
     "This package performs multivariate meta-analysis for high-dimensional
metabolomics data for integrating and collectively analysing individual-level
data generated from multiple studies as well as for combining summary estimates.
 This approach accounts for correlation between outcomes, considers variability
within and between studies, handles missing values and uses shrinkage estimation
to allow for high dimensionality.  A detailed vignette with example datasets and
code to prepare data and analyses are available on
<https://bookdown.org/a2delivera/@code{MetaHD/>}.")
    (license license:gpl3)))

(define-public r-metaggr
  (package
    (name "r-metaggr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaggR" version))
       (sha256
        (base32 "11kqic87xrm9d046kgc0l0wfip2gimnvsbm559dbyjbrv2f27bpb"))))
    (properties `((upstream-name . "metaggR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metaggR")
    (synopsis "Calculate the Knowledge-Weighted Estimate")
    (description
     "According to a phenomenon known as \"the wisdom of the crowds,\" combining point
estimates from multiple judges often provides a more accurate aggregate estimate
than using a point estimate from a single judge.  However, if the judges use
shared information in their estimates, the simple average will over-emphasize
this common component at the expense of the judgesâ private information.  Asa
Palley & Ville SatopÃ¤Ã¤ (2021) \"Boosting the Wisdom of Crowds Within a Single
Judgment Problem: Selective Averaging Based on Peer Predictions\"
<https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes a
procedure for calculating a weighted average of the judgesâ individual
estimates such that resulting aggregate estimate appropriately combines the
judges collective information within a single estimation problem.  The authors
use both simulation and data from six experimental studies to illustrate that
the weighting procedure outperforms existing averaging-like methods, such as the
equally weighted average, trimmed average, and median.  This aggregate estimate
-- know as \"the knowledge-weighted estimate\" -- inputs a) judges estimates of a
continuous outcome (E) and b) predictions of others average estimate of this
outcome (P).  In this R-package, the function knowledge_weighted_estimate(E,P)
implements the knowledge-weighted estimate.  Its use is illustrated with a
simple stylized example and on real-world experimental data.")
    (license license:gpl2)))

(define-public r-metagear
  (package
    (name "r-metagear")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metagear" version))
       (sha256
        (base32 "1gwfk6v591xxsccbi287b90vnf5c1jfz72jxcxy9iswp0jpjflaq"))))
    (properties `((upstream-name . "metagear")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-metafor r-matrix r-mass))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=metagear")
    (synopsis
     "Comprehensive Research Synthesis Tools for Systematic Reviews and Meta-Analysis")
    (description
     "Functionalities for facilitating systematic reviews, data extractions, and
meta-analyses.  It includes a GUI (graphical user interface) to help screen the
abstracts and titles of bibliographic data; tools to assign screening effort
across multiple collaborators/reviewers and to assess inter- reviewer
reliability; tools to help automate the download and retrieval of journal PDF
articles from online databases; figure and image extractions from PDFs; web
scraping of citations; automated and manual data extraction from scatter-plot
and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews
and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in
incomplete or missing study parameters; generation of random effects sizes for
Hedges d, log response ratio, odds ratio, and correlation coefficients for Monte
Carlo experiments; covariance equations for modelling dependencies among
multiple effect sizes (e.g., effect sizes with a common control); and finally
summaries that replicate analyses and outputs from widely used but no longer
updated meta-analysis software (i.e., metawin).  Funding for this package was
supported by National Science Foundation (NSF) grants DBI-1262545 and
DEB-1451031.  CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews,
data extraction and meta-analysis with the metagear package for R. Methods in
Ecology and Evolution 7, 323-330 <doi:10.1111/2041-210X.12472>.")
    (license license:gpl2+)))

(define-public r-metage
  (package
    (name "r-metage")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaGE" version))
       (sha256
        (base32 "01507d85fpiqrbir1rjy5vczrv9cjfscwbl3746rr8x1x8danz7w"))))
    (properties `((upstream-name . "metaGE")))
    (build-system r-build-system)
    (propagated-inputs (list r-yarrr
                             r-viridis
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rfast
                             r-qqman
                             r-purrr
                             r-ks
                             r-gplots
                             r-ggrepel
                             r-ggplot2
                             r-future
                             r-emdbook
                             r-dplyr
                             r-data-table
                             r-corrplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metaGE")
    (synopsis
     "Meta-Analysis for Detecting Genotype x Environment Associations")
    (description
     "This package provides functions to perform all steps of genome-wide association
meta-analysis for studying Genotype x Environment interactions, from collecting
the data to the manhattan plot.  The procedure accounts for the potential
correlation between studies.  In addition to the Fixed and Random models, one
can investigate the relationship between QTL effects and some qualitative or
quantitative covariate via the test of contrast and the meta-regression,
respectively.  The methodology is available from: (De Walsche, A., et al. (2023)
\\doi{10.1101/2023.03.01.530237}).")
    (license license:gpl3)))

(define-public r-metagam
  (package
    (name "r-metagam")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metagam" version))
       (sha256
        (base32 "1kpxf2jxbx13if3ir9iqsqbdg6gg0l9pa3qnxmff4w5dd59by5kd"))))
    (properties `((upstream-name . "metagam")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-mgcv r-metafor r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://lifebrain.github.io/metagam/")
    (synopsis "Meta-Analysis of Generalized Additive Models")
    (description
     "Meta-analysis of generalized additive models and generalized additive mixed
models.  A typical use case is when data cannot be shared across locations, and
an overall meta-analytic fit is sought.  metagam provides functionality for
removing individual participant data from models computed using the mgcv and
gamm4 packages such that the model objects can be shared without exposing
individual data.  Furthermore, methods for meta-analysing these fits are
provided.  The implemented methods are described in Sorensen et al. (2020),
<doi:10.1016/j.neuroimage.2020.117416>, extending previous works by Schwartz and
Zanobetti (2000) and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.")
    (license license:gpl3)))

(define-public r-metafuse
  (package
    (name "r-metafuse")
    (version "2.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metafuse" version))
       (sha256
        (base32 "1zdw22f5kzvy2xr3c2csy873hcags60as567brc2sw17d0apfy8j"))))
    (properties `((upstream-name . "metafuse")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-glmnet r-evd))
    (home-page "https://cran.r-project.org/package=metafuse")
    (synopsis "Fused Lasso Approach in Regression Coefficient Clustering")
    (description
     "Fused lasso method to cluster and estimate regression coefficients of the same
covariate across different data sets when a large number of independent data
sets are combined.  Package supports Gaussian, binomial, Poisson and Cox PH
models.")
    (license license:gpl2)))

(define-public r-metaforest
  (package
    (name "r-metaforest")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaforest" version))
       (sha256
        (base32 "1b578s08621x5g2sqgi88sdlknq3zaql5xbhdggzml8c0n7lhbc1"))))
    (properties `((upstream-name . "metaforest")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger r-metafor r-gtable r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metaforest")
    (synopsis "Exploring Heterogeneity in Meta-Analysis using Random Forests")
    (description
     "Conduct random forests-based meta-analysis, obtain partial dependence plots for
metaforest and classic meta-analyses, and cross-validate and tune metaforest-
and classic meta-analyses in conjunction with the caret package.  A requirement
of classic meta-analysis is that the studies being aggregated are conceptually
similar, and ideally, close replications.  However, in many fields, there is
substantial heterogeneity between studies on the same topic.  Classic
meta-analysis lacks the power to assess more than a handful of univariate
moderators. @code{MetaForest}, by contrast, has substantial power to explore
heterogeneity in meta-analysis.  It can identify important moderators from a
larger set of potential candidates (Van Lissa, 2020).  This is an appealing
quality, because many meta-analyses have small sample sizes.  Moreover,
@code{MetaForest} yields a measure of variable importance which can be used to
identify important moderators, and offers partial prediction plots to explore
the shape of the marginal relationship between moderators and effect size.")
    (license license:gpl3)))

(define-public r-metafolio
  (package
    (name "r-metafolio")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metafolio" version))
       (sha256
        (base32 "11y2igvwwkr4dj7v8ip66w1psi9wzb8q3sy018lhaxb42s1lspbh"))))
    (properties `((upstream-name . "metafolio")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-plyr r-mass r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/seananderson/metafolio")
    (synopsis
     "Metapopulation Simulations for Conserving Salmon Through Portfolio Optimization")
    (description
     "This package provides a tool to simulate salmon metapopulations and apply
financial portfolio optimization concepts.  The package accompanies the paper
Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.")
    (license license:gpl2)))

(define-public r-metaensembler
  (package
    (name "r-metaensembler")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaEnsembleR" version))
       (sha256
        (base32 "1vxrzdm4j3h91h0fvps36a8xhs1b8cylw2cw2mhvg20lpcjjdsdv"))))
    (properties `((upstream-name . "metaEnsembleR")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest
                             r-gridextra
                             r-ggplot2
                             r-gbm
                             r-e1071
                             r-caret))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=metaEnsembleR")
    (synopsis "Automated Intuitive Package for Meta-Ensemble Learning")
    (description
     "Extends the base classes and methods of caret package for integration of base
learners.  The user can input the number of different base learners, and specify
the final learner, along with the train-validation-test data partition split
ratio.  The predictions on the unseen new data is the resultant of the ensemble
meta-learning
<https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/>
of the heterogeneous learners aimed to reduce the generalization error in the
predictive models.  It significantly lowers the barrier for the practitioners to
apply heterogeneous ensemble learning techniques in an amateur fashion to their
everyday predictive problems.")
    (license license:gpl2+)))

(define-public r-metadynminer3d
  (package
    (name "r-metadynminer3d")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metadynminer3d" version))
       (sha256
        (base32 "1y1csfp57qrv394hj96yr5v3brxw7v0a07k2pg7ypw08gshvgqjm"))))
    (properties `((upstream-name . "metadynminer3d")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-rcpp r-misc3d r-metadynminer))
    (home-page "https://metadynamics.cz/metadynminer3d/")
    (synopsis
     "Tools to Read, Analyze and Visualize Metadynamics 3D HILLS Files from 'Plumed'")
    (description
     "Metadynamics is a state of the art biomolecular simulation technique.  Plumed
Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes it
possible to perform metadynamics using various simulation codes.  The results of
metadynamics done in Plumed can be analyzed by metadynminer'.  The package
metadynminer reads 1D and 2D metadynamics hills files from Plumed package.  As
an addendum, metadynaminer3d is used to visualize 3D hills.  It uses a fast
algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037> to
calculate a free energy surface from hills.  Minima can be located and plotted
on the free energy surface.  Free energy surfaces and minima can be plotted to
produce publication quality images.")
    (license license:gpl3)))

(define-public r-metadynminer
  (package
    (name "r-metadynminer")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metadynminer" version))
       (sha256
        (base32 "0ch86qqs3872d28c8wrs24ryg2xahqsmndmzjc3z6qznkrq1adzh"))))
    (properties `((upstream-name . "metadynminer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://metadynamics.cz/metadynminer/")
    (synopsis
     "Tools to Read, Analyze and Visualize Metadynamics HILLS Files from 'Plumed'")
    (description
     "Metadynamics is a state of the art biomolecular simulation technique.  Plumed
Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes it
possible to perform metadynamics using various simulation codes.  The results of
metadynamics done in Plumed can be analyzed by metadynminer'.  The package
metadynminer reads 1D and 2D metadynamics hills files from Plumed package.  It
uses a fast algorithm by Hosek, P. and Spiwok, V. (2016)
<doi:10.1016/j.cpc.2015.08.037> to calculate a free energy surface from hills.
Minima can be located and plotted on the free energy surface.  Transition states
can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H.
(2000) <doi:10.1063/1.1323224>.  Free energy surfaces, minima and transition
paths can be plotted to produce publication quality images.")
    (license license:gpl3)))

(define-public r-metadigitise
  (package
    (name "r-metadigitise")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaDigitise" version))
       (sha256
        (base32 "1cwvb6b4a1pfixbknbjjylsmpkccgi163h0kbf7zfjaqjg2y9mzh"))))
    (properties `((upstream-name . "metaDigitise")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-magick))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metaDigitise")
    (synopsis "Extract and Summarise Data from Published Figures")
    (description
     "High-throughput, flexible and reproducible extraction of data from figures in
primary research papers. @code{metaDigitise}() can extract data and / or
automatically calculate summary statistics for users from box plots, bar plots
(e.g., mean and errors), scatter plots and histograms.")
    (license license:gpl2+)))

(define-public r-metacycle
  (package
    (name "r-metacycle")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaCycle" version))
       (sha256
        (base32 "1lh1msrcz70p5jixz5j83pqa3cid21y7m1zvlk872k16awmif00k"))))
    (properties `((upstream-name . "MetaCycle")))
    (build-system r-build-system)
    (propagated-inputs (list r-gnm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MetaCycle")
    (synopsis "Evaluate Periodicity in Large Scale Data")
    (description
     "There are two functions-meta2d and meta3d for detecting rhythmic signals from
time-series datasets.  For analyzing time-series datasets without individual
information, meta2d is suggested, which could incorporates multiple methods from
ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms.  For
analyzing time-series datasets with individual information, meta3d is suggested,
which takes use of any one of these three methods to analyze time-series data
individual by individual and gives out integrated values based on analysis
result of each individual.")
    (license license:gpl2+)))

(define-public r-metacore
  (package
    (name "r-metacore")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metacore" version))
       (sha256
        (base32 "06ba9p1by6hfagj0s43b6dwwfci5fczl2fr7kw3cchqsr90y3vfr"))))
    (properties `((upstream-name . "metacore")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readxl
                             r-r6
                             r-purrr
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://atorus-research.github.io/metacore/")
    (synopsis
     "Centralized Metadata Object Focus on Clinical Trial Data Programming Workflows")
    (description
     "Create an immutable container holding metadata for the purpose of better
enabling programming activities and functionality of other packages within the
clinical programming workflow.")
    (license license:expat)))

(define-public r-metaconvert
  (package
    (name "r-metaconvert")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaConvert" version))
       (sha256
        (base32 "0akn9y8h2f7fnxi9sbvnhqzvvnysr4q9rssj4gysnj9b9pqb7gra"))))
    (properties `((upstream-name . "metaConvert")))
    (build-system r-build-system)
    (propagated-inputs (list r-rio
                             r-numderiv
                             r-mvtnorm
                             r-metafor
                             r-estimraw
                             r-comparedf))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=metaConvert")
    (synopsis
     "An Automatic Suite for Estimation of Various Effect Size Measures")
    (description
     "Automatically estimate 11 effect size measures from a well-formatted dataset.
Various other functions can help, for example, removing dependency between
several effect sizes, or identifying differences between two datasets.  This
package is mainly designed to assist in conducting a systematic review with a
meta-analysis but can be useful to any researcher interested in estimating an
effect size.")
    (license license:gpl3+)))

(define-public r-metaconfoundr
  (package
    (name "r-metaconfoundr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaconfoundr" version))
       (sha256
        (base32 "0aiz10wjb24p52hx5srmws7myncjdvhd8kxbnxyaxg31wgxl3183"))))
    (properties `((upstream-name . "metaconfoundr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-shiny
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-forcats
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/malcolmbarrett/metaconfoundr")
    (synopsis "Visualize 'Confounder' Control in Meta-Analyses")
    (description
     "Visualize confounder control in meta-analysis.  metaconfoundr is an approach to
evaluating bias in studies used in meta-analyses based on the causal inference
framework.  Study groups create a causal diagram displaying their assumptions
about the scientific question.  From this, they develop a list of important
confounders'.  Then, they evaluate whether studies controlled for these
variables well.  metaconfoundr is a toolkit to facilitate this process and
visualize the results as heat maps, traffic light plots, and more.")
    (license license:expat)))

(define-public r-metacomp
  (package
    (name "r-metacomp")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaComp" version))
       (sha256
        (base32 "1yb115qdmf5rxmjl4y7qr0i6668n46scs4yjpfq9bmy3w9i4ji1x"))))
    (properties `((upstream-name . "MetaComp")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-plyr
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-cairo))
    (home-page "https://github.com/seninp-bioinfo/MetaComp")
    (synopsis "EDGE Taxonomy Assignments Visualization")
    (description
     "This package implements routines for metagenome sample taxonomy assignments
collection, aggregation, and visualization.  Accepts the EDGE-formatted output
from GOTTCHA/GOTTCHA2, BWA, Kraken, @code{MetaPhlAn}, DIAMOND, and Pangia.
Produces SVG and PDF heatmap-like plots comparing taxa abundances across
projects.")
    (license license:gpl2)))

(define-public r-metacom
  (package
    (name "r-metacom")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metacom" version))
       (sha256
        (base32 "0c0gzn28cl2sl52wx2n36jm9c18d9s3yqjyx88kgbmd9069z1nvk"))))
    (properties `((upstream-name . "metacom")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan))
    (home-page "https://cran.r-project.org/package=metacom")
    (synopsis "Analysis of the 'Elements of Metacommunity Structure'")
    (description
     "This package provides functions to analyze coherence, boundary clumping, and
turnover following the pattern-based metacommunity analysis of Leibold and
Mikkelson 2002 <doi:10.1034/j.1600-0706.2002.970210.x>.  The package also
includes functions to visualize ecological networks, and to calculate modularity
as a replacement to boundary clumping.")
    (license license:gpl2)))

(define-public r-metacluster
  (package
    (name "r-metacluster")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaCluster" version))
       (sha256
        (base32 "0mirhkj37cmjy82yppx097ivy2lsvhyagirdbvdmr9mx4clmva9g"))))
    (properties `((upstream-name . "metaCluster")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqinr
                             r-factoextra
                             r-dplyr
                             r-dbscan
                             r-cluster
                             r-biostrings))
    (home-page "https://cran.r-project.org/package=metaCluster")
    (synopsis "Metagenomic Clustering")
    (description
     "Clustering in metagenomics is the process of grouping of microbial contigs in
species specific bins.  This package contains functions that extract genomic
features from metagenome data, find the number of clusters for that given data
and find the best clustering algorithm for binning.")
    (license license:gpl3)))

(define-public r-metacart
  (package
    (name "r-metacart")
    (version "2.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metacart" version))
       (sha256
        (base32 "1wim72fvhx6cyc16c9gl8cwzbhlpzilk7q8gi4jnzzazqmc1kb5n"))))
    (properties `((upstream-name . "metacart")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-rcpp r-gridextra r-ggplot2))
    (home-page "https://cran.r-project.org/package=metacart")
    (synopsis
     "Meta-CART: A Flexible Approach to Identify Moderators in Meta-Analysis")
    (description
     "Meta-CART integrates classification and regression trees (CART) into
meta-analysis.  Meta-CART is a flexible approach to identify interaction effects
between moderators in meta-analysis.  The method is described in Dusseldorp et
al. (2014) <doi:10.1037/hea0000018> and Li et al. (2017)
<doi:10.1111/bmsp.12088>.")
    (license license:gpl2+)))

(define-public r-metabup
  (package
    (name "r-metabup")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metabup" version))
       (sha256
        (base32 "1lwndn46bayw6jzxxpd8hywnzxydiz0nlm33nxr855035rmv231k"))))
    (properties `((upstream-name . "metabup")))
    (build-system r-build-system)
    (propagated-inputs (list r-partitions r-ggplot2))
    (home-page "https://cran.r-project.org/package=metabup")
    (synopsis "Bayesian Meta-Analysis Using Basic Uncertain Pooling")
    (description
     "This package contains functions that allow Bayesian meta-analysis (1) with
binomial data, counts(y) and total counts (n) or, (2) with user-supplied point
estimates and associated variances.  Case (1) provides an analysis based on the
logit transformation of the sample proportion.  This methodology is also
appropriate for combining data from sample surveys and related sources.  The
functions can calculate the corresponding similarity matrix.  More details can
be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022)
<doi:10.1007/s42519-018-0027-2>, Evans and Sedransk (2001)
<doi:10.1093/biomet/88.3.643>, and Malec and Sedransk (1992)
<doi:10.1093/biomet/79.3.593>.")
    (license license:gpl3+)))

(define-public r-metaboqc
  (package
    (name "r-metaboqc")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaboQC" version))
       (sha256
        (base32 "1rrhssm882xkp6ydgy3qba9c7ww3p6vxy04z4v7v6v7ilc5bvrgi"))))
    (properties `((upstream-name . "MetaboQC")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr))
    (home-page "https://cran.r-project.org/package=MetaboQC")
    (synopsis "Normalize Metabolomic Data using QC Signal")
    (description
     "Takes QC signal for each day and normalize metabolomic data that has been
acquired in a certain period of time.  At least three QC per day are required.")
    (license license:gpl2)))

(define-public r-metabolomicsbasics
  (package
    (name "r-metabolomicsbasics")
    (version "1.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetabolomicsBasics" version))
       (sha256
        (base32 "09yfakahah88sqranw56mr56jkqx6bgq52rcaj2bablr7bxswwvh"))))
    (properties `((upstream-name . "MetabolomicsBasics")))
    (build-system r-build-system)
    (propagated-inputs (list r-webchem
                             r-rpart
                             r-rlang
                             r-plyr
                             r-pcamethods
                             r-interpretmsspectrum
                             r-e1071
                             r-caret
                             r-c50))
    (home-page "https://github.com/janlisec/MetabolomicsBasics")
    (synopsis "Basic Functions to Investigate Metabolomics Data Matrices")
    (description
     "This package provides a set of functions to investigate raw data from
(metabol)omics experiments intended to be used on a raw data matrix, i.e.
following peak picking and signal deconvolution.  Functions can be used to
normalize data, detect biomarkers and perform sample classification.  A detailed
description of best practice usage may be found in the publication
<doi:10.1007/978-1-4939-7819-9_20>.")
    (license license:gpl3)))

(define-public r-metabolicsyndrome
  (package
    (name "r-metabolicsyndrome")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetabolicSyndrome" version))
       (sha256
        (base32 "0hacywgkpsa3j96k4z24j6r3srm22i8vs53010nyakdsczzlizvn"))))
    (properties `((upstream-name . "MetabolicSyndrome")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://github.com/jagadishramasamy/metsynd")
    (synopsis "Diagnosis of Metabolic Syndrome")
    (description
     "The modified Adult Treatment Panel -III guidelines (ATP-III) proposed by
American Heart Association (AHA) and National Heart, Lung and Blood Institute
(NHLBI) are used widely for the clinical diagnosis of Metabolic Syndrome.  The
AHA-NHLBI criteria advise using parameters such as waist circumference (WC),
systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma
glucose (FPG), triglycerides (TG) and high-density lipoprotein cholesterol
(HDLC) for diagnosis of metabolic syndrome.  Each parameter has to be
interpreted based on the proposed cut-offs, making the diagnosis slightly
complex and error-prone.  This package is developed by incorporating the
modified ATP-III guidelines, and it will aid in the easy and quick diagnosis of
metabolic syndrome in busy healthcare settings and also for research purposes.
The modified ATP-III-AHA-NHLBI criteria for the diagnosis is described by Grundy
et al ., (2005) <doi:10.1161/CIRCULATIONAHA.105.169404>.")
    (license license:gpl3)))

(define-public r-metabolicsurv
  (package
    (name "r-metabolicsurv")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetabolicSurv" version))
       (sha256
        (base32 "1c8i3a3gnbvlkjh0vqmz1igzaf8q87cb55l3kijwi0z23ywrbqch"))))
    (properties `((upstream-name . "MetabolicSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survminer
                             r-survival
                             r-superpc
                             r-rms
                             r-rdpack
                             r-pls
                             r-matrixstats
                             r-glmnet
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/OlajumokeEvangelina/MetabolicSurv")
    (synopsis
     "Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature")
    (description
     "An approach to identifies metabolic biomarker signature for metabolic data by
discovering predictive metabolite for predicting survival and classifying
patients into risk groups.  Classifiers are constructed as a linear combination
of predictive/important metabolites, prognostic factors and treatment effects if
necessary.  Several methods were implemented to reduce the metabolomics matrix
such as the principle component analysis of Wold Svante et al. (1987)
<doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert Tibshirani
(1998)
<doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the
elastic net approach by Hui Zou and Trevor Hastie (2005)
<doi:10.1111/j.1467-9868.2005.00503.x>.  Sensitivity analysis on the quantile
used for the classification can also be accessed to check the deviation of the
classification group based on the quantile specified.  Large scale cross
validation can be performed in order to investigate the mostly selected
predictive metabolites and for internal validation.  During the evaluation
process, validation is accessed using the hazard ratios (HR) distribution of the
test set and inference is mainly based on resampling and permutations technique.")
    (license license:gpl3)))

(define-public r-metabolic
  (package
    (name "r-metabolic")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metabolic" version))
       (sha256
        (base32 "01jzv35zf2zk5gdjd2f2ql187krxh4m7fg87dy9iv6z0h0vxnxgn"))))
    (properties `((upstream-name . "metabolic")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-scales
                             r-rmarkdown
                             r-purrr
                             r-patchwork
                             r-meta
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-ggimage
                             r-ggfittext
                             r-forcats
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/fmmattioni/metabolic")
    (synopsis "Datasets and Functions for Reproducing Meta-Analyses")
    (description
     "Dataset and functions from the meta-analysis published in Medicine & Science in
Sports & Exercise.  It contains all the data and functions to reproduce the
analysis. \"Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk
Factors in Health and Disease: A Meta-analysis\".  Felipe Mattioni Maturana,
Peter Martus, Stephan Zipfel, Andreas M NieÃ (2020)
<doi:10.1249/MSS.0000000000002506>.")
    (license license:cc0)))

(define-public r-metabolanalyze
  (package
    (name "r-metabolanalyze")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetabolAnalyze" version))
       (sha256
        (base32 "070qws759nczjrfdlh7jpbyjz4z3zm1hzb7vcvfgfks82wwb2ny0"))))
    (properties `((upstream-name . "MetabolAnalyze")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mclust r-gtools r-gplots r-ellipse))
    (home-page "https://cran.r-project.org/package=MetabolAnalyze")
    (synopsis "Probabilistic Latent Variable Models for Metabolomic Data")
    (description
     "Fits probabilistic principal components analysis, probabilistic principal
components and covariates analysis and mixtures of probabilistic principal
components models to metabolomic spectral data.")
    (license license:gpl2)))

(define-public r-metabodata
  (package
    (name "r-metabodata")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaboData" version))
       (sha256
        (base32 "0a8jz7q0a5dri5p2qyw0ah0j4w0xjyfj6gkh2gcsazac1d5fl02i"))))
    (properties `((upstream-name . "metaboData")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-piggyback
                             r-magrittr
                             r-fs
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://aberhrml.github.io/metaboData/")
    (synopsis "Example Metabolomics Data Sets")
    (description
     "Data sets from a variety of biological sample matrices, analysed using a number
of mass spectrometry based metabolomic analytical techniques.  The example data
sets are stored remotely using @code{GitHub} releases
<https://github.com/@code{aberHRML/metaboData/releases>} which can be accessed
from R using the package.  The package also includes the abr1 FIE-MS data set
from the FIEmspro package <https://users.aber.ac.uk/jhd/>
<doi:10.1038/nprot.2007.511>.")
    (license license:gpl3+)))

(define-public r-metabma
  (package
    (name "r-metabma")
    (version "0.6.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaBMA" version))
       (sha256
        (base32 "1y05c8mgim7v2991r1hr3ahx0mjji6gmp9mz25f0csl69kf3zr61"))))
    (properties `((upstream-name . "metaBMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mvtnorm
                             r-logspline
                             r-laplacesdemon
                             r-coda
                             r-bridgesampling
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/danheck/metaBMA")
    (synopsis
     "Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis")
    (description
     "Computes the posterior model probabilities for standard meta-analysis models
(null model vs. alternative model assuming either fixed- or random-effects,
respectively).  These posterior probabilities are used to estimate the overall
mean effect size as the weighted average of the mean effect size estimates of
the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck,
Cesario, Jonas, & Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>).  The
user can define a wide range of non-informative or informative priors for the
mean effect size and the heterogeneity coefficient.  Moreover, using
pre-compiled Stan models, meta-analysis with continuous and discrete moderators
with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested.  This allows
to compute Bayes factors and perform Bayesian model averaging across random- and
fixed-effects meta-analysis with and without moderators.  For a primer on
Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, &
Wagenmakers (2021, <doi:10.1177/25152459211031256>).")
    (license license:gpl3)))

(define-public r-metablue
  (package
    (name "r-metablue")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metaBLUE" version))
       (sha256
        (base32 "0ppn4bvr10z32pghmv4wjv86k6n5y5bkfxc6h5mvb556v6jnl6d8"))))
    (properties `((upstream-name . "metaBLUE")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://cran.r-project.org/package=metaBLUE")
    (synopsis
     "BLUE for Combining Location and Scale Information in a Meta-Analysis")
    (description
     "The sample mean and standard deviation are two commonly used statistics in
meta-analyses, but some trials use other summary statistics such as the median
and quartiles to report the results.  Therefore, researchers need to transform
those information back to the sample mean and standard deviation.  This package
implemented sample mean estimators by Luo et al. (2016)
<@code{arXiv:1505.05687>}, sample standard deviation estimators by Wan et al.
(2014) <@code{arXiv:1407.8038>}, and the best linear unbiased estimators (BLUEs)
of location and scale parameters by Yang et al. (2018, submitted) based on
sample quantiles derived summaries in a meta-analysis.")
    (license license:gpl2)))

(define-public r-metabias
  (package
    (name "r-metabias")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "metabias" version))
       (sha256
        (base32 "0xgckbyxjb6s43b3z48cxb2jbyd7jaw17x0w3wiafqjbz9ahzdmk"))))
    (properties `((upstream-name . "metabias")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://github.com/mathurlabstanford/metabias")
    (synopsis "Meta-Analysis for Within-Study and/or Across-Study Biases")
    (description
     "This package provides common components (classes, methods, documentation) for
packages that conduct meta-analytic corrections and sensitivity analyses for
within-study and/or across-study biases in meta-analysis.  See the packages
@code{PublicationBias}', phacking', and multibiasmeta'.  These package implement
methods described in, respectively: Mathur & @code{VanderWeele} (2020)
<doi:10.31219/osf.io/s9dp6>; Mathur (2022) <doi:10.31219/osf.io/ezjsx>; Mathur
(2022) <doi:10.31219/osf.io/u7vcb>.")
    (license license:expat)))

(define-public r-metaanalyser
  (package
    (name "r-metaanalyser")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MetaAnalyser" version))
       (sha256
        (base32 "0k58325nb7svihhpga7kdbryr9bi5470qsb8aa5llpiggs8j7sfz"))))
    (properties `((upstream-name . "MetaAnalyser")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rstudioapi r-ggvis r-dt))
    (home-page "https://github.com/chjackson/MetaAnalyser")
    (synopsis
     "An Interactive Visualisation of Meta-Analysis as a Physical Weighing Machine")
    (description
     "An interactive application to visualise meta-analysis data as a physical
weighing machine.  The interface is based on the Shiny web application
framework, though can be run locally and with the user's own data.")
    (license license:gpl2+)))

(define-public r-meta4diag
  (package
    (name "r-meta4diag")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meta4diag" version))
       (sha256
        (base32 "155xc313hvxy7n45rn9kmhhnkilfrazq5fgs2jbzhpgz3ddxfqkc"))))
    (properties `((upstream-name . "meta4diag")))
    (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-sp r-shinybs r-shiny r-catools))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=meta4diag")
    (synopsis "Meta-Analysis for Diagnostic Test Studies")
    (description
     "Bayesian inference analysis for bivariate meta-analysis of diagnostic test
studies using integrated nested Laplace approximation with INLA. A purpose built
graphic user interface is available.  The installation of R package INLA is
compulsory for successful usage.  The INLA package can be obtained from
<https://www.r-inla.org>.  We recommend the testing version, which can be
downloaded by running: install.packages(\"INLA\",
repos=c(@code{getOption(\"repos}\"),
INLA=\"https://inla.r-inla-download.org/R/testing\"), dep=TRUE).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-meta-shrinkage
  (package
    (name "r-meta-shrinkage")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meta.shrinkage" version))
       (sha256
        (base32 "15svx5d6bfy947kkc2na6yapxwliiwh757w8xahsdkgrxhzm8qc8"))))
    (properties `((upstream-name . "meta.shrinkage")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=meta.shrinkage")
    (synopsis "Meta-Analyses for Simultaneously Estimating Individual Means")
    (description
     "Implement meta-analyses for simultaneously estimating individual means with
shrinkage, isotonic regression and pretests.  Include our original
implementation of the isotonic regression via the pool-adjacent-violators
algorithm (PAVA) algorithm.  For the pretest estimator, the confidence interval
for individual means are provided.  Methodologies were published in Taketomi et
al. (2021) <doi:10.3390/axioms10040267>, Taketomi et al. (2022)
<doi:10.3390/a15010026>, Taketomi et al. (2023-) (under review).")
    (license license:gpl2)))

(define-public r-meta
  (package
    (name "r-meta")
    (version "7.0-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meta" version))
       (sha256
        (base32 "0zfyjyaabwgzlnws8ny2r7f1qr6h827kba4fddv44aqd9z4xksnq"))))
    (properties `((upstream-name . "meta")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stringr
                             r-readr
                             r-purrr
                             r-metafor
                             r-metadat
                             r-magrittr
                             r-lme4
                             r-dplyr
                             r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=meta")
    (synopsis "General Package for Meta-Analysis")
    (description
     "User-friendly general package providing standard methods for meta-analysis and
supporting Schwarzer, Carpenter, and RÃ¼cker <DOI:10.1007/978-3-319-21416-0>,
\"Meta-Analysis with R\" (2015): - common effect and random effects meta-analysis;
- several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); -
three-level meta-analysis model; - generalised linear mixed model; -
Hartung-Knapp method for random effects model; - Kenward-Roger method for random
effects model; - prediction interval; - statistical tests for funnel plot
asymmetry; - trim-and-fill method to evaluate bias in meta-analysis; -
meta-regression; - cumulative meta-analysis and leave-one-out meta-analysis; -
import data from @code{RevMan} 5'; - produce forest plot summarising several
(subgroup) meta-analyses.")
    (license license:gpl2+)))

(define-public r-mestim
  (package
    (name "r-mestim")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mestim" version))
       (sha256
        (base32 "1dl1fx6aqdz584d79nbm4ykgn915d6mr0m48m83pbr3k6q18varg"))))
    (properties `((upstream-name . "Mestim")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Mestim")
    (synopsis
     "Computes the Variance-Covariance Matrix of Multidimensional Parameters Using M-Estimation")
    (description
     "This package provides a flexible framework for estimating the
variance-covariance matrix of estimated parameters.  Estimation relies on
unbiased estimating functions to compute the empirical sandwich variance. (i.e.,
M-estimation in the vein of Tsiatis et al. (2019) <doi:10.1201/9780429192692>.")
    (license (license:fsdg-compatible "MIT + file LICENCE"))))

(define-public r-messydates
  (package
    (name "r-messydates")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "messydates" version))
       (sha256
        (base32 "1zljfs9igcm95s8dmiqn18b4dhpzgrll6zcfbwwsi35kl03b7655"))))
    (properties `((upstream-name . "messydates")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr r-purrr r-lubridate r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://globalgov.github.io/messydates/")
    (synopsis "Flexible Class for Messy Dates")
    (description
     "This package contains a set of tools for constructing and coercing into and from
the \"mdate\" class.  This date class implements ISO 8601-2:2019(E) and allows
regular dates to be annotated to express unspecified date components,
approximate or uncertain date components, date ranges, and sets of dates.  This
is useful for describing and analysing temporal information, whether historical
or recent, where date precision may vary.")
    (license license:expat)))

(define-public r-messy-cats
  (package
    (name "r-messy-cats")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "messy.cats" version))
       (sha256
        (base32 "0znjp1wcrkpgm778z5iqw91bnk8vv0x04dpng8pcmh0igsmc3j68"))))
    (properties `((upstream-name . "messy.cats")))
    (build-system r-build-system)
    (propagated-inputs (list r-varhandle
                             r-stringr
                             r-stringdist
                             r-rapportools
                             r-gt
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=messy.cats")
    (synopsis "Employs String Distance Tools to Help Clean Categorical Data")
    (description
     "Matching with string distance has never been easier! messy.cats contains various
functions that employ string distance tools in order to make data management
easier for users working with categorical data.  Categorical data, especially
user inputted categorical data that often tends to be plagued by typos, can be
difficult to work with.  messy.cats aims to provide functions that make cleaning
categorical data simple and easy.")
    (license license:expat)))

(define-public r-messi
  (package
    (name "r-messi")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "messi" version))
       (sha256
        (base32 "1z3fjxfmvxc9zr98zmkd84rj27iabjmw71hwqgnbjwcw5mnw2b5h"))))
    (properties `((upstream-name . "messi")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress r-patchwork r-mass r-ggplot2))
    (home-page "https://github.com/umich-cphds/messi")
    (synopsis
     "Mediation Analysis with External Summary-Level Information on Total Effect")
    (description
     "Fits the hard constraint, soft constraint, and unconstrained models in Boss et
al. (2023) <@code{arXiv:2306.17347>} for mediation analyses with external
summary-level information on the total effect.")
    (license license:gpl2)))

(define-public r-messaging
  (package
    (name "r-messaging")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "messaging" version))
       (sha256
        (base32 "0q19cqp1zgh0yhk1ql0jqf414bhx6jwhkairq6wx2cmkli2g7k1y"))))
    (properties `((upstream-name . "messaging")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rlang r-magrittr r-glue r-dplyr))
    (home-page "https://github.com/rich-iannone/messaging")
    (synopsis "Conveniently Issue Messages, Warnings, and Errors")
    (description
     "This package provides tools for creating and issuing nicely-formatted text
within R diagnostic messages and those messages given during warnings and
errors.  The formatting of the messages can be customized using templating
features.  Issues with singular and plural forms can be handled through
specialized syntax.")
    (license license:expat)))

(define-public r-mess
  (package
    (name "r-mess")
    (version "0.5.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MESS" version))
       (sha256
        (base32 "1xyw04glhpgdcc3p96yvm346l62lsq37ml6lv59ap2ksws9pkq21"))))
    (properties `((upstream-name . "MESS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-kinship2
                             r-glmnet
                             r-ggplot2
                             r-ggformula
                             r-geepack
                             r-geem
                             r-clipr))
    (home-page "https://github.com/ekstroem/MESS")
    (synopsis "Miscellaneous Esoteric Statistical Scripts")
    (description
     "This package provides a mixed collection of useful and semi-useful diverse
statistical functions, some of which may even be referenced in The R Primer
book.")
    (license license:gpl2)))

(define-public r-meshed
  (package
    (name "r-meshed")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meshed" version))
       (sha256
        (base32 "061q6b8603jbvlqkl6ddcmw1rgy53sqbxbsxhv218f7px392b1vf"))))
    (properties `((upstream-name . "meshed")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-magrittr
                             r-glue
                             r-fnn
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=meshed")
    (synopsis "Bayesian Regression with Meshed Gaussian Processes")
    (description
     "Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP)
as described in Peruzzi, Banerjee, Finley (2020)
<doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley
(2021) <@code{arXiv:2101.03579>}, Peruzzi and Dunson (2022)
<@code{arXiv:2201.10080>}.  Funded by ERC grant 856506 and NIH grant
R01ES028804.")
    (license license:gpl3+)))

(define-public r-mertools
  (package
    (name "r-mertools")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "merTools" version))
       (sha256
        (base32 "0wxiflkqhl40rr6ibzk4c362m8jxd97jkskdxghqa45lc5vgn1qh"))))
    (properties `((upstream-name . "merTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-mvtnorm
                             r-matrix
                             r-lme4
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-broom-mixed
                             r-blme
                             r-arm
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=merTools")
    (synopsis "Tools for Analyzing Mixed Effect Regression Models")
    (description
     "This package provides methods for extracting results from mixed-effect model
objects fit with the lme4 package.  Allows construction of prediction intervals
efficiently from large scale linear and generalized linear mixed-effects models.
 This method draws from the simulation framework used in the Gelman and Hill
(2007) textbook: Data Analysis Using Regression and Multilevel/Hierarchical
Models.")
    (license license:gpl2+)))

(define-public r-merror
  (package
    (name "r-merror")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "merror" version))
       (sha256
        (base32 "14mwkwz6ih4442b4vbxqr60694ka6v0salg0qa0fgw1vfxgknz44"))))
    (properties `((upstream-name . "merror")))
    (build-system r-build-system)
    (propagated-inputs (list r-openmx))
    (home-page "https://cran.r-project.org/package=merror")
    (synopsis "Accuracy and Precision of Measurements")
    (description
     "N>=3 methods are used to measure each of n items.  The data are used to estimate
simultaneously systematic error (bias) and random error (imprecision).  Observed
measurements for each method or device are assumed to be linear functions of the
unknown true values and the errors are assumed normally distributed.  Pairwise
calibration curves and plots can be easily generated.  Unlike the ncb.od
function, the omx function builds a one-factor measurement error model using
@code{OpenMx} and allows missing values, uses full information maximum
likelihood to estimate parameters, and provides both likelihood-based and
bootstrapped confidence intervals for all parameters, in addition to Wald-type
intervals.")
    (license license:gpl2+)))

(define-public r-mero
  (package
    (name "r-mero")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MERO" version))
       (sha256
        (base32 "10wz6qrmi612y6whrn3dka606llijp5d1fffh00b1pprx2rcfl8w"))))
    (properties `((upstream-name . "MERO")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress r-missforest r-ggpubr r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=MERO")
    (synopsis
     "Performing Monte Carlo Expectation Maximization Random Forest Imputation for Biological Data")
    (description
     "Perform missing value imputation for biological data using the random forest
algorithm, the imputation aim to keep the original mean and standard deviation
consistent after imputation.")
    (license license:gpl3)))

(define-public r-merlin
  (package
    (name "r-merlin")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "merlin" version))
       (sha256
        (base32 "11in84pxgz76l2cfpd7m03fai1gwdhp2rzrn3pbwrpywgq210pmj"))))
    (properties `((upstream-name . "merlin")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-statmod r-randtoolbox r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://www.mjcrowther.co.uk/software/merlin")
    (synopsis
     "Mixed Effects Regression for Linear, Non-Linear and User-Defined Models")
    (description
     "Fits linear, non-linear, and user-defined mixed effects regression models
following the framework developed by Crowther (2017) <@code{arXiv:1710.02223>}.
merlin can fit multivariate outcome models of any type, each of which could be
repeatedly measured (longitudinal), with any number of levels, and with any
number of random effects at each level.  Standard distributions/models available
include the Bernoulli, Gaussian, Poisson, beta, negative-binomial, and
time-to-event/survival models include the exponential, Gompertz, Royston-Parmar,
Weibull and general hazard model.  merlin provides a flexible predictor syntax,
allowing the user to define variables, random effects, spline and fractional
polynomial functions, functions of other outcome models, and any interaction
between each of them.  Non-linear and time-dependent effects are seamlessly
incorporated into the predictor.  merlin allows multivariate normal random
effects, which are integrated out using Gaussian quadrature or Monte-Carlo
integration.  Note, merlin is based on the Stata package of the same name,
described in Crowther (2018) <@code{arXiv:1806.01615>}.")
    (license license:gpl3+)))

(define-public r-mergingtools
  (package
    (name "r-mergingtools")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mergingTools" version))
       (sha256
        (base32 "04ryamfb19js6dq4iv2j0p2dlgrayl3zis2r1kic1wfvh4a46ihg"))))
    (properties `((upstream-name . "mergingTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-mass
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mergingTools")
    (synopsis
     "Tools to Merge Hardware Event Monitors (HEMs) Coming from Separate Subexperiments into One Single Dataframe")
    (description
     "Implementation of two tools to merge Hardware Event Monitors (HEMs) from
different subexperiments.  Hardware Reading and Merging (HRM), which uses order
statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a
multivariate normal distribution.  The reference paper for HRM is: S. Vilardell,
I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, \"HRM: Merging
Hardware Event Monitors for Improved Timing Analysis of Complex MP@code{SoCs},\"
in IEEE Transactions on Computer-Aided Design of Integrated Circuits and
Systems, vol.  39, no.  11, pp.  3662-3673, Nov.  2020,
<doi:10.1109/TCAD.2020.3013051>.  For MUCH: S. Vilardell, I. Serra, E. Mezzetti,
J. Abella, and F. J. Cazorla.  2021. \"MUCH: exploiting pairwise hardware event
monitor correlations for improved timing analysis of complex MP@code{SoCs}\".  In
Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC 21).
Association for Computing Machinery. <doi:10.1145/3412841.3441931>.  This work
has been supported by the European Research Council (ERC) under the European
Union's Horizon 2020 research and innovation programme (grant agreement No.
772773).")
    (license license:gpl3)))

(define-public r-mergetrees
  (package
    (name "r-mergetrees")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mergeTrees" version))
       (sha256
        (base32 "17aqgfd1ky5zq6i9s1djvdqjdd8lzkblf0cdw82i9mpzfhcz1fav"))))
    (properties `((upstream-name . "mergeTrees")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=mergeTrees")
    (synopsis "Aggregating Trees")
    (description
     "Aggregates a set of trees with the same leaves to create a consensus tree.  The
trees are typically obtained via hierarchical clustering, hence the hclust
format is used to encode both the aggregated trees and the final consensus tree.
 The method is exact and proven to be O(nqlog(n)), n being the individuals and q
being the number of trees to aggregate.")
    (license license:gpl2+)))

(define-public r-mergenstudio
  (package
    (name "r-mergenstudio")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mergenstudio" version))
       (sha256
        (base32 "1s83h2xglmf21sg6brb67lcw3kch097vykmr3bpc6h8ljmr0qi27"))))
    (properties `((upstream-name . "mergenstudio")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-yaml
                             r-waiter
                             r-stringr
                             r-shinyjs
                             r-shinyfiles
                             r-shiny-i18n
                             r-shiny
                             r-rvest
                             r-rstudioapi
                             r-rmarkdown
                             r-rlang
                             r-purrr
                             r-mergen
                             r-magrittr
                             r-jsonlite
                             r-ids
                             r-httr2
                             r-htmlwidgets
                             r-htmltools
                             r-glue
                             r-fs
                             r-fontawesome
                             r-colorspace
                             r-cli
                             r-bslib
                             r-assertthat))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=mergenstudio")
    (synopsis
     "'Mergen' Studio: An 'RStudio' Addin Wrapper for the 'Mergen' Package")
    (description
     "An RStudio Addin wrapper for the mergen package.  This package employs
artificial intelligence to convert data analysis questions into executable code,
explanations, and algorithms.  This package makes it easier to use Large
Language Models in your development environment by providing a chat-like
interface, while also allowing you to inspect and execute the returned code.")
    (license license:expat)))

(define-public r-mergen
  (package
    (name "r-mergen")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mergen" version))
       (sha256
        (base32 "0mrrqxjj0w2yw5q0g23wq4knpvv2hi4lhc9z7289id4n3xhw37xz"))))
    (properties `((upstream-name . "mergen")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown
                             r-openai
                             r-jsonlite
                             r-httr
                             r-biocmanager
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/BIMSBbioinfo/mergen")
    (synopsis
     "AI-Driven Code Generation, Explanation and Execution for Data Analysis")
    (description
     "Employing artificial intelligence to convert data analysis questions into
executable code, explanations, and algorithms.  The self-correction feature
ensures the generated code is optimized for performance and accuracy.  mergen
features a user-friendly chat interface, enabling users to interact with the AI
agent and extract valuable insights from their data effortlessly.")
    (license license:expat)))

(define-public r-merderiv
  (package
    (name "r-merderiv")
    (version "0.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "merDeriv" version))
       (sha256
        (base32 "1piswi4l7ri3251shmanhygipmsw8zg701jqd0am1s76kj16l9cc"))))
    (properties `((upstream-name . "merDeriv")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-numderiv
                             r-nonnest2
                             r-matrix
                             r-lme4
                             r-lavaan))
    (home-page "https://github.com/nctingwang/merDeriv")
    (synopsis
     "Case-Wise and Cluster-Wise Derivatives for Mixed Effects Models")
    (description
     "Compute case-wise and cluster-wise derivative for mixed effects models with
respect to fixed effects parameter, random effect (co)variances, and residual
variance.  This material is partially based on work supported by the National
Science Foundation under Grant Number 1460719.")
    (license license:gpl2+)))

(define-public r-mercator
  (package
    (name "r-mercator")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mercator" version))
       (sha256
        (base32 "0wj8bsrj2v4icj0byllaxyqnph3zpx98gvbxhdzy0zamyrfk57ss"))))
    (properties `((upstream-name . "Mercator")))
    (build-system r-build-system)
    (propagated-inputs (list r-umap
                             r-thresher
                             r-rtsne
                             r-polychrome
                             r-kohonen
                             r-kernsmooth
                             r-igraph
                             r-flexmix
                             r-dendextend
                             r-cluster
                             r-classdiscovery))
    (native-inputs (list r-knitr))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis "Clustering and Visualizing Distance Matrices")
    (description
     "Defines the classes used to explore, cluster and visualize distance matrices,
especially those arising from binary data.  See Abrams and colleagues, 2021,
<doi:10.1093/bioinformatics/btab037>.")
    (license license:asl2.0)))

(define-public r-mepdf
  (package
    (name "r-mepdf")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MEPDF" version))
       (sha256
        (base32 "15hbp7g5dsdpvi239jm6jn11fn371ir6la31g0flqkilq6sr1sqd"))))
    (properties `((upstream-name . "MEPDF")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-plyr r-mvtnorm r-gtools))
    (home-page "https://cran.r-project.org/package=MEPDF")
    (synopsis
     "Creation of Empirical Density Functions Based on Multivariate Data")
    (description
     "Based on the input data an n-dimensional cube with sub cells of user specified
side length is created.  The number of sample points which fall in each sub cube
is counted, and with the cell volume and overall sample size an empirical
probability can be computed.  A number of cubes of higher resolution can be
superimposed.  The basic method stems from J.L. Bentley in \"Multidimensional
Divide and Conquer\".  J. L. Bentley (1980) <doi:10.1145/358841.358850>.
Furthermore a simple kernel density estimation method is made available, as well
as an expansion of Bentleys method, which offers a kernel approach for the grid
method.")
    (license license:gpl2)))

(define-public r-mendelianrandomization
  (package
    (name "r-mendelianrandomization")
    (version "0.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MendelianRandomization" version))
       (sha256
        (base32 "0g71h1hwvm0ldrklvlliq2ylglb1w7zli0x5sh7z4934h8gyjl88"))))
    (properties `((upstream-name . "MendelianRandomization")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase
                             r-rmarkdown
                             r-rjson
                             r-rcpparmadillo
                             r-rcpp
                             r-quantreg
                             r-plotly
                             r-numderiv
                             r-matrix
                             r-knitr
                             r-iterpc
                             r-glmnet
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MendelianRandomization")
    (synopsis "Mendelian Randomization Package")
    (description
     "Encodes several methods for performing Mendelian randomization analyses with
summarized data.  Summarized data on genetic associations with the exposure and
with the outcome can be obtained from large consortia.  These data can be used
for obtaining causal estimates using instrumental variable methods.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-memss
  (package
    (name "r-memss")
    (version "0.9-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MEMSS" version))
       (sha256
        (base32 "0fq85jrajfwavq2fvsw2x9hknc57wc253zw2vw970f04khfbragd"))))
    (properties `((upstream-name . "MEMSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-lme4))
    (home-page "https://cran.r-project.org/package=MEMSS")
    (synopsis "Data Sets from Mixed-Effects Models in S")
    (description
     "Data sets and sample analyses from Pinheiro and Bates, \"Mixed-effects Models in
S and S-PLUS\" (Springer, 2000).")
    (license license:gpl2+)))

(define-public r-memoria
  (package
    (name "r-memoria")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memoria" version))
       (sha256
        (base32 "05nx6xkgf1mkn1ajjizwp66xhn7ddbvgdnmkdbcl4s7sq456vczw"))))
    (properties `((upstream-name . "memoria")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-viridislite
                             r-viridis
                             r-tidyr
                             r-stringr
                             r-ranger
                             r-hh
                             r-ggplot2
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=memoria")
    (synopsis
     "Quantifying Ecological Memory in Palaeoecological Datasets and Other Long Time-Series")
    (description
     "This package provides tools to quantify ecological memory in long time-series
with Random Forest models (Breiman 2001 <doi:10.1023/A:1010933404324>) fitted
with the ranger library (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>).
Particularly oriented to palaeoecological datasets and simulated pollen curves
produced by the @code{virtualPollen} package, but also applicable to other long
time-series involving a set of environmental drivers and a biotic response.")
    (license license:gpl2+)))

(define-public r-memor
  (package
    (name "r-memor")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memor" version))
       (sha256
        (base32 "1b3rjh121in7gi6khbhh2d1r0998a24ifcg6hpv8cdksl6lhm330"))))
    (properties `((upstream-name . "memor")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-rmarkdown r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hebrewseniorlife/memor")
    (synopsis "'rmarkdown' Template that Can be Highly Customized")
    (description
     "This package provides a rmarkdown template that supports company logo, contact
info, watermarks and more.  Currently restricted to Latex'/'Markdown'; a similar
HTML theme will be added in the future.")
    (license license:gpl3)))

(define-public r-memoir
  (package
    (name "r-memoir")
    (version "1.2-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memoiR" version))
       (sha256
        (base32 "1lqaw3a4wlfvbgxphzr6fwykl3b4glj3cmb7jywcdhbik2lngjfn"))))
    (properties `((upstream-name . "memoiR")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-usethis r-rmdformats r-rmarkdown r-distill
                             r-bookdown))
    (native-inputs (list r-knitr))
    (home-page "https://ericmarcon.github.io/memoiR/")
    (synopsis "R Markdown and Bookdown Templates to Publish Documents")
    (description
     "Producing high-quality documents suitable for publication directly from R is
made possible by the R Markdown ecosystem. @code{memoiR} makes it easy.  It
provides templates to knit memoirs, articles and slideshows with helpers to
publish the documents on @code{GitHub} Pages and activate continuous
integration.")
    (license license:gpl3+)))

(define-public r-memofunc
  (package
    (name "r-memofunc")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memofunc" version))
       (sha256
        (base32 "02y32mnwafqnsvs3vy4g8v0jmdvnx935dqavy3q1f2n8c76p0bk9"))))
    (properties `((upstream-name . "memofunc")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid r-magrittr r-digest))
    (home-page "https://github.com/rwetherall/memofunc")
    (synopsis "Function Memoization")
    (description
     "This package provides a simple way to memoize function results to improve
performance by eliminating unnecessary computation or data retrieval activities.")
    (license license:gpl3)))

(define-public r-memochange
  (package
    (name "r-memochange")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memochange" version))
       (sha256
        (base32 "144134kpchb7ps8b2g7pyga5add0iqphzcwygf11aybmbzmp90x7"))))
    (properties `((upstream-name . "memochange")))
    (build-system r-build-system)
    (propagated-inputs (list r-urca
                             r-strucchange
                             r-sandwich
                             r-longmemoryts
                             r-longmemo
                             r-fracdiff
                             r-forecast))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=memochange")
    (synopsis
     "Testing for Structural Breaks under Long Memory and Testing for Changes in Persistence")
    (description
     "Test procedures and break point estimators for persistent processes that exhibit
structural breaks in mean or in persistence.  On the one hand the package
contains the most popular approaches for testing whether a time series exhibits
a break in persistence from I(0) to I(1) or vice versa, such as those of Busetti
and Taylor (2004) and Leybourne, Kim, and Taylor (2007).  The approach by
Martins and Rodrigues (2014), which allows to detect changes from I(d1) to I(d2)
with d1 and d2 being non-integers, is included as well.  In case the tests
reject the null of constant persistence, various breakpoint estimators are
available to detect the point of the break as well as the order of integration
in the two regimes.  On the other hand the package contains the most popular
approaches to test for a change-in-mean of a long-memory time series, which were
recently reviewed by Wenger, Leschinski, and Sibbertsen (2018).  These include
memory robust versions of the CUSUM, sup-Wald, and Wilcoxon type tests.  The
tests either utilize consistent estimates of the long-run variance or a self
normalization approach in their test statistics.  Betken (2016)
<doi:10.1111/jtsa.12187> Busetti and Taylor (2004)
<doi:10.1016/j.jeconom.2003.10.028> Dehling, Rooch and Taqqu (2012)
<doi:10.1111/j.1467-9469.2012.00799.x> Harvey, Leybourne and Taylor (2006)
<doi:10.1016/j.jeconom.2005.07.002> Horvath and Kokoszka (1997)
<doi:10.1016/S0378-3758(96)00208-X> Hualde and Iacone (2017)
<doi:10.1016/j.econlet.2016.10.014> Iacone, Leybourne and Taylor (2014)
<doi:10.1111/jtsa.12049> Leybourne, Kim, Smith, and Newbold (2003)
<doi:10.1111/1368-423X.t01-1-00110> Leybourne and Taylor (2004)
<doi:10.1016/j.econlet.2003.12.015> Leybourne, Kim, and Taylor (2007):
<doi:10.1111/j.1467-9892.2006.00517.x> Martins and Rodrigues (2014)
<doi:10.1016/j.csda.2012.07.021> Shao (2011)
<doi:10.1111/j.1467-9892.2010.00717.x> Sibbertsen and Kruse (2009)
<doi:10.1111/j.1467-9892.2009.00611.x> Wang (2008)
<doi:10.1080/00949650701216604> Wenger, Leschinski and Sibbertsen (2018)
<doi:10.1016/j.econlet.2017.12.007>.")
    (license license:gpl2)))

(define-public r-memo
  (package
    (name "r-memo")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memo" version))
       (sha256
        (base32 "1gqdb8y2khcnd1h2906kz1k7x58lw2ri48s9glfjq6whpj6bzpdk"))))
    (properties `((upstream-name . "memo")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=memo")
    (synopsis "In-Memory Caching of Repeated Computations (Memoization)")
    (description
     "This package provides a simple in-memory, LRU cache that can be wrapped around
any function to memoize it.  The cache is keyed on a hash of the input data
(using digest') or on pointer equivalence.")
    (license license:expat)))

(define-public r-memify
  (package
    (name "r-memify")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memify" version))
       (sha256
        (base32 "1dnbaz57v50mg2kxn5y7cdfrsv1h046cwqh48mpabgh6vf018w4y"))))
    (properties `((upstream-name . "memify")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=memify")
    (synopsis "Constructing Functions That Keep State")
    (description
     "This package provides a simple way to construct and maintain functions that keep
state i.e.  remember their argument lists.  This can be useful when one needs to
repeatedly invoke the same function with only a small number of argument changes
at each invocation.")
    (license license:gpl3)))

(define-public r-memgene
  (package
    (name "r-memgene")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memgene" version))
       (sha256
        (base32 "1f1v651vab4b3bfxn8wp5p848h6vy7ylr52zirwhnhxj37fzhkq6"))))
    (properties `((upstream-name . "memgene")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-sp r-raster r-gdistance r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=memgene")
    (synopsis
     "Spatial Pattern Detection in Genetic Distance Data Using Moran's Eigenvector Maps")
    (description
     "Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector
Maps (MEM) to extract only the spatial component of genetic variation.  Has
applications in landscape genetics where the movement and dispersal of organisms
are studied using neutral genetic variation.")
    (license license:gpl2+)))

(define-public r-memery
  (package
    (name "r-memery")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memery" version))
       (sha256
        (base32 "0rz8pcxvfxg4iz1l46jkf95yyfn831zh31x28hmpi8i4hsj0607a"))))
    (properties `((upstream-name . "memery")))
    (build-system r-build-system)
    (propagated-inputs (list r-sysfonts
                             r-showtext
                             r-shinycssloaders
                             r-shinybs
                             r-shiny
                             r-purrr
                             r-png
                             r-magrittr
                             r-jpeg
                             r-ggplot2
                             r-cowplot
                             r-colourpicker
                             r-cairo))
    (home-page "https://github.com/leonawicz/memery")
    (synopsis "Internet Memes for Data Analysts")
    (description
     "Generates internet memes that optionally include a superimposed inset plot and
other atypical features, combining the visual impact of an attention-grabbing
meme with graphic results of data analysis.  The package differs from related
packages that focus on imitating and reproducing standard memes.  Some packages
do this by interfacing with online meme generators whereas others achieve this
natively.  This package takes the latter approach.  It does not interface with
online meme generators or require any authentication with external websites.  It
reads images directly from local files or via URL and meme generation is done by
the package.  While this is similar to the meme package available on CRAN, it
differs in that the focus is on allowing for non-standard meme layouts and
hybrids of memes mixed with graphs.  While this package can be used to make
basic memes like an online meme generator would produce, it caters primarily to
hybrid graph-meme plots where the meme presentation can be seen as a backdrop
highlighting foreground graphs of data analysis results.  The package also
provides support for an arbitrary number of meme text labels with arbitrary
size, position and other attributes rather than restricting to the standard top
and/or bottom text placement.  This is useful for proper aesthetic interleaving
of plots of data between meme image backgrounds and overlain text labels.  The
package offers a selection of templates for graph placement and appearance with
respect to the underlying meme.  Graph templates also permit additional
template-specific customization.  Animated gif support is provided but this is
optional and functional only if the magick package is installed.  magick is not
required unless gif functionality is desired.")
    (license license:expat)))

(define-public r-meme
  (package
    (name "r-meme")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meme" version))
       (sha256
        (base32 "009gkl9zmj2s6wmd7rl0khf04m05zcbbjjds2w7krnbr8h0dqpqm"))))
    (properties `((upstream-name . "meme")))
    (build-system r-build-system)
    (propagated-inputs (list r-sysfonts r-showtext r-magick r-gridgraphics
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/GuangchuangYu/meme/")
    (synopsis "Create Meme")
    (description
     "The word Meme was originated from the book, The Selfish Gene', authored by
Richard Dawkins (1976).  It is a unit of culture that is passed from one
generation to another and correlates to the gene, the unit of physical heredity.
 The internet memes are captioned photos that are intended to be funny,
ridiculous.  Memes behave like infectious viruses and travel from person to
person quickly through social media.  The meme package allows users to make
custom memes.")
    (license license:artistic2.0)))

(define-public r-memapp
  (package
    (name "r-memapp")
    (version "2.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "memapp" version))
       (sha256
        (base32 "0gjphzq2lr2qn7adjm6vvm50wqg6fhv0p28mcw2yjwiq8yrr0f3b"))))
    (properties `((upstream-name . "memapp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-stringi
                             r-shinywidgets
                             r-shinyjs
                             r-shinydashboard
                             r-shinybs
                             r-shiny
                             r-rcolorbrewer
                             r-plotly
                             r-mem
                             r-ggplot2
                             r-formattable
                             r-dt
                             r-dplyr))
    (home-page "https://github.com/lozalojo/memapp")
    (synopsis "The Moving Epidemic Method Web Application")
    (description
     "The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015)
<doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the
weekly assessment of the epidemic and intensity status to help in routine
respiratory infections surveillance in health systems.  Allows the comparison of
different epidemic indicators, timing and shape with past epidemics and across
different regions or countries with different surveillance systems.  Also, it
gives a measure of the performance of the method in terms of sensitivity and
specificity of the alert week.  memapp is a web application created in the Shiny
framework for the mem R package.")
    (license license:gpl2+)))

(define-public r-mem
  (package
    (name "r-mem")
    (version "2.18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mem" version))
       (sha256
        (base32 "1jz2zadwm9gvlgfr1dvi8m6p3zm3bjz4n4apkbpj8g594ghcbrpx"))))
    (properties `((upstream-name . "mem")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-sm
                             r-rcpproll
                             r-rcolorbrewer
                             r-purrr
                             r-mclust
                             r-ggplot2
                             r-envstats
                             r-dplyr
                             r-boot))
    (home-page "https://github.com/lozalojo/mem")
    (synopsis "The Moving Epidemic Method")
    (description
     "The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015)
<doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the
weekly assessment of the epidemic and intensity status to help in routine
respiratory infections surveillance in health systems.  Allows the comparison of
different epidemic indicators, timing and shape with past epidemics and across
different regions or countries with different surveillance systems.  Also, it
gives a measure of the performance of the method in terms of sensitivity and
specificity of the alert week.")
    (license license:gpl2+)))

(define-public r-meltt
  (package
    (name "r-meltt")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meltt" version))
       (sha256
        (base32 "1j2iz05xr9ddj9pk2yw7j7kjfr9rxmxkk123c6336fqdm14wvd21"))))
    (properties `((upstream-name . "meltt")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-shinyjs
                             r-shiny
                             r-reticulate
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-leaflet
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=meltt")
    (synopsis "Matching Event Data by Location, Time and Type")
    (description
     "Framework for merging and disambiguating event data based on spatiotemporal
co-occurrence and secondary event characteristics.  It can account for intrinsic
\"fuzziness\" in the coding of events, varying event taxonomies and different
geo-precision codes.")
    (license license:lgpl3)))

(define-public r-meltr
  (package
    (name "r-meltr")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meltr" version))
       (sha256
        (base32 "1h2a09n0az9s81m9n4a0h3dckxxgb9vpj03kal29ms0zwn525xpx"))))
    (properties `((upstream-name . "meltr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-r6 r-cpp11 r-cli))
    (home-page "https://r-lib.github.io/meltr/")
    (synopsis "Read Non-Rectangular Text Data")
    (description
     "The goal of meltr is to provide a fast and friendly way to read non-rectangular
data, such as ragged forms of csv (comma-separated values), tsv (tab-separated
values), and fwf (fixed-width format) files.")
    (license license:expat)))

(define-public r-melt
  (package
    (name "r-melt")
    (version "1.11.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "melt" version))
       (sha256
        (base32 "19g5cd8fnvvqlvrvim47ra53svjk07cr4dmgn3h6x13wkv0grg46"))))
    (properties `((upstream-name . "melt")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-dqrng r-checkmate r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/melt/")
    (synopsis "Multiple Empirical Likelihood Tests")
    (description
     "This package performs multiple empirical likelihood tests.  It offers an
easy-to-use interface and flexibility in specifying hypotheses and calibration
methods, extending the framework to simultaneous inferences.  The core
computational routines are implemented using the Eigen C++ library and
@code{RcppEigen} interface, with @code{OpenMP} for parallel computation.
Details of the testing procedures are provided in Kim, @code{MacEachern}, and
Peruggia (2023) <doi:10.1080/10485252.2023.2206919>.  A companion paper by Kim,
@code{MacEachern}, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is available
for further information.  This work was supported by the U.S. National Science
Foundation under Grants No.  SES-1921523 and DMS-2015552.")
    (license license:gpl2+)))

(define-public r-mekko
  (package
    (name "r-mekko")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mekko" version))
       (sha256
        (base32 "09y50k4r4mnx351a5zhkqg9s2xyl3d149dhmfy2z6hzm5yg2ajyi"))))
    (properties `((upstream-name . "mekko")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mekko")
    (synopsis "Variable Width Bar Charts: Bar Mekko")
    (description
     "Create variable width bar charts i.e. \"bar mekko\" charts to include important
quantitative context.  Closely related to mosaic, spine (or spinogram), matrix,
submarine, olympic, Mondrian or product plots and tree maps.")
    (license license:gpl3)))

(define-public r-meifly
  (package
    (name "r-meifly")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meifly" version))
       (sha256
        (base32 "0pgmxa4g16zhx3jx5g56h2s1f4cr4bkz3bp7404dm2729083rvjx"))))
    (properties `((upstream-name . "meifly")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-mass r-leaps))
    (home-page "https://github.com/hadley/meifly")
    (synopsis "Interactive Model Exploration using 'GGobi'")
    (description
     "Exploratory model analysis with <http://ggobi.org>.  Fit and graphical explore
ensembles of linear models.")
    (license license:expat)))

(define-public r-megena
  (package
    (name "r-megena")
    (version "1.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MEGENA" version))
       (sha256
        (base32 "0cr10pmj1n831scz5kgaq3nkglr3imwgc62cxng2dvdxr9991g21"))))
    (properties `((upstream-name . "MEGENA")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape
                             r-rcpp
                             r-matrix
                             r-igraph
                             r-ggrepel
                             r-ggraph
                             r-ggplot2
                             r-fpc
                             r-foreach
                             r-doparallel
                             r-cluster
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/songw01/MEGENA")
    (synopsis "Multiscale Clustering of Geometrical Network")
    (description
     "Co-Expression Network Analysis by adopting network embedding technique.  Song
W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis.
P@code{LoS} Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.")
    (license license:gpl3+)))

(define-public r-mega2r
  (package
    (name "r-mega2r")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mega2R" version))
       (sha256
        (base32 "05g0r7z6kiy0pgl7cbcc3c0wbf4wbc7fxdbha8sc77m3hqya882l"))))
    (properties `((upstream-name . "Mega2R")))
    (build-system r-build-system)
    (propagated-inputs (list r-skat
                             r-rsqlite
                             r-rcpp
                             r-pedgene
                             r-kinship2
                             r-genomeinfodb
                             r-gdsfmt
                             r-famskatrc
                             r-dbi
                             r-annotationdbi))
    (native-inputs (list r-knitr))
    (home-page "https://watson.hgen.pitt.edu/mega2/mega2r/")
    (synopsis "Accessing and Processing a 'Mega2' Genetic Database")
    (description
     "Uses as input genetic data that have been reformatted and stored in a SQLite
database; this database is initially created by the standalone mega2 C++ program
(available freely from <https://watson.hgen.pitt.edu/register/>).  Loads and
manipulates data frames containing genotype, phenotype, and family information
from the input SQLite database, and decompresses needed subsets of the genotype
data, on the fly, in a memory efficient manner.  We have also created several
more functions that illustrate how to use the data frames as well as perform
useful tasks: these permit one to run the pedgene package to carry out
gene-based association tests on family data using selected marker subsets, to
run the SKAT package to carry out gene-based association tests using selected
marker subsets, to run the @code{famSKATRC} package to carry out gene-based
association tests on families (optionally) and with rare or common variants
using selected marker subsets, to output the Mega2R data as a VCF file and
related files (for phenotype and family data), and to convert the data frames
into @code{CoreArray} Genomic Data Structure (GDS) format.")
    (license license:gpl2)))

(define-public r-mefm
  (package
    (name "r-mefm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MEFM" version))
       (sha256
        (base32 "0gp2z9wwvpwm44lan13fr7j73zkn31clphkizwj8r40yynnavygy"))))
    (properties `((upstream-name . "MEFM")))
    (build-system r-build-system)
    (propagated-inputs (list r-tensormiss))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MEFM")
    (synopsis "Perform MEFM Estimation on Matrix Time Series")
    (description
     "To perform main effect matrix factor model (MEFM) estimation for a given matrix
time series as described in Lam and Cen (2024)
<doi:10.48550/@code{arXiv.2406.00128>}.  Estimation of traditional matrix factor
models is also supported.  Supplementary functions for testing MEFM over factor
models are included.")
    (license license:gpl3)))

(define-public r-mefdind
  (package
    (name "r-mefdind")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mefdind" version))
       (sha256
        (base32 "1w52c5bq0jy38d8lkf9dglw3nr3c8dqvhdk4mmhc3q8r5x3a8cz0"))))
    (properties `((upstream-name . "mefdind")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rvest))
    (home-page "https://eldafani.github.io/mefdind/")
    (synopsis "Imports Data from MoE Spain")
    (description
     "Imports indicator data provided by the Ministry of Education (@code{MoE),Spain}.
 The data is stored at
<https://www.educacionyfp.gob.es/servicios-al-ciudadano/estadisticas/no-universitaria.html>
Includes functions for reading, downloading, and selecting data for main series.
 This package is not sponsored or supported by the @code{MoE} Spain.  Importa
datos con indicadores del Ministerio de EducaciÃ³n y FormaciÃ³n Profesional
(MEFD) de EspaÃ±Ã¡.  Los datos estÃ¡n en
<https://www.educacionyfp.gob.es/servicios-al-ciudadano/estadisticas/no-universitaria.html>
Contiene funciones para leer, descargar, y seleccionar bases de datos de series
principales.  Este paquete no es patrocinado o respaldado por el MEFD.")
    (license license:gpl2)))

(define-public r-mefa4
  (package
    (name "r-mefa4")
    (version "0.3-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mefa4" version))
       (sha256
        (base32 "02npawccc316vwgmk0yna7wvlxzsbdishxzmfd4wgl7g218wjk3m"))))
    (properties `((upstream-name . "mefa4")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://github.com/psolymos/mefa4")
    (synopsis "Multivariate Data Handling with S4 Classes and Sparse Matrices")
    (description
     "An S4 update of the mefa package using sparse matrices for enhanced efficiency.
Sparse array-like objects are supported via lists of sparse matrices.")
    (license license:gpl2)))

(define-public r-mefa
  (package
    (name "r-mefa")
    (version "3.2-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mefa" version))
       (sha256
        (base32 "1rllkxp218qsa1c0aaa5zdky5bh2rzqbhdc2n14bmvv4jlgzrl8c"))))
    (properties `((upstream-name . "mefa")))
    (build-system r-build-system)
    (home-page "https://github.com/psolymos/mefa")
    (synopsis "Multivariate Data Handling in Ecology and Biogeography")
    (description
     "This package provides a framework package aimed to provide standardized
computational environment for specialist work via object classes to represent
the data coded by samples, taxa and segments (i.e.  subpopulations, repeated
measures).  It supports easy processing of the data along with cross tabulation
and relational data tables for samples and taxa.  An object of class `mefa is a
project specific compendium of the data and can be easily used in further
analyses.  Methods are provided for extraction, aggregation, conversion,
plotting, summary and reporting of `mefa objects.  Reports can be generated in
plain text or @code{LaTeX} format.  Vignette contains worked examples.")
    (license license:gpl2)))

(define-public r-meetupapi
  (package
    (name "r-meetupapi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meetupapi" version))
       (sha256
        (base32 "1iaxpfzgn478np9cjwz0zalfy1p7jwabnwpfv31svw052i8bs9yl"))))
    (properties `((upstream-name . "meetupapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-magrittr r-httr r-dplyr))
    (home-page "https://github.com/zacdav/meetupapi")
    (synopsis "Access 'Meetup' API")
    (description
     "Allows management of Meetup groups via the <https:www.meetup.com/meetup_api/>.
Provided are a set of functions that enable fetching information of joined
meetups, attendance, and members.  This package requires the use of an API key.")
    (license license:gpl3)))

(define-public r-meerva
  (package
    (name "r-meerva")
    (version "0.2-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meerva" version))
       (sha256
        (base32 "1q1q6ydn1zwv8vif14qs2zgijhrdrnpx1ibd5afp2lb6yn8qd61i"))))
    (properties `((upstream-name . "meerva")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survival
                             r-mvtnorm
                             r-matrixcalc
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=meerva")
    (synopsis
     "Analysis of Data with Measurement Error Using a Validation Subsample")
    (description
     "Sometimes data for analysis are obtained using more convenient or less expensive
means yielding \"surrogate\" variables for what could be obtained more accurately,
albeit with less convenience; or less conveniently or at more expense yielding
\"reference\" variables, thought of as being measured without error.  Analysis of
the surrogate variables measured with error generally yields biased estimates
when the objective is to make inference about the reference variables.  Often it
is thought that ignoring the measurement error in surrogate variables only
biases effects toward the null hypothesis, but this need not be the case.
Measurement errors may bias parameter estimates either toward or away from the
null hypothesis.  If one has a data set with surrogate variable data from the
full sample, and also reference variable data from a randomly selected
subsample, then one can assess the bias introduced by measurement error in
parameter estimation, and use this information to derive improved estimates
based upon all available data.  Formulaically these estimates based upon the
reference variables from the validation subsample combined with the surrogate
variables from the whole sample can be interpreted as starting with the estimate
from reference variables in the validation subsample, and \"augmenting\" this with
additional information from the surrogate variables.  This suggests the term
\"augmented\" estimate.  The meerva package calculates these augmented estimates
in the regression setting when there is a randomly selected subsample with both
surrogate and reference variables.  Measurement errors may be differential or
non-differential, in any or all predictors (simultaneously) as well as outcome.
The augmented estimates derive, in part, from the multivariate correlation
between regression model parameter estimates from the reference variables and
the surrogate variables, both from the validation subset.  Because the
validation subsample is chosen at random any biases imposed by measurement
error, whether non-differential or differential, are reflected in this
correlation and these correlations can be used to derive estimates for the
reference variables using data from the whole sample.  The main functions in the
package are meerva.fit which calculates estimates for a dataset, and
meerva.sim.block which simulates multiple datasets as described by the user, and
analyzes these datasets, storing the regression coefficient estimates for
inspection.  The augmented estimates, as well as how measurement error may arise
in practice, is described in more detail by Kremers WK (2021)
<@code{arXiv:2106.14063>} and is an extension of the works by Chen Y-H, Chen H.
(2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002)
<doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015)
<doi:10.1016/j.jmva.2015.05.017> and Tong J, Huang J, Chubak J, et al. (2020)
<doi:10.1093/jamia/ocz180>.")
    (license license:gpl3)))

(define-public r-medseq
  (package
    (name "r-medseq")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MEDseq" version))
       (sha256
        (base32 "1w4jgy3fxwmk87r8kc4a6rx20salsk06qxxgx2jd3d22rvrj8crd"))))
    (properties `((upstream-name . "MEDseq")))
    (build-system r-build-system)
    (propagated-inputs (list r-weightedcluster
                             r-traminer
                             r-stringdist
                             r-seriation
                             r-nnet
                             r-matrixstats
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MEDseq")
    (synopsis "Mixtures of Exponential-Distance Models with Covariates")
    (description
     "This package implements a model-based clustering method for categorical
life-course sequences relying on mixtures of exponential-distance models
introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>.  A range of
flexible precision parameter settings corresponding to weighted generalisations
of the Hamming distance metric are considered, along with the potential
inclusion of a noise component.  Gating covariates can be supplied in order to
relate sequences to baseline characteristics and sampling weights are also
accommodated.  The models are fitted using the EM algorithm and tools for
visualising the results are also provided.")
    (license license:gpl3+)))

(define-public r-medscan
  (package
    (name "r-medscan")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medScan" version))
       (sha256
        (base32 "17h97fnklci6prj62nzg49dcczns7s1yzi410jrdypsfhpz912kv"))))
    (properties `((upstream-name . "medScan")))
    (build-system r-build-system)
    (propagated-inputs (list r-qvalue r-qqman r-locfdr r-hdmt r-fdrtool))
    (home-page "https://github.com/umich-cphds/medScan")
    (synopsis "Large Scale Single Mediator Hypothesis Testing")
    (description
     "This package provides a collection of methods for large scale single mediator
hypothesis testing.  The six included methods for testing the mediation effect
are Sobel's test, Max P test, joint significance test under the composite null
hypothesis, high dimensional mediation testing, divide-aggregate composite null
test, and Sobel's test under the composite null hypothesis.  Du et al (2023)
<doi:10.1002/gepi.22510>.")
    (license license:gpl3)))

(define-public r-medrxivr
  (package
    (name "r-medrxivr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medrxivr" version))
       (sha256
        (base32 "03p5rz6a362wvlcaq2khzyhjivvw62i50846db8p6rj9rwqba228"))))
    (properties `((upstream-name . "medrxivr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-progress
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-data-table
                             r-curl
                             r-bib2df))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/medrxivr")
    (synopsis "Access and Search MedRxiv and BioRxiv Preprint Data")
    (description
     "An increasingly important source of health-related bibliographic content are
preprints - preliminary versions of research articles that have yet to undergo
peer review.  The two preprint repositories most relevant to health-related
sciences are @code{medRxiv} <https://www.medrxiv.org/> and @code{bioRxiv}
<https://www.biorxiv.org/>, both of which are operated by the Cold Spring Harbor
Laboratory.  medrxivr provides programmatic access to the Cold Spring Harbour
Laboratory (CSHL) API <https://api.biorxiv.org/>, allowing users to easily
download @code{medRxiv} and @code{bioRxiv} preprint metadata (e.g. title,
abstract, publication date, author list, etc) into R. medrxivr also provides
functions to search the downloaded preprint records using regular expressions
and Boolean logic, as well as helper functions that allow users to export their
search results to a .BIB file for easy import to a reference manager and to
download the full-text PDFs of preprints matching their search criteria.")
    (license license:gpl2)))

(define-public r-medparser
  (package
    (name "r-medparser")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medparser" version))
       (sha256
        (base32 "1xgylvgjcaxw49sy65chwr56iinsmfm0bj32mw1f4ssc6l0bm2ab"))))
    (properties `((upstream-name . "medparser")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=medparser")
    (synopsis "MedPC Text Parser")
    (description
     "Parses information from text files with specific utility aimed at pulling
information from Med Associate's (MPC) files.  These functions allow for further
analysis of MPC files.")
    (license license:expat)))

(define-public r-medlea
  (package
    (name "r-medlea")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MedLEA" version))
       (sha256
        (base32 "1jnrav6hhb97v5ncrspkq7ph2xqzc7sn14awlsnmbfzpv3543m4y"))))
    (properties `((upstream-name . "MedLEA")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MedLEA")
    (synopsis "Morphological and Structural Features of Medicinal Leaves")
    (description
     "This package contains a dataset of morphological and structural features of
Medicinal LEAves (@code{MedLEA})'.  The features of each species is recorded by
manually viewing the medicinal plant repository available at
(<http://www.instituteofayurveda.org/plants/>).  You can also download
repository of leaf images of 1099 medicinal plants in Sri Lanka.")
    (license license:gpl3)))

(define-public r-meditations
  (package
    (name "r-meditations")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meditations" version))
       (sha256
        (base32 "0wnx3zadpngfch0sb0ifqvsmzl613639f1yazwsgvhk29mycx4ss"))))
    (properties `((upstream-name . "meditations")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jacobkap/meditations")
    (synopsis "Prints a Random Quote from Marcus Aurelius' Book Meditations")
    (description
     "Prints a random quote from Marcus Aurelius book Meditations.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-medicare
  (package
    (name "r-medicare")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medicare" version))
       (sha256
        (base32 "00a1gml2khzisdavnaip6ap4bw45b17nbl9cqb1mqrm4y0p9gm16"))))
    (properties `((upstream-name . "medicare")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "http://www.github.com/robertgambrel/medicare")
    (synopsis "Tools for Obtaining and Cleaning Medicare Public Use Files")
    (description
     "Publicly available data from Medicare frequently requires extensive initial
effort to extract desired variables and merge them; this package formalizes the
techniques I've found work best.  More information on the Medicare program, as
well as guidance for the publicly available data this package targets, can be
found on CMS's website covering publicly available data.  See
<https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.")
    (license license:expat)))

(define-public r-medicalrisk
  (package
    (name "r-medicalrisk")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medicalrisk" version))
       (sha256
        (base32 "12p3mjy6lphjajxz7is04cahp0598ip9ixfkdimvw1k8n5hs9mj0"))))
    (properties `((upstream-name . "medicalrisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-plyr r-hash))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/patrickmdnet/medicalrisk")
    (synopsis "Medical Risk and Comorbidity Tools for ICD-9-CM Data")
    (description
     "Generates risk estimates and comorbidity flags from ICD-9-CM codes available in
administrative medical datasets.  The package supports the Charlson Comorbidity
Index, the Elixhauser Comorbidity classification, the Revised Cardiac Risk
Index, and the Risk Stratification Index.  Methods are table-based, fast, and
use the plyr package, so parallelization is possible for large jobs.  Also
includes a sample of real ICD-9 data for 100 patients from a publicly available
dataset.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-medicaldata
  (package
    (name "r-medicaldata")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medicaldata" version))
       (sha256
        (base32 "0dljqg24mkn7bjbbz5dp0z8yb5sl3i4a89rl32grlvwf0z3b1njn"))))
    (properties `((upstream-name . "medicaldata")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://higgi13425.github.io/medicaldata/")
    (synopsis "Data Package for Medical Datasets")
    (description
     "This package provides access to well-documented medical datasets for teaching.
Featuring several from the Teaching of Statistics in the Health Sciences website
<https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets
of historical significance in medical research, some reformatted and extended
from existing R packages, and some data donations.")
    (license license:expat)))

(define-public r-mediationsens
  (package
    (name "r-mediationsens")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mediationsens" version))
       (sha256
        (base32 "1f7v98h8n34sw5vkvps2z770kmc1iaqyd1xjmd8nia7636k91f43"))))
    (properties `((upstream-name . "mediationsens")))
    (build-system r-build-system)
    (propagated-inputs (list r-mediation r-distr))
    (home-page "https://cran.r-project.org/package=mediationsens")
    (synopsis
     "Simulation-Based Sensitivity Analysis for Causal Mediation Studies")
    (description
     "Simulation-based sensitivity analysis for causal mediation studies.  It
numerically and graphically evaluates the sensitivity of causal mediation
analysis results to the presence of unmeasured pretreatment confounding.  The
proposed method has primary advantages over existing methods.  First, using an
unmeasured pretreatment confounder conditional associations with the treatment,
mediator, and outcome as sensitivity parameters, the method enables users to
intuitively assess sensitivity in reference to prior knowledge about the
strength of a potential unmeasured pretreatment confounder.  Second, the method
accurately reflects the influence of unmeasured pretreatment confounding on the
efficiency of estimation of the causal effects.  Third, the method can be
implemented in different causal mediation analysis approaches, including
regression-based, simulation-based, and propensity score-based methods.  It is
applicable to both randomized experiments and observational studies.")
    (license license:gpl2)))

(define-public r-mediation
  (package
    (name "r-mediation")
    (version "4.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mediation" version))
       (sha256
        (base32 "0i88wvqqw8cmxmh9i78rqzp4s16zhf12dgl9fsk9bhw7hxhhc0i1"))))
    (properties `((upstream-name . "mediation")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-lpsolve
                             r-lme4
                             r-hmisc
                             r-boot))
    (home-page "https://imai.princeton.edu/projects/mechanisms.html")
    (synopsis "Causal Mediation Analysis")
    (description
     "We implement parametric and non parametric mediation analysis.  This package
performs the methods and suggestions in Imai, Keele and Yamamoto (2010)
<DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>,
Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai
and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013)
<http://web.mit.edu/teppei/www/research/IVmediate.pdf>.  In addition to the
estimation of causal mediation effects, the software also allows researchers to
conduct sensitivity analysis for certain parametric models.")
    (license license:gpl2+)))

(define-public r-mediatep
  (package
    (name "r-mediatep")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mediateP" version))
       (sha256
        (base32 "0qmams2hdvhq7abfidblgdz44nh5ja3byn233q3k61hvz7asxmc7"))))
    (properties `((upstream-name . "mediateP")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (home-page "https://cran.r-project.org/package=mediateP")
    (synopsis "Mediation Analysis Based on the Product Method")
    (description
     "This package provides functions for calculating the point and interval estimates
of the natural indirect effect (NIE), total effect (TE), and mediation
proportion (MP), based on the product approach.  We perform the methods
considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect
effect and the mediation proportion via the product method.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-medianews
  (package
    (name "r-medianews")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MediaNews" version))
       (sha256
        (base32 "0dr2zip9p0hgnf1bhgz8dd1kk48m60z9nyw2s84l0321ivpdl5i9"))))
    (properties `((upstream-name . "MediaNews")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-stopwords r-rvest r-lubridate))
    (home-page "https://cran.r-project.org/package=MediaNews")
    (synopsis "Media News Extraction for Text Analysis")
    (description
     "Extract textual data from different media channels through its source based on
users choice of keywords.  These data can be used to perform text analysis to
identify patterns in respective media reporting.  The media channels used in
this package are print media.  The data (or news) used are publicly available to
consumers.")
    (license license:lgpl3)))

(define-public r-medianadesigner
  (package
    (name "r-medianadesigner")
    (version "0.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MedianaDesigner" version))
       (sha256
        (base32 "07vzfm583gqk0ars4gamyn21lpcsdxx86nkp227i9yk7lyj0r6kp"))))
    (properties `((upstream-name . "MedianaDesigner")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-shinymatrix
                             r-shinydashboard
                             r-shiny
                             r-rootsolve
                             r-rcppnumerical
                             r-rcppeigen
                             r-rcpp
                             r-pbkrtest
                             r-officer
                             r-mvtnorm
                             r-mass
                             r-lmertest
                             r-lme4
                             r-foreach
                             r-flextable
                             r-doparallel
                             r-devemf))
    (home-page "https://github.com/medianasoft/MedianaDesigner")
    (synopsis "Power and Sample Size Calculations for Clinical Trials")
    (description
     "Efficient simulation-based power and sample size calculations are supported for
a broad class of late-stage clinical trials.  The following modules are included
in the package: Adaptive designs with data-driven sample size or event count
re-estimation, Adaptive designs with data-driven treatment selection, Adaptive
designs with data-driven population selection, Optimal selection of a futility
stopping rule, Event prediction in event-driven trials, Adaptive trials with
response-adaptive randomization (experimental module), Traditional trials with
multiple objectives (experimental module).  Traditional trials with
cluster-randomized designs (experimental module).")
    (license license:gpl3)))

(define-public r-mediana
  (package
    (name "r-mediana")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mediana" version))
       (sha256
        (base32 "0j1ikc2iaxl70134v0gijj2i2g0xgjafvn8rc108r9fhm8rpw564"))))
    (properties `((upstream-name . "Mediana")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-mvtnorm
                             r-mass
                             r-foreach
                             r-dorng
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "http://gpaux.github.io/Mediana/")
    (synopsis "Clinical Trial Simulations")
    (description
     "This package provides a general framework for clinical trial simulations based
on the Clinical Scenario Evaluation (CSE) approach.  The package supports a
broad class of data models (including clinical trials with continuous, binary,
survival-type and count-type endpoints as well as multivariate outcomes that are
based on combinations of different endpoints), analysis strategies and commonly
used evaluation criteria.")
    (license license:gpl2)))

(define-public r-mediak
  (package
    (name "r-mediak")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MediaK" version))
       (sha256
        (base32 "19cmxl2wksw9kvjsfn1m4nkr5gpcx6bk0sqrabj1n0dla1l32v2a"))))
    (properties `((upstream-name . "MediaK")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=MediaK")
    (synopsis "Calculate MeDiA_K Distance")
    (description
     "Calculates @code{MeDiA_K} (means Mean Distance Association by K-nearest
neighbor) in order to detect nonlinear associations.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mediacloudr
  (package
    (name "r-mediacloudr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mediacloudr" version))
       (sha256
        (base32 "1r11mpz44msla71lqw45r9s8z7rwvbnqi026l0ljx6crby183h0w"))))
    (properties `((upstream-name . "mediacloudr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-rvest r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mediacloudr")
    (synopsis "Wrapper for the 'mediacloud.org' API")
    (description
     "API wrapper to gather news stories, media information and tags from the
mediacloud.org API, based on a multilevel query <https://mediacloud.org/>.  A
personal API key is required.")
    (license license:expat)))

(define-public r-medflex
  (package
    (name "r-medflex")
    (version "0.6-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medflex" version))
       (sha256
        (base32 "0cybshbk2qvh38n9sc6qf9jwjhzs4zz31awx3bbyqfwzjgzai2dx"))))
    (properties `((upstream-name . "medflex")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-multcomp r-matrix r-car r-boot))
    (home-page "https://github.com/jmpsteen/medflex")
    (synopsis "Flexible Mediation Analysis Using Natural Effect Models")
    (description
     "Run flexible mediation analyses using natural effect models as described in
Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, Vansteelandt,
Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> and Loeys, Moerkerke, De
Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.")
    (license license:gpl2)))

(define-public r-medfateland
  (package
    (name "r-medfateland")
    (version "2.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medfateland" version))
       (sha256
        (base32 "1mq70p0j3a2vff4ybl8lgqflp4fbl4clg4y266hm6d0wnali6a1r"))))
    (properties `((upstream-name . "medfateland")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyterra
                             r-tidyr
                             r-tibble
                             r-terra
                             r-stars
                             r-shiny
                             r-sf
                             r-rlang
                             r-rcpp
                             r-meteoland
                             r-medfate
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (home-page "https://emf-creaf.github.io/medfateland/")
    (synopsis "Mediterranean Landscape Simulation")
    (description
     "Simulate forest hydrology, forest function and dynamics over landscapes [De
Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>].  Parallelization
is allowed in several simulation functions and simulations may be conducted
including spatial processes such as lateral water transfer and seed dispersal.")
    (license license:gpl2+)))

(define-public r-medfate
  (package
    (name "r-medfate")
    (version "4.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medfate" version))
       (sha256
        (base32 "0fxzvqvg879ps59b7469cp4mhjs5hj7d55hl4q80p2zv31dc84k3"))))
    (properties `((upstream-name . "medfate")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rcpp r-meteoland r-ggplot2))
    (home-page "https://emf-creaf.github.io/medfate/")
    (synopsis "Mediterranean Forest Simulation")
    (description
     "Simulate Mediterranean forest functioning and dynamics using cohort-based
description of vegetation [De Caceres et al. (2015)
<doi:10.1016/j.agrformet.2015.06.012>; De Caceres et al. (2021)
<doi:10.1016/j.agrformet.2020.108233>].")
    (license (list license:gpl2+ license:lgpl3+))))

(define-public r-medextractr
  (package
    (name "r-medextractr")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "medExtractR" version))
       (sha256
        (base32 "0dn85yj61mz62bdxxvaxads6j9qf209sypmdvb9jx83c2sc7f4g6"))))
    (properties `((upstream-name . "medExtractR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-stringi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=medExtractR")
    (synopsis "Extraction of Medication Information from Clinical Text")
    (description
     "Function and support for medication and dosing information extraction from
free-text clinical notes.  Medication entities for the basic @code{medExtractR}
implementation that can be extracted include drug name, strength, dose amount,
dose, frequency, intake time, dose change, and time of last dose.  The basic
@code{medExtractR} is outlined in Weeks, Beck, @code{McNeer}, Williams, Bejan,
Denny, Choi (2020) <doi: 10.1093/jamia/ocz207>.  The extended
@code{medExtractR_tapering} implementation is intended to extract dosing
information for more tapering schedules, which are far more complex.  The
tapering extension allows for the extraction of additional entities including
dispense amount, refills, dose schedule, time keyword, transition, and
preposition.")
    (license license:gpl2+)))

(define-public r-mederrrank
  (package
    (name "r-mederrrank")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mederrRank" version))
       (sha256
        (base32 "01dv7dabp2mvn99szaf8l44gbsy7zrllcm55zy6rbd0g9989ilnl"))))
    (properties `((upstream-name . "mederrRank")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-bb))
    (home-page "https://cran.r-project.org/package=mederrRank")
    (synopsis
     "Bayesian Methods for Identifying the Most Harmful Medication Errors")
    (description
     "Two distinct but related statistical approaches to the problem of identifying
the combinations of medication error characteristics that are more likely to
result in harm are implemented in this package: 1) a Bayesian hierarchical model
with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes
model that estimates the ratio of the observed count of harm to the count that
would be expected if error characteristics and harm were independent.  In
addition, for the Bayesian hierarchical model, the package provides functions to
assess the sensitivity of results to different specifications of the random
effects distributions.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-meddietcalc
  (package
    (name "r-meddietcalc")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MedDietCalc" version))
       (sha256
        (base32 "11q1yn6l697qa33wqik9y9lji39r6pph109yxkkprg9p80c187xy"))))
    (properties `((upstream-name . "MedDietCalc")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MedDietCalc")
    (synopsis
     "Multi Calculator to Compute Scores of Adherence to Mediterranean Diet")
    (description
     "Multi Calculator of different scores to measure adherence to Mediterranean Diet,
to compute them in nutriepidemiological data.  Additionally, a sample dataset of
this kind of data is provided, and some other minor tools useful in
epidemiological studies.")
    (license license:gpl3)))

(define-public r-med
  (package
    (name "r-med")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MED" version))
       (sha256
        (base32 "0fy0qmxirkdz5kigy2r7l5czjzws0fhjxy13kv1kqh628ldrp109"))))
    (properties `((upstream-name . "MED")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MED")
    (synopsis "Mediation by Tilted Balancing")
    (description
     "Nonparametric estimation and inference for natural direct and indirect effects
by Chan, Imai, Yam and Zhang (2016) <@code{arXiv:1601.03501>}.")
    (license license:gpl2+)))

(define-public r-mecoturn
  (package
    (name "r-mecoturn")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mecoturn" version))
       (sha256
        (base32 "1qr7p50wplg3nwvaz595nxfrf5b1dc8l4y13jx65r8kvksqrl07g"))))
    (properties `((upstream-name . "mecoturn")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-microeco
                             r-magrittr
                             r-lmertest
                             r-gunifrac
                             r-glmmtmb
                             r-ggpubr
                             r-ggplot2
                             r-betareg))
    (home-page "https://github.com/ChiLiubio/mecoturn")
    (synopsis "Decipher Microbial Turnover along a Gradient")
    (description
     "Two pipelines are provided to study microbial turnover along a gradient,
including the beta diversity and microbial abundance change.  The betaturn class
consists of the steps of community dissimilarity matrix generation, matrix
conversion, differential test and visualization.  The workflow of taxaturn class
includes the taxonomic abundance calculation, abundance transformation,
abundance change summary, statistical analysis and visualization.  Multiple
statistical approaches can contribute to the analysis of microbial turnover.")
    (license license:gpl3)))

(define-public r-mecor
  (package
    (name "r-mecor")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mecor" version))
       (sha256
        (base32 "0jcqf6d7x16hs0mrnkawlkl9idd2g5zb6l6mqdavkdx43fhk38j4"))))
    (properties `((upstream-name . "mecor")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-lmertest r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/LindaNab/mecor")
    (synopsis
     "Measurement Error Correction in Linear Models with a Continuous Outcome")
    (description
     "Covariate measurement error correction is implemented by means of regression
calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006,
ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ &
Kipnis V (2001)
<doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum
likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009)
<doi:10.1002/sim.3713>.  Outcome measurement error correction is implemented by
means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and
efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI &
Freedman LS (2014) <doi:10.1002/sim.7011>.  Standard error estimation of the
corrected estimators is implemented by means of the Delta method by Rosner B,
Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and
Rosner B, Spiegelman D & Willett WC (1992)
<doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by
Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert
D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).")
    (license license:gpl3)))

(define-public r-meconetcomp
  (package
    (name "r-meconetcomp")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meconetcomp" version))
       (sha256
        (base32 "00k9n3sbr6216zld4prl0v6mig472mhwca4ah8hr2s8q24v6ridx"))))
    (properties `((upstream-name . "meconetcomp")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-r6
                             r-microeco
                             r-magrittr
                             r-igraph
                             r-ggpubr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/ChiLiubio/meconetcomp")
    (synopsis
     "Compare Microbial Networks of 'trans_network' Class of 'microeco' Package")
    (description
     "Compare microbial co-occurrence networks created from trans_network class of
microeco package <https://github.com/@code{ChiLiubio/microeco>}.  This package
is the extension of trans_network class of microeco package and especially
useful when different networks are constructed and analyzed simultaneously.")
    (license license:gpl3)))

(define-public r-meclustnet
  (package
    (name "r-meclustnet")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MEclustnet" version))
       (sha256
        (base32 "0rs58dafxngk2lkq14cy14ayjwgnz1gmi12jmi4z74sdj0b3h0gw"))))
    (properties `((upstream-name . "MEclustnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-nnet
                             r-mvtnorm
                             r-mass
                             r-latentnet
                             r-ellipse
                             r-e1071))
    (home-page "https://cran.r-project.org/package=MEclustnet")
    (synopsis
     "Fit the Mixture of Experts Latent Position Cluster Model to Network Data")
    (description
     "This package provides functions to facilitate model-based clustering of nodes in
a network in a mixture of experts setting, which incorporates covariate
information on the nodes in the modelling process.  Isobel Claire Gormley and
Thomas Brendan Murphy (2010) <doi:10.1016/j.stamet.2010.01.002>.")
    (license license:gpl2)))

(define-public r-meboot
  (package
    (name "r-meboot")
    (version "1.4-9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meboot" version))
       (sha256
        (base32 "0akap3dl6nsfvh2f6d96ij6dvbblyrdnbqqh23ay8ah0r51zx9fs"))))
    (properties `((upstream-name . "meboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tdigest r-nlme r-hdrcde r-dynlm))
    (home-page "https://cran.r-project.org/package=meboot")
    (synopsis "Maximum Entropy Bootstrap for Time Series")
    (description
     "Maximum entropy density based dependent data bootstrap.  An algorithm is
provided to create a population of time series (ensemble) without assuming
stationarity.  The reference paper (Vinod, H.D., 2004 <DOI:
10.1016/j.jempfin.2003.06.002>) explains how the algorithm satisfies the ergodic
theorem and the central limit theorem.")
    (license license:gpl2+)))

(define-public r-measuring
  (package
    (name "r-measuring")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "measuRing" version))
       (sha256
        (base32 "0yblqkv5444y75jkb6d0zs728i6vybm294h7b5622pwfjqlfhnh2"))))
    (properties `((upstream-name . "measuRing")))
    (build-system r-build-system)
    (propagated-inputs (list r-tiff r-png r-pastecs r-dplr))
    (home-page "https://cran.r-project.org/package=measuRing")
    (synopsis
     "Detection and Control of Tree-Ring Widths on Scanned Image Sections")
    (description
     "Identification of ring borders on scanned image sections from
dendrochronological samples.  Processing of image reflectances to produce gray
matrices and time series of smoothed gray values.  Luminance data is plotted on
segmented images for users to perform both: visual identification of ring
borders or control of automatic detection.  Routines to visually include/exclude
ring borders on the R graphical devices, or automatically detect ring borders
using a linear detection algorithm.  This algorithm detects ring borders
according to positive/negative extreme values in the smoothed time-series of
gray values.  Most of the in-package routines can be recursively implemented
using the @code{multiDetect}() function.")
    (license license:gpl3)))

(define-public r-measures
  (package
    (name "r-measures")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "measures" version))
       (sha256
        (base32 "0325hhncg9fhq28cfl1hk6rhcmzzcngsmnkmbn9ri3b4hwqlafal"))))
    (properties `((upstream-name . "measures")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=measures")
    (synopsis "Performance Measures for Statistical Learning")
    (description
     "This package provides the biggest amount of statistical measures in the whole R
world.  Includes measures of regression, (multiclass) classification and
multilabel classification.  The measures come mainly from the mlr package and
were programed by several mlr developers.")
    (license license:gpl3)))

(define-public r-measurementprotocol
  (package
    (name "r-measurementprotocol")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "measurementProtocol" version))
       (sha256
        (base32 "02k60psb6299p82bk7yv7isy41jq26yk4r8kmsjpfnx9dwm1kgk8"))))
    (properties `((upstream-name . "measurementProtocol")))
    (build-system r-build-system)
    (propagated-inputs (list r-rappdirs r-jsonlite r-httr r-cli r-assertthat))
    (home-page "https://code.markedmondson.me/measurementProtocol/")
    (synopsis "Send Data from R to the Measurement Protocol")
    (description
     "Send server-side tracking data from R. The Measurement Protocol version 2
<https://developers.google.com/analytics/devguides/collection/protocol/ga4>
allows sending HTTP tracking events from R code.")
    (license license:expat)))

(define-public r-measr
  (package
    (name "r-measr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "measr" version))
       (sha256
        (base32 "1w2b1g7zivilc4pip09yi5fyx052g835vax5ja6bl36725h8giwk"))))
    (properties `((upstream-name . "measr")))
    (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-psych
                             r-posterior
                             r-magrittr
                             r-loo
                             r-glue
                             r-fs
                             r-dplyr
                             r-dcm2
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://measr.info")
    (synopsis "Bayesian Psychometric Measurement Using 'Stan'")
    (description
     "Estimate diagnostic classification models (also called cognitive diagnostic
models) with Stan'.  Diagnostic classification models are confirmatory latent
class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0).
Automatically generate Stan code for the general loglinear cognitive diagnostic
diagnostic model proposed by Henson et al. (2009)
<doi:10.1007/s11336-008-9089-5> and other subtypes that introduce additional
model constraints.  Using the generated Stan code, estimate the model evaluate
the model's performance using model fit indices, information criteria, and
reliability metrics.")
    (license license:gpl3+)))

(define-public r-meantables
  (package
    (name "r-meantables")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meantables" version))
       (sha256
        (base32 "1lg1a9dccb5xhbnmmgwkncj9w3g1zndh0ddjmascxhvzp9ckdvhg"))))
    (properties `((upstream-name . "meantables")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr r-rlang r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=meantables")
    (synopsis "Make Quick Descriptive Tables for Continuous Variables")
    (description
     "Quickly make tables of descriptive statistics (i.e., counts, means, confidence
intervals) for continuous variables.  This package is designed to work in a
Tidyverse pipeline, and consideration has been given to get results from R to
Microsoft Word Â® with minimal pain.")
    (license license:expat)))

(define-public r-meanshiftr
  (package
    (name "r-meanshiftr")
    (version "0.56")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meanShiftR" version))
       (sha256
        (base32 "1bn75r5mf3pr1mr8384w175yabw75cynxlz2xh1gl2nar86k4m0x"))))
    (properties `((upstream-name . "meanShiftR")))
    (build-system r-build-system)
    (home-page
     "http://meanmean.me/meanshift/r/cran/2016/08/28/meanShiftR.html")
    (synopsis "Computationally Efficient Mean Shift Implementation")
    (description
     "This package performs mean shift classification using linear and k-d tree based
nearest neighbor implementations for the Gaussian, Epanechnikov, and biweight
product kernels.")
    (license license:gpl2+)))

(define-public r-meanr
  (package
    (name "r-meanr")
    (version "0.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "meanr" version))
       (sha256
        (base32 "0d8zi0c902dqs5457x2zhcp88gg8wf62wrfcnrn5h769j7zy3vk4"))))
    (properties `((upstream-name . "meanr")))
    (build-system r-build-system)
    (home-page "https://github.com/wrathematics/meanr")
    (synopsis "Sentiment Analysis Scorer")
    (description
     "Sentiment analysis is a popular technique in text mining that attempts to
determine the emotional state of some text.  We provide a new implementation of
a common method for computing sentiment, whereby words are scored as positive or
negative according to a dictionary lookup.  Then the sum of those scores is
returned for the document.  We use the Hu and Liu sentiment dictionary ('Hu and
Liu', 2004) <doi:10.1145/1014052.1014073> for determining sentiment.  The
scoring function is vectorized by document, and scores for multiple documents
are computed in parallel via @code{OpenMP}'.")
    (license (license:fsdg-compatible "BSD 2-clause License + file LICENSE"))))

(define-public r-mdw
  (package
    (name "r-mdw")
    (version "2024.8-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdw" version))
       (sha256
        (base32 "1qvz84qrsm904ia8vr0n07magkj1ydgiw1hgmncchmq8l1a2fw6f"))))
    (properties `((upstream-name . "mdw")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-kyotil))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=mdw")
    (synopsis "Maximum Diversity Weighting")
    (description
     "Dimension-reduction methods aim at defining a score that maximizes signal
diversity.  Three approaches, tree weight, maximum entropy weights, and maximum
variance weights are provided.  These methods are described in He and Fong
(2019) <DOI:10.1002/sim.8212>.")
    (license license:gpl2)))

(define-public r-mdss
  (package
    (name "r-mdss")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDSS" version))
       (sha256
        (base32 "1x1d1r8bddrga9v1zbf6iv9n6hrag1fchdr0gkh1vrkai0a25l4a"))))
    (properties `((upstream-name . "MDSS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MDSS")
    (synopsis "Modeling Human Dentin Serial Sectioning")
    (description
     "Modeling microstructures of human tooth dentin and horizontal serial-sectioning
of the dentin.  Corresponding age range of dentin serial sections, that is used
in stable isotope analyses, can be calculated by using this package.")
    (license license:gpl3+)))

(define-public r-mdsr
  (package
    (name "r-mdsr")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdsr" version))
       (sha256
        (base32 "171c79kl32qam0fm9hxikbfimfvccazmnlz08kg10dkv8l67b423"))))
    (properties `((upstream-name . "mdsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-webshot2
                             r-tibble
                             r-stringr
                             r-skimr
                             r-rmariadb
                             r-kableextra
                             r-htmlwidgets
                             r-ggplot2
                             r-fs
                             r-dplyr
                             r-downloader
                             r-dbplyr
                             r-dbi
                             r-babynames))
    (home-page "https://github.com/mdsr-book/mdsr")
    (synopsis "Complement to 'Modern Data Science with R'")
    (description
     "This package provides a complement to all editions of *Modern Data Science with
R* (ISBN: 978-0367191498, publisher URL:
<https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>).
 This package contains data and code to complete exercises and reproduce
examples from the text.  It also facilitates connections to the SQL database
server used in the book.  All editions of the book are supported by this
package.")
    (license license:cc0)))

(define-public r-mdspcashiny
  (package
    (name "r-mdspcashiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDSPCAShiny" version))
       (sha256
        (base32 "0qsjx4v9j5p5a7h1g0xk4j5ib1mdrjsy84bglnjdxr9rpqg2m7n5"))))
    (properties `((upstream-name . "MDSPCAShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rmarkdown r-psych r-mass r-dplyr))
    (home-page "https://cran.r-project.org/package=MDSPCAShiny")
    (synopsis
     "Interactive Document for Working with Multidimensional Scaling and Principal Component Analysis")
    (description
     "An interactive document on the topic of multidimensional scaling and principal
component analysis using rmarkdown and shiny packages.  Runtime examples are
provided in the package function as well as at
<https://kartikeyabolar.shinyapps.io/MDS_PCAShiny/>.")
    (license license:gpl2)))

(define-public r-mdsopt
  (package
    (name "r-mdsopt")
    (version "0.7-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdsOpt" version))
       (sha256
        (base32 "1przk2ganrvs2g15rzby4npy58iwlxv1lsqrzan6z65filncqifq"))))
    (properties `((upstream-name . "mdsOpt")))
    (build-system r-build-system)
    (propagated-inputs (list r-symbolicda
                             r-spdep
                             r-smacof
                             r-plotrix
                             r-clustersim
                             r-animation))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=mdsOpt")
    (synopsis
     "Searching for Optimal MDS Procedure for Metric and Interval-Valued Data")
    (description
     "Selecting the optimal multidimensional scaling (MDS) procedure for metric data
via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal).
Selecting the optimal multidimensional scaling (MDS) procedure for
interval-valued data via metric MDS (ratio, interval, mspline).Selecting the
optimal multidimensional scaling procedure for interval-valued data by varying
all combinations of normalization and optimization methods.Selecting the optimal
MDS procedure for statistical data referring to the evaluation of tourist
attractiveness of Lower Silesian counties. (Borg, I., Groenen, P.J.F., Mair, P.
(2013) <doi:10.1007/978-3-642-31848-1>, Walesiak, M. (2016)
<doi:10.15611/ekt.2016.2.01>, Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).")
    (license license:gpl2+)))

(define-public r-mdsmap
  (package
    (name "r-mdsmap")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDSMap" version))
       (sha256
        (base32 "0knrkfwgl763fwdb3ln3g5y3rarnpqdfgzxf4m045664m7gkipsg"))))
    (properties `((upstream-name . "MDSMap")))
    (build-system r-build-system)
    (propagated-inputs (list r-smacof r-rgl r-reshape r-princurve))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=MDSMap")
    (synopsis
     "High Density Genetic Linkage Mapping using Multidimensional Scaling")
    (description
     "Estimate genetic linkage maps for markers on a single chromosome (or in a single
linkage group) from pairwise recombination fractions or intermarker distances
using weighted metric multidimensional scaling.  The methods are suitable for
autotetraploid as well as diploid populations.  Options for assessing the fit to
a known map are also provided.  Methods are discussed in detail in Preedy and
Hackett (2016) <doi:10.1007/s00122-016-2761-8>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mdsgui
  (package
    (name "r-mdsgui")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDSGUI" version))
       (sha256
        (base32 "1mkwy7i9wcciggvn19i88gzjyykwmkd0cvlln17l9yhcp9q9cly8"))))
    (properties `((upstream-name . "MDSGUI")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-tkrplot
                             r-tcltk2
                             r-scatterplot3d
                             r-rpanel
                             r-rgl
                             r-rcolorbrewer
                             r-mass
                             r-boot))
    (home-page "https://cran.r-project.org/package=MDSGUI")
    (synopsis "GUI for interactive MDS in R")
    (description
     "This package provides a graphical user interface (GUI) for performing
Multidimensional Scaling applications and interactively analysing the results
all within the GUI environment.  The MDS-GUI provides means of performing
Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF,
Kruskal's Analysis and Sammon Mapping with animated optimisation.")
    (license license:gpl3+)))

(define-public r-mdscore
  (package
    (name "r-mdscore")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdscore" version))
       (sha256
        (base32 "10cl5r6kd9chdik5v0q91x40xpw2cjvvyi220z4bvngpb0989x8j"))))
    (properties `((upstream-name . "mdscore")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=mdscore")
    (synopsis "Improved Score Tests for Generalized Linear Models")
    (description
     "This package provides a set of functions to obtain modified score test for
generalized linear models.")
    (license license:gpl2+)))

(define-public r-mds
  (package
    (name "r-mds")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mds" version))
       (sha256
        (base32 "1r4cz49h1sp1kl1sjqapadhd49lpdnr48w9xbwgpfh2ghwxfcval"))))
    (properties `((upstream-name . "mds")))
    (build-system r-build-system)
    (propagated-inputs (list r-parsedate r-lubridate))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mds")
    (synopsis "Medical Devices Surveillance")
    (description
     "This package provides a set of core functions for handling medical device event
data in the context of post-market surveillance, pharmacovigilance, signal
detection and trending, and regulatory reporting.  Primary inputs are data on
events by device and data on exposures by device.  Outputs include: standardized
device-event and exposure datasets, defined analyses, and time series.")
    (license license:gpl3)))

(define-public r-mdptoolbox
  (package
    (name "r-mdptoolbox")
    (version "4.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDPtoolbox" version))
       (sha256
        (base32 "0aaw787fvcyhw6mbl42icgqfdzvfr618vg179984xqifafr40dyc"))))
    (properties `((upstream-name . "MDPtoolbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-linprog))
    (home-page "https://cran.r-project.org/package=MDPtoolbox")
    (synopsis "Markov Decision Processes Toolbox")
    (description
     "The Markov Decision Processes (MDP) toolbox proposes functions related to the
resolution of discrete-time Markov Decision Processes: finite horizon, value
iteration, policy iteration, linear programming algorithms with some variants
and also proposes some functions related to Reinforcement Learning.")
    (license license:bsd-3)))

(define-public r-mdplot
  (package
    (name "r-mdplot")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDplot" version))
       (sha256
        (base32 "1xijh18hrkb807nfkdr7g642aspn5psc8nw29q387jl5hly2w8s9"))))
    (properties `((upstream-name . "MDplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-mass r-gtools r-gplots))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/MDplot/MDplot")
    (synopsis "Visualising Molecular Dynamics Analyses")
    (description
     "This package provides automatization for plot generation succeeding common
molecular dynamics analyses.  This includes straightforward plots, such as RMSD
(Root-Mean-Square-Deviation) and RMSF (Root-Mean-Square-Fluctuation) but also
more sophisticated ones such as dihedral angle maps, hydrogen bonds, cluster bar
plots and DSSP (Definition of Secondary Structure of Proteins) analysis.
Currently able to load GROMOS, GROMACS and AMBER formats, respectively.")
    (license license:gpl3)))

(define-public r-mdpiexplorer
  (package
    (name "r-mdpiexplorer")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDPIexploreR" version))
       (sha256
        (base32 "1amndzpmm739cnhk66acpv50xmklc5pcxaqvfcsfhd2d7pxyvw8g"))))
    (properties `((upstream-name . "MDPIexploreR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-scales
                             r-rvest
                             r-magrittr
                             r-lubridate
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://pgomba.github.io/MDPI_explorer/")
    (synopsis "Web Scraping and Bibliometric Analysis of MDPI Journals")
    (description
     "This package provides comprehensive tools to scrape and analyze data from the
MDPI journals.  It allows users to extract metrics such as
submission-to-acceptance times, article types, and whether articles are part of
special issues.  The package can also visualize this information through plots.
Additionally, MDP@code{IexploreR} offers tools to explore patterns of
self-citations within articles and provides insights into guest-edited special
issues.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-mdpeer
  (package
    (name "r-mdpeer")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdpeer" version))
       (sha256
        (base32 "1vsqhah8h47s8k687fwa352549mdz16mwk1g7rcmhx75c2k85w2q"))))
    (properties `((upstream-name . "mdpeer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve
                             r-reshape2
                             r-psych
                             r-nloptr
                             r-nlme
                             r-magic
                             r-glmnet
                             r-ggplot2
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mdpeer")
    (synopsis
     "Graph-Constrained Regression with Enhanced Regularization Parameters Selection")
    (description
     "This package provides graph-constrained regression methods in which
regularization parameters are selected automatically via estimation of
equivalent Linear Mixed Model formulation. @code{riPEER} (ridgified Partially
Empirical Eigenvectors for Regression) method employs a penalty term being a
linear combination of graph-originated and ridge-originated penalty terms, whose
two regularization parameters are ML estimators from corresponding Linear Mixed
Model solution; a graph-originated penalty term allows imposing similarity
between coefficients based on graph information given whereas additional
ridge-originated penalty term facilitates parameters estimation: it reduces
computational issues arising from singularity in a graph-originated penalty
matrix and yields plausible results in situations when graph information is not
informative. @code{riPEERc} (ridgified Partially Empirical Eigenvectors for
Regression with constant) method utilizes addition of a diagonal matrix
multiplied by a predefined (small) scalar to handle the non-invertibility of a
graph Laplacian matrix. @code{vrPEER} (variable reducted PEER) method performs
variable-reduction procedure to handle the non-invertibility of a graph
Laplacian matrix.")
    (license license:gpl2)))

(define-public r-mdols
  (package
    (name "r-mdols")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDOLS" version))
       (sha256
        (base32 "02fkrv6yn2r0ryphvxhlnv05xxq3j824ijnj75dag3v9kinx1ar2"))))
    (properties `((upstream-name . "MDOLS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MDOLS")
    (synopsis "Inference of Quadratic Functional for Moderate-Dimensional OLS")
    (description
     "Statistical inference for quadratic functional of the moderate-dimensional
linear model in Guo and Cheng (2021) <DOI:10.1080/01621459.2021.1893177>.")
    (license license:gpl3)))

(define-public r-mdmr
  (package
    (name "r-mdmr")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDMR" version))
       (sha256
        (base32 "1pv57xl7pjsqxz1j8mnfmjaa36mlr749yb4c44ppdrx6khvw662x"))))
    (properties `((upstream-name . "MDMR")))
    (build-system r-build-system)
    (propagated-inputs (list r-lme4 r-compquadform r-car))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/dmcartor/mdmr")
    (synopsis "Multivariate Distance Matrix Regression")
    (description
     "Allows users to conduct multivariate distance matrix regression using analytic
p-values and compute measures of effect size.  For details on the method, see
@code{McArtor}, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.")
    (license license:gpl2+)))

(define-public r-mdmb
  (package
    (name "r-mdmb")
    (version "1.9-22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdmb" version))
       (sha256
        (base32 "09lsp0niazw0v9pqnw5inw65j7p0irwwnxrr09iqbpjd7gqnlkrz"))))
    (properties `((upstream-name . "mdmb")))
    (build-system r-build-system)
    (propagated-inputs (list r-sirt
                             r-rcpparmadillo
                             r-rcpp
                             r-miceadds
                             r-coda
                             r-cdm))
    (home-page "https://github.com/alexanderrobitzsch/mdmb")
    (synopsis "Model Based Treatment of Missing Data")
    (description
     "This package contains model-based treatment of missing data for regression
models with missing values in covariates or the dependent variable using maximum
likelihood or Bayesian estimation (Ibrahim et al., 2005;
<doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b;
<doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>).  The regression
model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline
functions).  Multilevel models with missing data in predictors are available for
Bayesian estimation.  Substantive-model compatible multiple imputation can be
also conducted.")
    (license license:gpl2+)))

(define-public r-mdmapr
  (package
    (name "r-mdmapr")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDMAPR" version))
       (sha256
        (base32 "0w2n6qrjvyzf2wc1yw8a8k027g3y3w2k0z6wkwnj1p8q5zg24c66"))))
    (properties `((upstream-name . "MDMAPR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xfun
                             r-writexl
                             r-shinywidgets
                             r-shinyjs
                             r-shinydashboard
                             r-shiny
                             r-rmysql
                             r-readxl
                             r-reactable
                             r-plotly
                             r-leaflet-extras
                             r-leaflet
                             r-htmlwidgets
                             r-htmltools
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-dbi
                             r-bslib
                             r-berryfunctions))
    (home-page "https://github.com/HannerLab/MDMAPR")
    (synopsis "Molecular Detection Mapping and Analysis Platform")
    (description
     "Runs a Shiny web application that merges raw @code{qPCR} fluorescence data with
related metadata to visualize species presence/absence detection patterns and
assess data quality.  The application calculates threshold values from raw
fluorescence data using a method based on the second derivative method, Luu-The
et al (2005) <doi:10.2144/05382RR05>, and utilizes the â@code{chipPCRâ}
package by RÃ¶diger, Burdukiewicz, & Schierack (2015)
<doi:10.1093/bioinformatics/btv205> to calculate Cq values.  The application has
the ability to connect to a custom developed @code{MySQL} database to populate
the applications interface.  The application allows users to interact with
visualizations such as a dynamic map, amplification curves and standard curves,
that allow for zooming and/or filtering.  It also enables the generation of
customized exportable reports based on filtered mapping data.")
    (license license:gpl3)))

(define-public r-mdma
  (package
    (name "r-mdma")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDMA" version))
       (sha256
        (base32 "0lfn44p18jm6q1ci7m9r79jhfdd3x07489b7nadksn59xsianvaw"))))
    (properties `((upstream-name . "MDMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-car))
    (home-page "https://github.com/mathijsdeen/MDMA")
    (synopsis "Mathijs Deen's Miscellaneous Auxiliaries")
    (description
     "This package provides a variety of functions useful for data analysis,
selection, manipulation, and graphics.")
    (license license:gpl3)))

(define-public r-mdir-logrank
  (package
    (name "r-mdir-logrank")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdir.logrank" version))
       (sha256
        (base32 "0bhsr005dq9v85sjm2g9xlpksxwzsmsk14g0q5v3hkjvm5wk6xmv"))))
    (properties `((upstream-name . "mdir.logrank")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=mdir.logrank")
    (synopsis "Multiple-Direction Logrank Test")
    (description
     "Implemented are the one-sided and two-sided multiple-direction logrank test for
two-sample right censored data.  In addition to the statistics p-values are
calculated: 1.  For the one-sided testing problem one p-value based on a wild
bootstrap approach is determined.  2.  In the two-sided case one p-value based
on a chi-squared approximation and a second p-values based on a permutation
approach are calculated.  Ditzhaus, M. and Friedrich, S. (2018)
<@code{arXiv:1807.05504>}.  Ditzhaus, M. and Pauly, M. (2018)
<@code{arXiv:1808.05627>}.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mdimnormn
  (package
    (name "r-mdimnormn")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDimNormn" version))
       (sha256
        (base32 "080m0irx5v8l45fg9ig5yzcj92s3ah8a9aha288byszli1cchgpn"))))
    (properties `((upstream-name . "MDimNormn")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MDimNormn")
    (synopsis "Multi-Dimensional MA Normalization for Plate Effect")
    (description
     "Normalize data to minimize the difference between sample plates (batch effects).
 For given data in a matrix and grouping variable (or plate), the function
normn_MA normalizes the data on MA coordinates.  More details are in the
citation.  The primary method is Multi-MA'.  Other fitting functions on MA
coordinates can also be employed e.g. loess.")
    (license license:gpl3)))

(define-public r-mdhglm
  (package
    (name "r-mdhglm")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdhglm" version))
       (sha256
        (base32 "1myky8d2ilwdliqrzd2i5qvja82iz7w6qbigl7h4znpk8pdgrjb3"))))
    (properties `((upstream-name . "mdhglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-boot))
    (home-page "https://cran.r-project.org/package=mdhglm")
    (synopsis "Multivariate Double Hierarchical Generalized Linear Models")
    (description
     "Allows various models for multivariate response variables where each response is
assumed to follow double hierarchical generalized linear models.  In double
hierarchical generalized linear models, the mean, dispersion parameters for
variance of random effects, and residual variance can be further modeled as
random-effect models.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-mdgc
  (package
    (name "r-mdgc")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdgc" version))
       (sha256
        (base32 "0p2dfvngyv9rwbcjfw7s3diim9k2gbjn7qsmh7ybxhd529y4w0yl"))))
    (properties `((upstream-name . "mdgc")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-rcpparmadillo r-rcpp r-psqn r-bh))
    (native-inputs (list gfortran))
    (home-page "https://github.com/boennecd/mdgc")
    (synopsis "Missing Data Imputation Using Gaussian Copulas")
    (description
     "This package provides functions to impute missing values using Gaussian copulas
for mixed data types as described by Christoffersen et al. (2021)
<@code{arXiv:2102.02642>}.  The method is related to Hoff (2007)
<doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <@code{arXiv:1910.12845>} but
differs by making a direct approximation of the log marginal likelihood using an
extended version of the Fortran code created by Genz and Bretz (2002)
<doi:10.1198/106186002394> in addition to also support multinomial variables.")
    (license license:gpl2)))

(define-public r-mdftracks
  (package
    (name "r-mdftracks")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdftracks" version))
       (sha256
        (base32 "0v48ffs1dfbls4h0s6r9b7kklzc7v4xznprd8qczsazvw3jj3zgb"))))
    (properties `((upstream-name . "mdftracks")))
    (build-system r-build-system)
    (home-page "https://github.com/burgerga/mdftracks")
    (synopsis "Read and Write 'MTrackJ Data Files'")
    (description
     "M@code{TrackJ} is an @code{ImageJ} plugin for motion tracking and analysis (see
<https://imagescience.org/meijering/software/mtrackj/>).  This package reads and
writes M@code{TrackJ} Data Files ('.mdf', see
<https://imagescience.org/meijering/software/mtrackj/format/>).  It supports 2D
data and read/writes cluster, point, and channel information.  If desired,
generates track identifiers that are unique over the clusters.  See the project
page for more information and examples.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mdfs
  (package
    (name "r-mdfs")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDFS" version))
       (sha256
        (base32 "0gazmcgkgz915s3ljjdksm0bw4z6f4fz9qfzibdq6wscwcg9kfjn"))))
    (properties `((upstream-name . "MDFS")))
    (build-system r-build-system)
    (home-page "https://www.mdfs.it/")
    (synopsis "MultiDimensional Feature Selection")
    (description
     "This package provides functions for @code{MultiDimensional} Feature Selection
(MDFS): calculating multidimensional information gains, scoring variables,
finding important variables, plotting selection results.  This package includes
an optional CUDA implementation that speeds up information gain calculation
using NVIDIA GPGPUs.  R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.")
    (license license:gpl3)))

(define-public r-mdendro
  (package
    (name "r-mdendro")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdendro" version))
       (sha256
        (base32 "0am5kda7ydwiniqmypzmmqnaz4pkd9hp82vm0pdk3plak5b0dz6w"))))
    (properties `((upstream-name . "mdendro")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://webs-deim.urv.cat/~sergio.gomez/mdendro.php")
    (synopsis "Extended Agglomerative Hierarchical Clustering")
    (description
     "This package provides a comprehensive collection of linkage methods for
agglomerative hierarchical clustering on a matrix of proximity data (distances
or similarities), returning a multifurcated dendrogram or multidendrogram.
Multidendrograms can group more than two clusters when ties in proximity data
occur, and therefore they do not depend on the order of the input data.
Descriptive measures to analyze the resulting dendrogram are additionally
provided.")
    (license license:agpl3)))

(define-public r-mdei
  (package
    (name "r-mdei")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDEI" version))
       (sha256
        (base32 "1bj9cp73lyj2dzcycf5a196a3d8swblhxs4qwsyc7vmy4pwpxq43"))))
    (properties `((upstream-name . "MDEI")))
    (build-system r-build-system)
    (propagated-inputs (list r-splines2 r-rcpparmadillo r-rcpp r-ranger r-mass))
    (home-page "https://cran.r-project.org/package=MDEI")
    (synopsis "Implementing the Method of Direct Estimation and Inference")
    (description
     "Causal and statistical inference on an arbitrary treatment effect curve requires
care in both estimation and inference.  This package, implements the Method of
Direct Estimation and Inference as introduced in \"Estimation and Inference on
Nonlinear and Heterogeneous Effects\" by Ratkovic and Tingley (2023)
<doi:10.1086/723811>.  The method takes an outcome, variable of theoretical
interest (treatment), and set of variables and then returns a partial derivative
(marginal effect) of the treatment variable at each point along with uncertainty
intervals.  The approach offers two advances.  First, a split-sample approach is
used as a guard against over-fitting.  Second, the method uses a data-driven
interval derived from conformal inference, rather than relying on a normality
assumption on the error terms.")
    (license license:gpl2+)))

(define-public r-mded
  (package
    (name "r-mded")
    (version "0.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mded" version))
       (sha256
        (base32 "1j8fcz5yc70p9qd9l010xj1b625scdps8z1pqh75b45p2hiqbhlc"))))
    (properties `((upstream-name . "mded")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mded")
    (synopsis "Measuring the Difference Between Two Empirical Distributions")
    (description
     "This package provides a function for measuring the difference between two
independent or non-independent empirical distributions and returning a
significance level of the difference.")
    (license license:cc0)))

(define-public r-mde
  (package
    (name "r-mde")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mde" version))
       (sha256
        (base32 "121bzypndsx4abls1f7rjrvg98rqkb155vd5aiql8vi1lwgzy9jl"))))
    (properties `((upstream-name . "mde")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Nelson-Gon/mde")
    (synopsis "Missing Data Explorer")
    (description
     "Correct identification and handling of missing data is one of the most important
steps in any analysis.  To aid this process, mde provides a very easy to use yet
robust framework to quickly get an idea of where the missing data lies and
therefore find the most appropriate action to take.  Graham WJ (2009)
<doi:10.1146/annurev.psych.58.110405.085530>.")
    (license license:gpl3)))

(define-public r-mddmaps
  (package
    (name "r-mddmaps")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mddmaps" version))
       (sha256
        (base32 "1yxq4mzw67wfnbhfrllxpq0warpwfqsq6lfwnpr3cwbfqbil60p0"))))
    (properties `((upstream-name . "mddmaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-stringr r-readr r-progress r-httr))
    (home-page "<https://github.com/alrobles/mddmaps>")
    (synopsis "Download World Mammal Maps")
    (description
     "Lightweight maps of mammals of the world.  These maps are a comprehensive
collection of maps aligned with the Mammal Diversity Database taxonomy of the
American Society of Mammalogists.  They are generated at low resolution for easy
access, consultation and manipulation in shapefile format.  The package connects
to a binary backup hosted in the Digital Ocean cloud service and allows
individual or batch download of any mammal species in the mdd taxonomy by
providing the scientific species name.")
    (license license:gpl3+)))

(define-public r-mddc
  (package
    (name "r-mddc")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDDC" version))
       (sha256
        (base32 "1c02qzf8hpvjpjqkkcmxw0c3vb7wv0hnq6cwpfyfj1ry7a1phfiw"))))
    (properties `((upstream-name . "MDDC")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-mass
                             r-ggplot2
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/niuniular/MDDC")
    (synopsis
     "Modified Detecting Deviating Cells Algorithm in Pharmacovigilance")
    (description
     "This package provides methods for detecting signals related to (adverse event,
medical product e.g. drugs, vaccines) pairs, a data generation function for
simulating pharmacovigilance datasets, and various utility functions.  For more
details please see Liu A., Mukhopadhyay R., and Markatou M.
<doi:10.48550/@code{arXiv.2410.01168>}.")
    (license license:gpl3)))

(define-public r-mdbr
  (package
    (name "r-mdbr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdbr" version))
       (sha256
        (base32 "0mzd0qvwq3dvqdhp0dk7ad11m9bqh8jf3m7b8i6255s7c3rs3r0h"))))
    (properties `((upstream-name . "mdbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr))
    (home-page "https://k5cents.github.io/mdbr/")
    (synopsis "Work with Microsoft Access Files")
    (description
     "Use the open source MDB Tools utilities <https://github.com/mdbtools/mdbtools/>.
 Primarily used for converting proprietary Microsoft Access files to simple text
files and then reading those as data frames.")
    (license license:gpl3)))

(define-public r-mdbed
  (package
    (name "r-mdbed")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MDBED" version))
       (sha256
        (base32 "1r3sizb2mqd4pqjy25krwmsjfkr6cl7lkd8db5jn3hgxx3qh6sgc"))))
    (properties `((upstream-name . "MDBED")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-psych
                             r-orthopolynom
                             r-lattice
                             r-ggplot2
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=MDBED")
    (synopsis "Moran-Downton Bivariate Exponential Distribution")
    (description
     "This package provides 3D plots of the Moran-Downton bivariate exponential
distribution (MDBED), generates bivariate random values, and also provides
values of the joint and conditional PDFs and CDFs.  Nagao M, Kadoya M (1971)
<http://hdl.handle.net/2433/124795>.  Balakrishna N, Lai CD (2009)
<doi:10.1007/b101765>.")
    (license license:gpl3)))

(define-public r-mdatools
  (package
    (name "r-mdatools")
    (version "0.14.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mdatools" version))
       (sha256
        (base32 "0w54phv95cv1mvz2mq7bijw9b03byg7bvxz98a81dzc907j8r2bh"))))
    (properties `((upstream-name . "mdatools")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://mda.tools")
    (synopsis "Multivariate Data Analysis for Chemometrics")
    (description
     "Projection based methods for preprocessing, exploring and analysis of
multivariate data used in chemometrics.  S. Kucheryavskiy (2020)
<doi:10.1016/j.chemolab.2020.103937>.")
    (license license:expat)))

(define-public r-mdag
  (package
    (name "r-mdag")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mDAG" version))
       (sha256
        (base32 "0k85sl0jmpcpiz92cz6x3kd8jc6yyfv9z4wm1kyaikcf5q4qlad4"))))
    (properties `((upstream-name . "mDAG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-pcalg
                             r-nnet
                             r-mgm
                             r-logistf
                             r-bnlearn))
    (home-page "https://cran.r-project.org/package=mDAG")
    (synopsis
     "Inferring Causal Network from Mixed Observational Data Using a Directed Acyclic Graph")
    (description
     "Learning a mixed directed acyclic graph based on both continuous and categorical
data.")
    (license license:gpl2+)))

(define-public r-md4r
  (package
    (name "r-md4r")
    (version "0.5.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "md4r" version))
       (sha256
        (base32 "1kdy6izd4gxqf4bdrb3hav3mhn1gw9bjgmdk4r6j9qhm7mmkkkps"))))
    (properties `((upstream-name . "md4r")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-textutils
                             r-stringr
                             r-rcpp
                             r-purrr
                             r-glue
                             r-cli
                             r-checkmate))
    (home-page "https://cran.r-project.org/package=md4r")
    (synopsis "Markdown Parser Implemented using the 'MD4C' Library")
    (description
     "This package provides an R wrapper for the MD4C (Markdown for C') library.
Functions exist for parsing markdown ('@code{CommonMark} compliant) along with
support for other common markdown extensions (e.g. @code{GitHub} flavored
markdown, @code{LaTeX} equation support, etc.).  The package also provides a
number of higher level functions for exploring and manipulating markdown
abstract syntax trees as well as translating and displaying the documents.")
    (license license:expat)))

(define-public r-md-log
  (package
    (name "r-md-log")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "md.log" version))
       (sha256
        (base32 "1k0l3imx1jdlf6fks7x8q8lg2l4090fq069h5x4i0nwq71sfslj0"))))
    (properties `((upstream-name . "md.log")))
    (build-system r-build-system)
    (propagated-inputs (list r-futile-logger))
    (home-page "https://github.com/haghish/md.log")
    (synopsis "Produces Markdown Log File with a Built-in Function Call")
    (description
     "This package produces clean and neat Markdown log file and also provide an
argument to include the function call inside the Markdown log.")
    (license license:expat)))

(define-public r-md
  (package
    (name "r-md")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "md" version))
       (sha256
        (base32 "13z8f3p84kivk6j58fb1qpzrmml41mq9pgv9nv2gvxrhyhanzi46"))))
    (properties `((upstream-name . "md")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=md")
    (synopsis
     "Selecting Bandwidth for Kernel Density Estimator with Minimum Distance Method")
    (description
     "Selects bandwidth for the kernel density estimator with minimum distance method
as proposed by Devroye and Lugosi (1996).  The minimum distance method directly
selects the optimal kernel density estimator from countably infinite kernel
density estimators and indirectly selects the optimal bandwidth.  This package
selects the optimal bandwidth from finite kernel density estimators.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mcwr
  (package
    (name "r-mcwr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcwr" version))
       (sha256
        (base32 "1aqmrxjmllq1waw3pzmwhqd07q02n1h1g6c0qi1fvay4xz0rwgik"))))
    (properties `((upstream-name . "mcwr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mcwr")
    (synopsis "Markov Chains with Rewards")
    (description
     "In the context of multistate models, which are popular in sociology, demography,
and epidemiology, Markov chain with rewards calculations can help to refine
transition timings and so obtain more accurate estimates.  The package code
accommodates up to nine transient states and irregular age (time) intervals.
Traditional demographic life tables result as a special case.  Formulas and
methods involved are explained in detail in the accompanying article: Schneider
/ Myrskyla / van Raalte (2021): Flexible Transition Timing in Discrete-Time
Multistate Life Tables Using Markov Chains with Rewards, MPIDR Working Paper
WP-2021-002.")
    (license license:expat)))

(define-public r-mcvis
  (package
    (name "r-mcvis")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcvis" version))
       (sha256
        (base32 "0dc7jwdfzyn6x785lbwikpql96b3gh9fhb65fv4sk9ib28b2kpmy"))))
    (properties `((upstream-name . "mcvis")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rlang
                             r-reshape2
                             r-purrr
                             r-psych
                             r-magrittr
                             r-igraph
                             r-ggplot2
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mcvis")
    (synopsis "Multi-Collinearity Visualization")
    (description
     "Visualize the relationship between linear regression variables and causes of
multi-collinearity.  Implements the method in Lin et.  al. (2020)
<doi:10.1080/10618600.2020.1779729>.")
    (license license:gpl3)))

(define-public r-mcunit
  (package
    (name "r-mcunit")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcunit" version))
       (sha256
        (base32 "16nnm50g87iaf058b1w5w5hphx99xplczndcbv1356f2gs2gfy6l"))))
    (properties `((upstream-name . "mcunit")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-simctest r-rlang r-rdpack))
    (native-inputs (list r-knitr))
    (home-page "https://bitbucket.org/agandy/mcunit/")
    (synopsis "Unit Tests for MC Methods")
    (description
     "Unit testing for Monte Carlo methods, particularly Markov Chain Monte Carlo
(MCMC) methods, are implemented as extensions of the testthat package.  The MCMC
methods check whether the MCMC chain has the correct invariant distribution.
They do not check other properties of successful samplers such as whether the
chain can reach all points, i.e.  whether is recurrent.  The tests require the
ability to sample from the prior and to run steps of the MCMC chain.  The
methodology is described in Gandy and Scott (2020) <@code{arXiv:2001.06465>}.")
    (license license:gpl3)))

(define-public r-mctrend
  (package
    (name "r-mctrend")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCTrend" version))
       (sha256
        (base32 "0nfiwpd7pynl7a3cy1c8g3iwdc6bfay0726c3skz4ifmjlgp0kg2"))))
    (properties `((upstream-name . "MCTrend")))
    (build-system r-build-system)
    (propagated-inputs (list r-trend
                             r-reshape2
                             r-magrittr
                             r-lmomco
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MCTrend")
    (synopsis "Monte Carlo Trend Analysis")
    (description
     "Application of a test to rule out that trends detected in hydrological time
series are explained exclusively by the randomness of the climate.  Based on:
Ricchetti, (2018) <https://repositorio.uchile.cl/handle/2250/168487>.")
    (license license:gpl3)))

(define-public r-mctq
  (package
    (name "r-mctq")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mctq" version))
       (sha256
        (base32 "1qp9jmpi6pbzn8jqg0i35zznc1sawi1jnky5cdf26hlyi2bjxy48"))))
    (properties `((upstream-name . "mctq")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate
                             r-lifecycle
                             r-hms
                             r-ggplot2
                             r-dplyr
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/mctq/")
    (synopsis "Tools to Process the Munich ChronoType Questionnaire (MCTQ)")
    (description
     "This package provides a complete toolkit to process the Munich @code{ChronoType}
Questionnaire (MCTQ) for its three versions (standard, micro, and shift).  MCTQ
is a quantitative and validated tool to assess chronotypes using peoples sleep
behavior, originally presented by Till Roenneberg, Anna Wirz-Justice, and Martha
Merrow (2003, <doi:10.1177/0748730402239679>).")
    (license license:expat)))

(define-public r-mctest
  (package
    (name "r-mctest")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mctest" version))
       (sha256
        (base32 "1d248r9ifwl1xhvka6z69l943rvbq7s7ag6vqp9lljkcybvg75f2"))))
    (properties `((upstream-name . "mctest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mctest")
    (synopsis "Multicollinearity Diagnostic Measures")
    (description
     "Package computes popular and widely used multicollinearity diagnostic measures
<doi:10.17576/jsm-2019-4809-26> and <doi:10.32614/RJ-2016-062> .  Package also
indicates which regressors may be the reason of collinearity among regressors.")
    (license license:gpl2+)))

(define-public r-mcstatsim
  (package
    (name "r-mcstatsim")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcstatsim" version))
       (sha256
        (base32 "0v1nxn4jbk941x6vskpiqrgdzfijlhlwzhavwzyy19x2f3wc91iy"))))
    (properties `((upstream-name . "mcstatsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply))
    (home-page "https://github.com/ielbadisy/mcstatsim")
    (synopsis
     "Monte Carlo Statistical Simulation Tools Using a Functional Approach")
    (description
     "This package provides a lightweight package designed to facilitate statistical
simulations through functional programming.  It centralizes the simulation
process into a single higher-order function, enhancing manageability and
usability without adding overhead from external dependencies.  The package
includes ready-to-use functions for common simulation targets.  A detailed
example can be found on <https://github.com/ielbadisy/mcstatsim>.")
    (license license:agpl3+)))

(define-public r-mcstats
  (package
    (name "r-mcstats")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcStats" version))
       (sha256
        (base32 "1r8dxdfcvznl2968xr6fbany1rh7wq5z1p7fhdf5g4b5l28j8pf9"))))
    (properties `((upstream-name . "mcStats")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-magrittr
                             r-gridextra
                             r-ggthemes
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=mcStats")
    (synopsis "Visualize Results of Statistical Hypothesis Tests")
    (description
     "This package provides functionality to produce graphs of sampling distributions
of test 	statistics from a variety of common statistical tests.  With only a few
keystrokes, 	the user can conduct a hypothesis test and visualize the test
statistic and 		corresponding p-value through the shading of its sampling
distribution.  Initially 	created for statistics at Middlebury College.")
    (license license:gpl3)))

(define-public r-mcsim
  (package
    (name "r-mcsim")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCSim" version))
       (sha256
        (base32 "1nqry41qa5c02an5fxm3y1g3w1vcqjgsy1an95i3szz1w1ndaj14"))))
    (properties `((upstream-name . "MCSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-circstats))
    (home-page "https://cran.r-project.org/package=MCSim")
    (synopsis "Determine the Optimal Number of Clusters")
    (description
     "Identifies the optimal number of clusters by calculating the similarity between
two clustering methods at the same number of clusters using the corrected
indices of Rand and Jaccard as described in Albatineh and Niewiadomska-Bugaj
(2011).  The number of clusters at which the index attain its maximum more
frequently is a candidate for being the optimal number of clusters.")
    (license license:gpl2)))

(define-public r-mcs
  (package
    (name "r-mcs")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCS" version))
       (sha256
        (base32 "1kiz1jq1bm2n8f33nsybp5jfrzzl9xbsi3m9l8818ybmph99xms6"))))
    (properties `((upstream-name . "MCS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MCS")
    (synopsis "Model Confidence Set Procedure")
    (description
     "Perform the model confidence set procedure of Hansen et al (2011)
<doi:10.3982/ECTA5771>.")
    (license license:gpl2)))

(define-public r-mcrpioda
  (package
    (name "r-mcrpioda")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcrPioda" version))
       (sha256
        (base32 "1f3gwcbm7sgy0ny9q7l6dkc1a74sjh92x8h54rimm57b98avij4k"))))
    (properties `((upstream-name . "mcrPioda")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-rrcov r-robslopes r-mixtools))
    (native-inputs (list pkg-config gfortran))
    (home-page "https://cran.r-project.org/package=mcrPioda")
    (synopsis
     "Method Comparison Regression - Mcr Fork for M- And MM-Deming Regression")
    (description
     "Regression methods to quantify the relation between two measurement methods are
provided by this package.  In particular it addresses regression problems with
errors in both variables and without repeated measurements.  It implements the
Clinical Laboratory Standard International (CLSI) recommendations (see J. A.
Budd et al. (2018,
<https://clsi.org/standards/products/method-evaluation/documents/ep09/>) for
analytical method comparison and bias estimation using patient samples.
Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators
are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and J.
Raymaekers and F. Dufey (2022, <@code{arXiv:2202:08060>}).  Further the robust
M-Deming and MM-Deming (experimental) are available, see G. Pioda (2021,
<@code{arXiv:2105:04628>}).  A comprehensive overview over the implemented
methods and references can be found in the manual pages @code{mcrPioda-package}
and mcreg'.")
    (license license:gpl3+)))

(define-public r-mcreplicate
  (package
    (name "r-mcreplicate")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcreplicate" version))
       (sha256
        (base32 "12pmfp75sc00cdrkx4xniq74qqx3y67qxwdrrbp2mf562pdn2gmn"))))
    (properties `((upstream-name . "mcreplicate")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mcreplicate")
    (synopsis "Multi-Core Replicate")
    (description
     "Multi-core replication function to make it easier to do fast Monte Carlo
simulation.  Based on the mcreplicate() function from the rethinking package.
The rethinking package requires installing rstan', which is onerous to install,
while also not adding capabilities to this function.")
    (license license:agpl3+)))

(define-public r-mcradds
  (package
    (name "r-mcradds")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcradds" version))
       (sha256
        (base32 "07wh4xh1dr966ybncbh3938kpnjny7qikiiwagivv90mzapf2g7m"))))
    (properties `((upstream-name . "mcradds")))
    (build-system r-build-system)
    (propagated-inputs (list r-vca
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-proc
                             r-mcr
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-formatters
                             r-dplyr
                             r-desctools
                             r-checkmate
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kaigu1990/mcradds")
    (synopsis "Processing and Analyzing of Diagnostics Trials")
    (description
     "This package provides methods and functions to analyze the quantitative or
qualitative performance for diagnostic assays, and outliers detection, reader
precision and reference range are discussed.  Most of the methods and algorithms
refer to CLSI (Clinical & Laboratory Standards Institute) recommendations and
NMPA (National Medical Products Administration) guidelines.  In additional,
relevant plots are constructed by ggplot2'.")
    (license license:gpl3+)))

(define-public r-mcr
  (package
    (name "r-mcr")
    (version "1.3.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcr" version))
       (sha256
        (base32 "0gbpnbs8mgqpyiayddcaf1xwh908gk1n375bqpnvfppyj0wz3yr9"))))
    (properties `((upstream-name . "mcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-robslopes))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=mcr")
    (synopsis "Method Comparison Regression")
    (description
     "Regression methods to quantify the relation between two measurement methods are
provided by this package.  In particular it addresses regression problems with
errors in both variables and without repeated measurements.  It implements the
CLSI recommendations (see J. A. Budd et al. (2018,
<https://clsi.org/standards/products/method-evaluation/documents/ep09/>) for
analytical method comparison and bias estimation using patient samples.
Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators
are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and J.
Raymaekers and F. Dufey (2022, <@code{arXiv:2202:08060>}).  A comprehensive
overview over the implemented methods and references can be found in the manual
pages \"mcr-package\" and \"mcreg\".")
    (license license:gpl3+)))

(define-public r-mcptests
  (package
    (name "r-mcptests")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCPtests" version))
       (sha256
        (base32 "1lzlvzw60fb0mllbl0dx8s30zigzbl9shjas629syb9yw55ypxa2"))))
    (properties `((upstream-name . "MCPtests")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-writexl r-smr r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=MCPtests")
    (synopsis "Multiples Comparisons Procedures")
    (description
     "This package performs the execution of the main procedures of multiple
comparisons in the literature, Scott-Knott (1974)
<http://www.jstor.org/stable/2529204>, Batista (2016)
<http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic
representations and export to different extensions of its results.  An
additional part of the package is the presence of the performance evaluation of
the tests (Type I error per experiment and the power).  This will assist the
user in making the decision for the chosen test.")
    (license license:gpl2+)))

(define-public r-mcprogress
  (package
    (name "r-mcprogress")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcprogress" version))
       (sha256
        (base32 "1b1g92sw5x2rz21hl6fnfqc9q1bhawmbj8f4pbq6llljxb85k0id"))))
    (properties `((upstream-name . "mcprogress")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/myles-lewis/mcprogress")
    (synopsis "Progress Bars and Messages for Parallel Processes")
    (description
     "This package provides tools for monitoring progress during parallel processing.
Lightweight package which acts as a wrapper around mclapply() and adds a
progress bar to it in RStudio or Linux environments.  Simply replace your
original call to mclapply() with pmclapply().  A progress bar can also be
displayed during parallelisation via the foreach package.  Also included are
functions to safely print messages (including error messages) from within
parallelised code, which can be useful for debugging parallelised R code.")
    (license license:gpl3+)))

(define-public r-mcprofile
  (package
    (name "r-mcprofile")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcprofile" version))
       (sha256
        (base32 "118n3pxzjm4j9y09d1n2hw7pm0b8b0m8mnm5v9kq76vignklh75l"))))
    (properties `((upstream-name . "mcprofile")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-mvtnorm r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mcprofile")
    (synopsis
     "Testing Generalized Linear Hypotheses for Generalized Linear Model Parameters by Profile Deviance")
    (description
     "Calculation of signed root deviance profiles for linear combinations of
parameters in a generalized linear model.  Multiple tests and simultaneous
confidence intervals are provided.")
    (license license:gpl2+)))

(define-public r-mcpmodpack
  (package
    (name "r-mcpmodpack")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCPModPack" version))
       (sha256
        (base32 "00s5jx0affbphrl61i9x7vpccjbgnwskc4ly7kg7xff58i96fkri"))))
    (properties `((upstream-name . "MCPModPack")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard
                             r-shiny
                             r-rcppnumerical
                             r-rcppeigen
                             r-rcpp
                             r-officer
                             r-mvtnorm
                             r-flextable
                             r-devemf))
    (home-page "https://github.com/medianasoft/MCPModPack")
    (synopsis "Simulation-Based Design and Analysis of Dose-Finding Trials")
    (description
     "An efficient implementation of the MCPMod (Multiple Comparisons and Modeling)
method to support a simulation-based design and analysis of dose-finding trials
with normally distributed, binary and count endpoints (Bretz et al. (2005)
<doi:10.1111/j.1541-0420.2005.00344.x>).")
    (license license:gpl3)))

(define-public r-mcpmodgeneral
  (package
    (name "r-mcpmodgeneral")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCPModGeneral" version))
       (sha256
        (base32 "011rvj9rp5hj5nwxdkfg1i7n2121pqh8f4hk0dvbyf62121k0gxp"))))
    (properties `((upstream-name . "MCPModGeneral")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-dosefinding))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MCPModGeneral")
    (synopsis "Supplement to the 'DoseFinding' Package for the General Case")
    (description
     "Analyzes non-normal data via the Multiple Comparison Procedures and Modeling
approach (MCP-Mod).  Many functions rely on the @code{DoseFinding} package.
This package makes it so the user does not need to provide or calculate the mu
vector and S matrix.  Instead, the user typically supplies the data in its raw
form, and this package will calculate the needed objects and passes them into
the @code{DoseFinding} functions.  If the user wishes to primarily use the
functions provided in the @code{DoseFinding} package, a singular function
(@code{prepareGen}()) will provide mu and S. The package currently handles power
analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial
data.  The MCP-Mod procedure can also be applied to survival data, but power
analysis is not available.  Bretz, F., Pinheiro, J. C., and Branson, M. (2005)
<doi:10.1111/j.1541-0420.2005.00344.x>.  Buckland, S. T., Burnham, K. P. and
Augustin, N. H. (1997) <doi:10.2307/2533961>.  Pinheiro, J. C., Bornkamp, B.,
Glimm, E. and Bretz, F. (2014) <doi:10.1002/sim.6052>.")
    (license license:gpl3)))

(define-public r-mcpmodbc
  (package
    (name "r-mcpmodbc")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCPModBC" version))
       (sha256
        (base32 "0gzcxn3pf86k32ccvbsh4g5awcxcnsr5c48lkyrpqgwr0k3g0sjs"))))
    (properties `((upstream-name . "MCPModBC")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rlang
                             r-nleqslv
                             r-foreach
                             r-dplyr
                             r-dosefinding
                             r-dorng
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=MCPModBC")
    (synopsis
     "Improved Inference in Multiple Comparison Procedure â Modelling")
    (description
     "Implementation of Multiple Comparison Procedures with Modeling (MCP-Mod)
procedure with bias-corrected estimators and second-order covariance matrices as
described in Diniz, Gallardo and Magalhaes (2023) <doi:10.1002/pst.2303>.")
    (license license:gpl2+)))

(define-public r-mcpmod
  (package
    (name "r-mcpmod")
    (version "1.0-10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCPMod" version))
       (sha256
        (base32 "0j15dpxy62vwbd6pcsvkbpi1nklh0yjzwf53kcmv4jhj0v6xmpp9"))))
    (properties `((upstream-name . "MCPMod")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-lattice))
    (home-page "https://cran.r-project.org/package=MCPMod")
    (synopsis "Design and Analysis of Dose-Finding Studies")
    (description
     "This package implements a methodology for the design and analysis of
dose-response studies that combines aspects of multiple comparison procedures
and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61,
738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>).  The package provides tools
for the analysis of dose finding trials as well as a variety of tools necessary
to plan a trial to be conducted with the MCP-Mod methodology.  Please note: The
MCPMod package will not be further developed, all future development of the
MCP-Mod methodology will be done in the @code{DoseFinding} R-package.")
    (license license:gpl3)))

(define-public r-mcparalleldo
  (package
    (name "r-mcparalleldo")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcparallelDo" version))
       (sha256
        (base32 "1ivh0d4f7ks6xwcjywk62h1zhr7h5bbhzpisky7973wi4g8vipbn"))))
    (properties `((upstream-name . "mcparallelDo")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-r-utils r-checkmate))
    (home-page "https://github.com/drknexus/mcparallelDo")
    (synopsis
     "Simplified Interface for Running Commands on Parallel Processes")
    (description
     "This package provides a function that wraps mcparallel() and mccollect() from
parallel with temporary variables and a task handler.  Wrapped in this way the
results of an mcparallel() call can be returned to the R session when the fork
is complete without explicitly issuing a specific mccollect() to retrieve the
value.  Outside of top-level tasks, multiple mcparallel() jobs can be retrieved
with a single call to @code{mcparallelDoCheck}().")
    (license license:gpl2)))

(define-public r-mcpan
  (package
    (name "r-mcpan")
    (version "1.1-21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCPAN" version))
       (sha256
        (base32 "0q1m0xg8825q9zjwxcz2h2n0dyr21q5bk29qbqpdhirlwm6f1a51"))))
    (properties `((upstream-name . "MCPAN")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-mvtnorm r-multcomp r-mcmcpack r-magic))
    (home-page "https://cran.r-project.org/package=MCPAN")
    (synopsis "Multiple Comparisons Using Normal Approximation")
    (description
     "Multiple contrast tests and simultaneous confidence intervals based on normal
approximation.  With implementations for binomial proportions in a 2xk setting
(risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity
indices (multinomial data) and expected values under lognormal assumption.
Approximative power calculation for multiple contrast tests of binomial and
Gaussian data.")
    (license license:gpl2)))

(define-public r-mcp
  (package
    (name "r-mcp")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcp" version))
       (sha256
        (base32 "1va2h974wl9kmc3y82a5wjxli4r2spka6chgrzn7v2w2ir1qckq7"))))
    (properties `((upstream-name . "mcp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tidybayes
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rjags
                             r-patchwork
                             r-magrittr
                             r-loo
                             r-ggplot2
                             r-future-apply
                             r-future
                             r-dplyr
                             r-coda
                             r-bayesplot))
    (home-page "https://lindeloev.github.io/mcp/")
    (synopsis "Regression with Multiple Change Points")
    (description
     "Flexible and informed regression with Multiple Change Points.  mcp can infer
change points in means, variances, autocorrelation structure, and any
combination of these, as well as the parameters of the segments in between.  All
parameters are estimated with uncertainty and prediction intervals are supported
- also near the change points.  mcp supports hypothesis testing via
Savage-Dickey density ratio, posterior contrasts, and cross-validation.  mcp is
described in LindelÃ¸v (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes
the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570>
and Stephens (1994) <doi:10.2307/2986119>.")
    (license license:gpl2)))

(define-public r-mcount
  (package
    (name "r-mcount")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcount" version))
       (sha256
        (base32 "1l42d4xd2xyvgzvc8sj59qsnpfn2k8x57dqv4bx9yschm25hspdd"))))
    (properties `((upstream-name . "mcount")))
    (build-system r-build-system)
    (propagated-inputs (list r-bbmle))
    (home-page "https://cran.r-project.org/package=mcount")
    (synopsis "Marginalized Count Regression Models")
    (description
     "Implementation of marginalized models for zero-inflated count data.  This
package provides a tool to implement an estimation algorithm for the
marginalized count models, which directly makes inference on the effect of each
covariate on the marginal mean of the outcome.  The method involves the
marginalized zero-inflated Poisson model described in Long et al. (2014)
<doi:10.1002/sim.6293>.")
    (license license:gpl3)))

(define-public r-mcompanion
  (package
    (name "r-mcompanion")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcompanion" version))
       (sha256
        (base32 "0d4q4rc8qscjw7d9xhpc6lr9v14vy0jxzzpn68lxaxi4qr31jwrg"))))
    (properties `((upstream-name . "mcompanion")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-matrix r-mass r-gbutils))
    (home-page "https://geobosh.github.io/mcompanion/")
    (synopsis "Objects and Methods for Multi-Companion Matrices")
    (description
     "This package provides a class for multi-companion matrices with methods for
arithmetic and factorization.  A method for generation of multi-companion
matrices with prespecified spectral properties is provided, as well as some
utilities for periodically correlated and multivariate time series models.  See
Boshnakov (2002) <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan
(2009) <doi:10.1111/j.1467-9892.2009.00617.x>.")
    (license license:gpl2+)))

(define-public r-mcomp
  (package
    (name "r-mcomp")
    (version "2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mcomp" version))
       (sha256
        (base32 "1wz5fr3dhxn4s0qvxm2mzq9dbz3x0vgh6pixjrgk9d4i9w2p7s60"))))
    (properties `((upstream-name . "Mcomp")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-forecast))
    (home-page "http://pkg.robjhyndman.com/Mcomp/")
    (synopsis "Data from the M-Competitions")
    (description
     "The 1001 time series from the M-competition (Makridakis et al.  1982)
<DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3
competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.")
    (license license:gpl3)))

(define-public r-mcoe
  (package
    (name "r-mcoe")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCOE" version))
       (sha256
        (base32 "1ajijv3zl01am5qrkb6fdgrynnw31hgqdg8ph7bvm3z2y7z8zrbr"))))
    (properties `((upstream-name . "MCOE")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-odbc
                             r-magick
                             r-keyring
                             r-googlesheets4
                             r-ggthemes
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-dbi))
    (home-page "https://github.com/dobrowski/MCOE")
    (synopsis
     "Creates New Folders and Loads Standard Practices for Monterey County Office of Education")
    (description
     "Basic Setup for Projects in R for Monterey County Office of Education.  It
contains functions often used in the analysis of education data in the county
office including seeing if an item is not in a list, rounding in the manner the
general public expects, including logos for districts, switching between
district names and their county-district-school codes, accessing the local SQL
table and making thematically consistent graphs.")
    (license license:expat)))

(define-public r-mcode
  (package
    (name "r-mcode")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcODE" version))
       (sha256
        (base32 "1rnh9hqw97gxxs3ai5nnaszmyzyvv558p2b25i5h4218a2pkf8h3"))))
    (properties `((upstream-name . "mcODE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mcODE")
    (synopsis "Monte Carlo Solution of First Order Differential Equations")
    (description
     "Two functions for simulating the solution of initial value problems of the form
g'(x) = G(x, g) with g(x0) = g0.  One is an acceptance-rejection method.  The
other is a method based on the Mean Value Theorem.")
    (license license:gpl2+)))

(define-public r-mco
  (package
    (name "r-mco")
    (version "1.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mco" version))
       (sha256
        (base32 "18lyd6qp320jxw50hi6jvy3f408jws3gbfl6s9dqahd5b68yk242"))))
    (properties `((upstream-name . "mco")))
    (build-system r-build-system)
    (home-page "https://github.com/olafmersmann/mco")
    (synopsis
     "Multiple Criteria Optimization Algorithms and Related Functions")
    (description
     "This package provides a collection of function to solve multiple criteria
optimization problems using genetic algorithms (NSGA-II).  Also included is a
collection of test functions.")
    (license license:gpl2)))

(define-public r-mcmsupply
  (package
    (name "r-mcmsupply")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmsupply" version))
       (sha256
        (base32 "1130nl2qz36gyw9d6nqrq2zpa9p4171n45mqmjqzx8jbrp9za7y3"))))
    (properties `((upstream-name . "mcmsupply")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tidyr
                             r-tidybayes
                             r-tibble
                             r-stringr
                             r-runjags
                             r-rlang
                             r-readxl
                             r-r2jags
                             r-plyr
                             r-magrittr
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://hannahcomiskey.github.io/mcmsupply/")
    (synopsis
     "Estimating Public and Private Sector Contraceptive Market Supply Shares")
    (description
     "Family Planning programs and initiatives typically use nationally representative
surveys to estimate key indicators of a countryâs family planning progress.
However, in recent years, routinely collected family planning services data
(Service Statistics) have been used as a supplementary data source to bridge
gaps in the surveys.  The use of service statistics comes with the caveat that
adjustments need to be made for missing private sector contributions to the
contraceptive method supply chain.  Evaluating the supply source of modern
contraceptives often relies on Demographic Health Surveys (DHS), where many
countries do not have recent data beyond 2015/16.  Fortunately, in the absence
of recent surveys we can rely on statistical model-based estimates and
projections to fill the knowledge gap.  We present a Bayesian, hierarchical,
penalized-spline model with multivariate-normal spline coefficients, to account
for across method correlations, to produce country-specific,annual estimates for
the proportion of modern contraceptive methods coming from the public and
private sectors.  This package provides a quick and convenient way for users to
access the DHS modern contraceptive supply share data at national and
subnational administration levels, estimate, evaluate and plot annual estimates
with uncertainty for a sample of low- and middle-income countries.  Methods for
the estimation of method supply shares at the national level are described in
Comiskey, Alkema, Cahill (2022) <@code{arXiv:2212.03844>}.")
    (license license:expat)))

(define-public r-mcmst
  (package
    (name "r-mcmst")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcMST" version))
       (sha256
        (base32 "058da7hhcizh902pj99wmf5mhfd6fhpjvf6cprbsl0fx19x9l14w"))))
    (properties `((upstream-name . "mcMST")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-vegan
                             r-qgraph
                             r-igraph
                             r-gtools
                             r-grapherator
                             r-ggplot2
                             r-ecr
                             r-checkmate
                             r-bbmisc))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jakobbossek/mcMST")
    (synopsis "Toolbox for the Multi-Criteria Minimum Spanning Tree Problem")
    (description
     "Algorithms to approximate the Pareto-front of multi-criteria minimum spanning
tree problems.")
    (license license:bsd-2)))

(define-public r-mcmcvis
  (package
    (name "r-mcmcvis")
    (version "0.16.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCMCvis" version))
       (sha256
        (base32 "06a4dp5b33sqmrm6x64qaz6v7hn5lhqqygrx62gzpqj6v3yvcbhf"))))
    (properties `((upstream-name . "MCMCvis")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstan r-overlapping r-colorspace r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/caseyyoungflesh/MCMCvis")
    (synopsis "Tools to Visualize, Manipulate, and Summarize MCMC Output")
    (description
     "This package performs key functions for MCMC analysis using minimal code -
visualizes, manipulates, and summarizes MCMC output.  Functions support simple
and straightforward subsetting of model parameters within the calls, and produce
presentable and publication-ready output.  MCMC output may be derived from
Bayesian model output fit with Stan', NIMBLE', JAGS', and other software.")
    (license license:gpl3)))

(define-public r-mcmctreer
  (package
    (name "r-mcmctreer")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCMCtreeR" version))
       (sha256
        (base32 "00r6f62h7ryil7qxd25icvkqypjjssjsrz4i5sbqsnw5cdy5al8z"))))
    (properties `((upstream-name . "MCMCtreeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-coda r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MCMCtreeR")
    (synopsis
     "Prepare MCMCtree Analyses and Plot Bayesian Divergence Time Analyses Estimates on Trees")
    (description
     "This package provides functions to prepare time priors for MCMCtree analyses in
the PAML software from Yang (2007)<doi:10.1093/molbev/msm088> and plot
time-scaled phylogenies from any Bayesian divergence time analysis.  Most
time-calibrated node prior distributions require user-specified parameters.  The
package provides functions to refine these parameters, so that the resulting
prior distributions accurately reflect confidence in known, usually fossil, time
information.  These functions also enable users to visualise distributions and
write MCMCtree ready input files.  Additionally, the package supplies flexible
functions to visualise age uncertainty on a plotted tree with using node bars,
using branch widths proportional to the age uncertainty, or by plotting the full
posterior distributions on nodes.  Time-scaled phylogenetic plots can be
visualised with absolute and geological timescales .  All plotting functions are
applicable with output from any Bayesian software, not just MCMCtree'.")
    (license license:gpl2+)))

(define-public r-mcmcse
  (package
    (name "r-mcmcse")
    (version "1.5-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmcse" version))
       (sha256
        (base32 "0qvlkv7j35h2lxdzhbsbsrf1cgsbgf4in7lxgwmx7vs85k10v0ja"))))
    (properties `((upstream-name . "mcmcse")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-rcpparmadillo r-rcpp r-fftwtools
                             r-ellipse))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mcmcse")
    (synopsis "Monte Carlo Standard Errors for MCMC")
    (description
     "This package provides tools for computing Monte Carlo standard errors (MCSE) in
Markov chain Monte Carlo (MCMC) settings.  MCSE computation for expectation and
quantile estimators is supported as well as multivariate estimations.  The
package also provides functions for computing effective sample size and for
plotting Monte Carlo estimates versus sample size.")
    (license license:gpl2+)))

(define-public r-mcmcsae
  (package
    (name "r-mcmcsae")
    (version "0.7.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmcsae" version))
       (sha256
        (base32 "0q3rw6xbx5wqpa0hf0jldplcs9ry80bck95bn9g6bhzj0128ysm4"))))
    (properties `((upstream-name . "mcmcsae")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-matrixstats
                             r-matrix
                             r-loo
                             r-gigrvg))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mcmcsae")
    (synopsis "Markov Chain Monte Carlo Small Area Estimation")
    (description
     "Fit multi-level models with possibly correlated random effects using Markov
Chain Monte Carlo simulation.  Such models allow smoothing over space and time
and are useful in, for example, small area estimation.")
    (license license:gpl3)))

(define-public r-mcmcr
  (package
    (name "r-mcmcr")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmcr" version))
       (sha256
        (base32 "0ag8awdibrcylm7gshb60lxyv167k62zldffi7axni3z8w4s9cy5"))))
    (properties `((upstream-name . "mcmcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-universals
                             r-term
                             r-purrr
                             r-nlist
                             r-lifecycle
                             r-generics
                             r-extras
                             r-coda
                             r-chk
                             r-abind))
    (home-page "https://github.com/poissonconsulting/mcmcr")
    (synopsis "Manipulate MCMC Samples")
    (description
     "This package provides functions and classes to store, manipulate and summarise
Monte Carlo Markov Chain (MCMC) samples.  For more information see Brooks et al.
(2011) <isbn:978-1-4200-7941-8>.")
    (license license:expat)))

(define-public r-mcmcprecision
  (package
    (name "r-mcmcprecision")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCMCprecision" version))
       (sha256
        (base32 "0r0qchiv61sk3drrb0rhwsk55gci4w343hd2gsvclrlyb8r9qhaf"))))
    (properties `((upstream-name . "MCMCprecision")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-combinat))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/danheck/MCMCprecision")
    (synopsis "Precision of Discrete Parameters in Transdimensional MCMC")
    (description
     "Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC)
output, which is often used for Bayesian analysis of models with different
dimensionality (e.g., model selection).  Transdimensional MCMC (e.g., reversible
jump MCMC) relies on sampling a discrete model-indicator variable to estimate
the posterior model probabilities.  If only few switches occur between the
models, precision may be low and assessment based on the assumption of
independent samples misleading.  Based on the observed transition matrix of the
indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019,
Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws
posterior samples of the stationary distribution to (a) assess the uncertainty
in the estimated posterior model probabilities and (b) estimate the effective
sample size of the MCMC output.")
    (license license:gpl3)))

(define-public r-mcmcplots
  (package
    (name "r-mcmcplots")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmcplots" version))
       (sha256
        (base32 "0187z79gmvcrwqybxh3ckhcrqi0nqhvcvlczgxfkpq95y5czprdq"))))
    (properties `((upstream-name . "mcmcplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfsmisc r-denstrip r-colorspace r-coda))
    (home-page "https://cran.r-project.org/package=mcmcplots")
    (synopsis "Create Plots from MCMC Output")
    (description
     "This package provides functions for convenient plotting and viewing of MCMC
output.")
    (license license:gpl2+)))

(define-public r-mcmcoutput
  (package
    (name "r-mcmcoutput")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmcOutput" version))
       (sha256
        (base32 "1jw4wyppzlnxaa3iahbwx4gxs3zvppy980rc92dvy18y2sr5138v"))))
    (properties `((upstream-name . "mcmcOutput")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-hdinterval r-coda))
    (home-page "https://github.com/mikemeredith/mcmcOutput")
    (synopsis
     "Functions to Store, Manipulate and Display Markov Chain Monte Carlo (MCMC) Output")
    (description
     "This package implements a class ('@code{mcmcOutput}') for efficiently storing
and handling Markov chain Monte Carlo (MCMC) output, intended as an aid for
those writing customized MCMC samplers.  A range of constructor methods are
provided covering common output formats.  Functions are provided to generate
summary and diagnostic statistics and to display histograms or density plots of
posterior distributions, for the entire output, or subsets of draws, nodes, or
parameters.")
    (license license:gpl3+)))

(define-public r-mcmcensemble
  (package
    (name "r-mcmcensemble")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmcensemble" version))
       (sha256
        (base32 "0cbld6yr91jzi084pkzvfhqlqympqv9f91sj5gxvannnwszw3454"))))
    (properties `((upstream-name . "mcmcensemble")))
    (build-system r-build-system)
    (propagated-inputs (list r-progressr r-future-apply))
    (native-inputs (list r-knitr))
    (home-page "https://hugogruson.fr/mcmcensemble/")
    (synopsis "Ensemble Sampler for Affine-Invariant MCMC")
    (description
     "This package provides ensemble samplers for affine-invariant Monte Carlo Markov
Chain, which allow a faster convergence for badly scaled estimation problems.
Two samplers are proposed: the differential.evolution sampler from ter Braak and
Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the stretch sampler from
Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.")
    (license license:gpl2)))

(define-public r-mcmcderive
  (package
    (name "r-mcmcderive")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcmcderive" version))
       (sha256
        (base32 "0v89nyvr2d57zvjy9qq6drqb48s4n88hb65pima1gx4aqb479yfh"))))
    (properties `((upstream-name . "mcmcderive")))
    (build-system r-build-system)
    (propagated-inputs (list r-universals
                             r-purrr
                             r-nlist
                             r-mcmcr
                             r-extras
                             r-chk
                             r-abind))
    (home-page "https://github.com/poissonconsulting/mcmcderive")
    (synopsis "Derive MCMC Parameters")
    (description
     "Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples
using R code.  This allows Bayesian models to be fitted without the inclusion of
derived parameters which add unnecessary clutter and slow model fitting.  For
more information on MCMC samples see Brooks et al. (2011)
<isbn:978-1-4200-7941-8>.")
    (license license:expat)))

(define-public r-mcmc4extremes
  (package
    (name "r-mcmc4extremes")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCMC4Extremes" version))
       (sha256
        (base32 "06p6xdbja7vrdq4vnhybpkmyf4rkaihcmy9sc81sb0h2wa21vcq0"))))
    (properties `((upstream-name . "MCMC4Extremes")))
    (build-system r-build-system)
    (propagated-inputs (list r-evir))
    (home-page "https://cran.r-project.org/package=MCMC4Extremes")
    (synopsis "Posterior Distribution of Extreme Value Models in R")
    (description
     "This package provides some function to perform posterior estimation for some
distribution, with emphasis to extreme value distributions.  It contains some
extreme datasets, and functions that perform the runs of posterior points of the
GPD and GEV distribution.  The package calculate some important extreme measures
like return level for each t periods of time, and some plots as the predictive
distribution, and return level plots.")
    (license license:gpl2)))

(define-public r-mcmc-qpcr
  (package
    (name "r-mcmc-qpcr")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCMC.qpcr" version))
       (sha256
        (base32 "0v0d43wnq3swbrcsbg9qmwkmk4s50grd6cik4ng5baj2i6i4hd2n"))))
    (properties `((upstream-name . "MCMC.qpcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcglmm r-ggplot2 r-coda))
    (home-page "https://cran.r-project.org/package=MCMC.qpcr")
    (synopsis "Bayesian Analysis of qRT-PCR Data")
    (description
     "Quantitative RT-PCR data are analyzed using generalized linear mixed models
based on lognormal-Poisson error distribution, fitted using MCMC. Control genes
are not required but can be incorporated as Bayesian priors or, when template
abundances correlate with conditions, as trackers of global effects (common to
all genes).  The package also implements a lognormal model for higher-abundance
data and a \"classic\" model involving multi-gene normalization on a by-sample
basis.  Several plotting functions are included to extract and visualize
results.  The detailed tutorial is available here:
<https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.")
    (license license:gpl3)))

(define-public r-mcmc-otu
  (package
    (name "r-mcmc-otu")
    (version "1.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCMC.OTU" version))
       (sha256
        (base32 "1h1b0lw7d96q47sgq3px8j6rbkyhxhykm1c891px89khgg6hnszc"))))
    (properties `((upstream-name . "MCMC.OTU")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcglmm r-ggplot2 r-coda))
    (home-page "https://cran.r-project.org/package=MCMC.OTU")
    (synopsis
     "Bayesian Analysis of Multivariate Counts Data in DNA Metabarcoding and Ecology")
    (description
     "Poisson-lognormal generalized linear mixed model analysis of multivariate counts
data using MCMC, aiming to infer the changes in relative proportions of
individual variables.  The package was originally designed for sequence-based
analysis of microbial communities (\"metabarcoding\", variables = operational
taxonomic units, OTUs), but can be used for other types of multivariate counts,
such as in ecological applications (variables = species).  The results are
summarized and plotted using ggplot2 functions.  Includes functions to remove
sample and variable outliers and reformat counts into normalized log-transformed
values for correlation and principal component/coordinate analysis.  Walkthrough
and examples:
http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/@code{walkthroughExample_mcmcOTU_R.txt}.")
    (license license:gpl3)))

(define-public r-mcm
  (package
    (name "r-mcm")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCM" version))
       (sha256
        (base32 "17k1msiggqqbz1c8bjy9ig5nzvqddvz53mzfhaclj1453hy793xp"))))
    (properties `((upstream-name . "MCM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey
                             r-stringr
                             r-parameters
                             r-lme4
                             r-gee
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=MCM")
    (synopsis
     "Estimating and Testing Intergenerational Social Mobility Effect")
    (description
     "Estimate and test inter-generational social mobility effect on an outcome with
cross-sectional or longitudinal data.")
    (license license:gpl2)))

(define-public r-mclustcomp
  (package
    (name "r-mclustcomp")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mclustcomp" version))
       (sha256
        (base32 "1kxrms77m7n9lv32771zvwg82xqh5kx8al6f0bknsj8idq8aqbh3"))))
    (properties `((upstream-name . "mclustcomp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=mclustcomp")
    (synopsis "Measures for Comparing Clusters")
    (description
     "Given a set of data points, a clustering is defined as a disjoint partition
where each pair of sets in a partition has no overlapping elements.  This
package provides 25 methods that play a role somewhat similar to distance or
metric that measures similarity of two clusterings - or partitions.  For a more
detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.")
    (license license:gpl3+)))

(define-public r-mclustaddons
  (package
    (name "r-mclustaddons")
    (version "0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mclustAddons" version))
       (sha256
        (base32 "0vj84i1lrk6kxz1ymi164mb2hkjga6kydi6d0vb4c269kr7gi38j"))))
    (properties `((upstream-name . "mclustAddons")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown
                             r-rcpparmadillo
                             r-rcpp
                             r-mclust
                             r-knitr
                             r-iterators
                             r-foreach
                             r-dorng
                             r-doparallel
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://mclust-org.github.io/mclustAddons/")
    (synopsis "Addons for the 'mclust' Package")
    (description
     "Extend the functionality of the mclust package for Gaussian finite mixture
modeling by including: density estimation for data with bounded support
(Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal clustering using MEM (Modal
EM) algorithm for Gaussian mixtures (Scrucca, 2021 <doi:10.1002/sam.11527>);
entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023
<doi:10.1016/j.csda.2022.107582>).")
    (license license:gpl2+)))

(define-public r-mclm
  (package
    (name "r-mclm")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mclm" version))
       (sha256
        (base32 "1vpg6xsa8im60ld5mlm6yhf2prs6kmwn0sn12b9g8v27dgw3h0iq"))))
    (properties `((upstream-name . "mclm")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-xml2
                             r-tm
                             r-tibble
                             r-stringr
                             r-stringi
                             r-readr
                             r-rcpp
                             r-dplyr
                             r-crayon
                             r-ca))
    (home-page "https://github.com/masterclm/mclm")
    (synopsis "Mastering Corpus Linguistics Methods")
    (description
     "Read, inspect and process corpus files for quantitative corpus linguistics.
Obtain concordances via regular expressions, tokenize texts, and compute
frequencies and association measures.  Useful for collocation analysis, keywords
analysis and variationist studies (comparison of linguistic variants and of
linguistic varieties).")
    (license license:gpl2)))

(define-public r-mcl
  (package
    (name "r-mcl")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCL" version))
       (sha256
        (base32 "1w36h4vhd525h57pz6ik3abbsrvxnkcqypl2aj1ijb6wm7nfp4ri"))))
    (properties `((upstream-name . "MCL")))
    (build-system r-build-system)
    (propagated-inputs (list r-expm))
    (home-page "https://cran.r-project.org/package=MCL")
    (synopsis "Markov Cluster Algorithm")
    (description
     "This package contains the Markov cluster algorithm (MCL) for identifying
clusters in networks and graphs.  The algorithm simulates random walks on a (n x
n) matrix as the adjacency matrix of a graph.  It alternates an expansion step
and an inflation step until an equilibrium state is reached.")
    (license license:gpl2+)))

(define-public r-mcid
  (package
    (name "r-mcid")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCID" version))
       (sha256
        (base32 "034r4qxbqnl9hhkpv9r79p26lzj5qh0379ya43ynyvbbi8b8z3rl"))))
    (properties `((upstream-name . "MCID")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MCID")
    (synopsis "Estimating the Minimal Clinically Important Difference")
    (description
     "Apply the marginal classification method to achieve the purpose of providing the
point and interval estimates for the minimal clinically important difference
based on the classical anchor-based method.  For more details of the
methodology, please see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021)
<@code{arXiv:2108.11589>}.")
    (license license:gpl2+)))

(define-public r-mci
  (package
    (name "r-mci")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCI" version))
       (sha256
        (base32 "1wn8lcr5sq3697nsmly6syb9jv7p99ks0fy933ff5rx54x3rf9hz"))))
    (properties `((upstream-name . "MCI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MCI")
    (synopsis "Multiplicative Competitive Interaction (MCI) Model")
    (description
     "Market area models are used to analyze and predict store choices and market
areas concerning retail and service locations.  This package implements two
market area models (Huff Model, Multiplicative Competitive Interaction Model)
into R, while the emphases lie on 1.) fitting these models based on empirical
data via OLS regression and nonlinear techniques and 2.) data preparation and
processing (esp.  interaction matrices and data preparation for the MCI Model).")
    (license license:gpl2+)))

(define-public r-mchtest
  (package
    (name "r-mchtest")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MChtest" version))
       (sha256
        (base32 "1b3p6ry8gkvyphakn3504ibl2x8dbhx95saild0mj0iwm3sjyspf"))))
    (properties `((upstream-name . "MChtest")))
    (build-system r-build-system)
    (home-page "https://www.niaid.nih.gov/about/brb-staff-fay")
    (synopsis "Monte Carlo Hypothesis Tests with Sequential Stopping")
    (description
     "This package performs Monte Carlo hypothesis tests, allowing a couple of
different sequential stopping boundaries.  For example, a truncated sequential
probability ratio test boundary (Fay, Kim and Hachey, 2007
<DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford,
1991 <DOI:10.1093/biomet/78.2.301>.  Gives valid p-values and confidence
intervals on p-values.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mcgibbsit
  (package
    (name "r-mcgibbsit")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcgibbsit" version))
       (sha256
        (base32 "1ykg2g57zxaw2z9f7sgyd6kc6wx1lmad13asiy7q79ydzw8x8smw"))))
    (properties `((upstream-name . "mcgibbsit")))
    (build-system r-build-system)
    (propagated-inputs (list r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/r-gregmisc/mcgibbsit")
    (synopsis
     "Warnes and Raftery's 'MCGibbsit' MCMC Run Length and Convergence Diagnostic")
    (description
     "Implementation of Warnes & Raftery's MCGibbsit run-length and convergence
diagnostic for a set of (not-necessarily independent) Markov Chain Monte Carlo
(MCMC) samplers.  It combines the quantile estimate error-bounding approach of
the Raftery and Lewis MCMC run length diagnostic `gibbsit` with the between
verses within chain approach of the Gelman and Rubin MCMC convergence
diagnostic.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mcgf
  (package
    (name "r-mcgf")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcgf" version))
       (sha256
        (base32 "1xgrswmz4ra18qh3m46l4xkj13bq0lx15flhnx97j16y5dq3rl1k"))))
    (properties `((upstream-name . "mcgf")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tianxia-jia/mcgf")
    (synopsis
     "Markov Chain Gaussian Fields Simulation and Parameter Estimation")
    (description
     "Simulating and estimating (regime-switching) Markov chain Gaussian fields with
covariance functions of the Gneiting class (Gneiting 2002)
<doi:10.1198/016214502760047113>.  It supports parameter estimation by weighted
least squares and maximum likelihood methods, and produces Kriging forecasts and
intervals for existing and new locations.")
    (license license:expat)))

(define-public r-mcga
  (package
    (name "r-mcga")
    (version "3.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcga" version))
       (sha256
        (base32 "16vy5iy4k5i3mqchrrh8r229hyhwq4n2clvvp2mpz1iasvq7qz4f"))))
    (properties `((upstream-name . "mcga")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ga))
    (home-page "https://cran.r-project.org/package=mcga")
    (synopsis
     "Machine Coded Genetic Algorithms for Real-Valued Optimization Problems")
    (description
     "Machine coded genetic algorithm (MCGA) is a fast tool for real-valued
optimization problems.  It uses the byte representation of variables rather than
real-values.  It performs the classical crossover operations (uniform) on these
byte representations.  Mutation operator is also similar to classical mutation
operator, which is to say, it changes a randomly selected byte value of a
chromosome by +1 or -1 with probability 1/2.  In MCGAs there is no need for
encoding-decoding process and the classical operators are directly applicable on
real-values.  It is fast and can handle a wide range of a search space with high
precision.  Using a 256-unary alphabet is the main disadvantage of this
algorithm but a moderate size population is convenient for many problems.
Package also includes multi_mcga function for multi objective optimization
problems.  This function sorts the chromosomes using their ranks calculated from
the non-dominated sorting algorithm.")
    (license license:gpl2+)))

(define-public r-mcen
  (package
    (name "r-mcen")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcen" version))
       (sha256
        (base32 "16f7860h6hbpnv1drk793mz3vwm9q4ar6xkag05z0p22p69mm4qy"))))
    (properties `((upstream-name . "mcen")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-glmnet r-flexclust r-faraway))
    (home-page "https://cran.r-project.org/package=mcen")
    (synopsis "Multivariate Cluster Elastic Net")
    (description
     "Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood
(2018) <@code{arXiv:1707.03530>}.  The MCEN model simultaneously estimates
regression coefficients and a clustering of the responses for a multivariate
response model.  Currently accommodates the Gaussian and binomial likelihood.")
    (license license:expat)))

(define-public r-mcemglm
  (package
    (name "r-mcemglm")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcemGLM" version))
       (sha256
        (base32 "0159zak179ziax5n1mmncidg50cjiqzfyc8j96585csah1dzx01d"))))
    (properties `((upstream-name . "mcemGLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-trust r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=mcemGLM")
    (synopsis
     "Maximum Likelihood Estimation for Generalized Linear Mixed Models")
    (description
     "Maximum likelihood estimation for generalized linear mixed models via Monte
Carlo EM. For a description of the algorithm see Brian S. Caffo, Wolfgang Jank
and Galin L. Jones (2005) <DOI:10.1111/j.1467-9868.2005.00499.x>.")
    (license license:gpl2+)))

(define-public r-mcda
  (package
    (name "r-mcda")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCDA" version))
       (sha256
        (base32 "060mqk1cyxh2v68dr8533zr4k3wkz2f4k22400xgj40rwgj9a355"))))
    (properties `((upstream-name . "MCDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-triangle
                             r-rglpk
                             r-rcolorbrewer
                             r-plyr
                             r-glpkapi
                             r-ggplot2
                             r-combinat))
    (home-page "https://github.com/paterijk/MCDA")
    (synopsis "Support for the Multicriteria Decision Aiding Process")
    (description
     "Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with
algorithms, preference elicitation and data visualisation functions.  SÃ©bastien
Bigaret, Richard Hodgett, Patrick Meyer, Tatyana Mironova, Alexandru Olteanu
(2017) Supporting the multi-criteria decision aiding process : R and the MCDA
package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4, pages 169 -
194 <doi:10.1007/s40070-017-0064-1>.")
    (license (license:fsdg-compatible "EUPL (>= 1.1)"))))

(define-public r-mccr
  (package
    (name "r-mccr")
    (version "0.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mccr" version))
       (sha256
        (base32 "1scqds1yx317qmjappy2h1m564dcmsqqfpdm8pm3plalag16xrhj"))))
    (properties `((upstream-name . "mccr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mccr")
    (synopsis "The Matthews Correlation Coefficient")
    (description
     "The Matthews correlation coefficient (MCC) score is calculated (Matthews BW
(1975) <DOI:10.1016/0005-2795(75)90109-9>).")
    (license license:expat)))

(define-public r-mccmeiv
  (package
    (name "r-mccmeiv")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mccmeiv" version))
       (sha256
        (base32 "1lkghvvwxvg474p37h1mpbackr5qmy05qkx8yiq4x066wrfq1w1i"))))
    (properties `((upstream-name . "mccmeiv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-numderiv r-mass))
    (home-page "https://cran.r-project.org/package=mccmeiv")
    (synopsis
     "Analysis of Matched Case Control Data with a Mismeasured Exposure that is Accompanied by Instrumental Variables")
    (description
     "Applying the methodology from Manuel et al.  to estimate parameters using a
matched case control data with a mismeasured exposure variable that is
accompanied by instrumental variables (Submitted).")
    (license license:gpl2)))

(define-public r-mccm
  (package
    (name "r-mccm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCCM" version))
       (sha256
        (base32 "1iyd81smamxxy97gzb7jzxkys94ql9nchik3aszkd91zicixd12k"))))
    (properties `((upstream-name . "MCCM")))
    (build-system r-build-system)
    (propagated-inputs (list r-polycor r-mvtnorm r-mass r-lavaan))
    (home-page "https://cran.r-project.org/package=MCCM")
    (synopsis "Mixed Correlation Coefficient Matrix")
    (description
     "The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of
Moments) methods are applied to estimate mixed correlation coefficient matrix
(Pearson, Polyseries, Polychoric), which can be estimated in pairs or
simultaneously.  For more information see Peng Zhang and Ben Liu (2024)
<doi:10.1080/10618600.2023.2257251>; Ben Liu and Peng Zhang (2024)
<doi:10.48550/@code{arXiv.2404.06781>}.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mcclust
  (package
    (name "r-mcclust")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcclust" version))
       (sha256
        (base32 "1mifgrm2canfcrln9rspqwws1f61vrana5m4lap54lys0kiyrrqq"))))
    (properties `((upstream-name . "mcclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve))
    (home-page "https://cran.r-project.org/package=mcclust")
    (synopsis "Process an MCMC Sample of Clusterings")
    (description
     "This package implements methods for processing a sample of (hard) clusterings,
e.g. the MCMC output of a Bayesian clustering model.  Among them are methods
that find a single best clustering to represent the sample, which are based on
the posterior similarity matrix or a relabelling algorithm.")
    (license license:gpl2+)))

(define-public r-mccf1
  (package
    (name "r-mccf1")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mccf1" version))
       (sha256
        (base32 "023pvjfc1iv7hac3gz5rakg6ljs92j0qfm4givad5iiwv5n4k3zr"))))
    (properties `((upstream-name . "mccf1")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr r-ggplot2))
    (home-page "https://bitbucket.org/hoffmanlab/mccf1/")
    (synopsis
     "Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric and the Best Threshold")
    (description
     "The MCC-F1 analysis is a method to evaluate the performance of binary
classifications.  The MCC-F1 curve is more reliable than the Receiver Operating
Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced
ground truth.  The MCC-F1 analysis also provides the MCC-F1 metric that
integrates classifier performance over varying thresholds, and the best
threshold of binary classification.")
    (license license:gpl2+)))

(define-public r-mccca
  (package
    (name "r-mccca")
    (version "1.1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mccca" version))
       (sha256
        (base32 "1bmcncm53pbmc9dv61rwmp8xk9zf70ihw2gg6d0lmd1sgmf5i4hz"))))
    (properties `((upstream-name . "mccca")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordcloud
                             r-stringr
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-magic
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=mccca")
    (synopsis
     "Visualizing Class Specific Heterogeneous Tendencies in Categorical Data")
    (description
     "Performing multiple-class cluster correspondence analysis(MCCCA).  The main
functions are create.MCCCAdata() to create a list to be applied to MCCCA,
MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result.  Methods
used in the package refers to Mariko Takagishi and Michel van de Velden
(2022)<doi:10.1080/10618600.2022.2035737>.")
    (license license:gpl2+)))

(define-public r-mcca
  (package
    (name "r-mcca")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcca" version))
       (sha256
        (base32 "01nm0plgkik241qhd06v1vs84lf7agc0vkxa27cpr5qlfl4cm4zb"))))
    (properties `((upstream-name . "mcca")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart
                             r-rgl
                             r-proc
                             r-nnet
                             r-mass
                             r-e1071
                             r-caret))
    (home-page "https://github.com/gaoming96/mcca")
    (synopsis "Multi-Category Classification Accuracy")
    (description
     "It contains six common multi-category classification accuracy evaluation
measures.  All of these measures could be found in Li and Ming (2019)
<doi:10.1002/sim.8103>.  Specifically, Hypervolume Under Manifold (HUM),
described in Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>.  Correct
Classification Percentage (CCP), Integrated Discrimination Improvement (IDI),
Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li,
Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>.  Polytomous
Discrimination Index (PDI), described in Van Calster et al. (2012)
<doi:10.1007/s10654-012-9733-3>.  Li et al. (2018)
<doi:10.1177/0962280217692830>.  We described all these above measures and our
mcca package in Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mcboost
  (package
    (name "r-mcboost")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcboost" version))
       (sha256
        (base32 "17zflvafz6w91lxbr8saasxp9bn18bla1jqg53fka2k8fnziz4g8"))))
    (properties `((upstream-name . "mcboost")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart
                             r-rmarkdown
                             r-r6
                             r-mlr3pipelines
                             r-mlr3misc
                             r-mlr3
                             r-glmnet
                             r-data-table
                             r-checkmate
                             r-backports))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mlr-org/mcboost")
    (synopsis "Multi-Calibration Boosting")
    (description
     "This package implements Multi-Calibration Boosting (2018)
<https://proceedings.mlr.press/v80/hebert-johnson18a.html> and Multi-Accuracy
Boosting (2019) <doi:10.48550/@code{arXiv.1805.12317>} for the multi-calibration
of a machine learning model's prediction.  MCBoost updates predictions for
sub-groups in an iterative fashion in order to mitigate biases like poor
calibration or large accuracy differences across subgroups.  Multi-Calibration
works best in scenarios where the underlying data & labels are unbiased, but
resulting models are.  This is often the case, e.g. when an algorithm fits a
majority population while ignoring or under-fitting minority populations.")
    (license license:lgpl3+)))

(define-public r-mcbiopi
  (package
    (name "r-mcbiopi")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcbiopi" version))
       (sha256
        (base32 "1caw2sy15hw2zw0bdynwnsa7hn4rly34hlxp06nwm5lssz8l2597"))))
    (properties `((upstream-name . "mcbiopi")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mcbiopi")
    (synopsis "Matrix Computation Based Identification of Prime Implicants")
    (description
     "Computes the prime implicants or a minimal disjunctive normal form for a logic
expression presented by a truth table or a logic tree.  Has been particularly
developed for logic expressions resulting from a logic regression analysis, i.e.
 logic expressions typically consisting of up to 16 literals, where the prime
implicants are typically composed of a maximum of 4 or 5 literals.")
    (license license:lgpl2.0+)))

(define-public r-mcbette
  (package
    (name "r-mcbette")
    (version "1.15.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcbette" version))
       (sha256
        (base32 "11r2mc1scgvvgffhpmvakj1a9i9q1b0a0rk6m40vdd9m5dkhxnk8"))))
    (properties `((upstream-name . "mcbette")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-txtplot
                             r-testit
                             r-rmpfr
                             r-mauricer
                             r-devtools
                             r-curl
                             r-beautier
                             r-beastier
                             r-babette))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/mcbette/")
    (synopsis "Model Comparison Using 'babette'")
    (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.  mcbette allows to do a Bayesian
model comparison over some site and clock models, using babette
(<https://github.com/ropensci/babette/>).")
    (license license:gpl3)))

(define-public r-mcbackscattering
  (package
    (name "r-mcbackscattering")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCBackscattering" version))
       (sha256
        (base32 "0vvcqjrh8psl480s4q2830rzm2bwy9mmvk9kipp72lzln51qnha6"))))
    (properties `((upstream-name . "MCBackscattering")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MCBackscattering")
    (synopsis "Monte Carlo Simulation for Surface Backscattering")
    (description
     "Monte Carlo simulation is a stochastic method computing trajectories of photons
in media.  Surface backscattering is performing calculations in semi-infinite
media and summarizing photon flux leaving the surface.  This simulation is
modeling the optical measurement of diffuse reflectance using an incident light
beam.  The semi-infinite media is considered to have flat surface.  Media,
typically biological tissue, is described by four optical parameters: absorption
coefficient, scattering coefficient, anisotropy factor, refractive index.  The
media is assumed to be homogeneous.  Computational parameters of the simulation
include: number of photons, radius of incident light beam, lowest photon energy
threshold, intensity profile (halo) radius, spatial resolution of intensity
profile.  You can find more information and validation in the Open Access paper.
 Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.")
    (license license:lgpl2.1)))

(define-public r-mcb
  (package
    (name "r-mcb")
    (version "0.1.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcb" version))
       (sha256
        (base32 "0k986m2vyvfqxw3jmlyvf6r5bq68y92fymy03lffq5lmiqhypixj"))))
    (properties `((upstream-name . "mcb")))
    (build-system r-build-system)
    (propagated-inputs (list r-smoothmest
                             r-reshape2
                             r-ncvreg
                             r-mass
                             r-leaps
                             r-lars
                             r-glmnet
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=mcb")
    (synopsis "Model Confidence Bounds")
    (description
     "When choosing proper variable selection methods, it is important to consider the
uncertainty of a certain method.  The model confidence bound for variable
selection identifies two nested models (upper and lower confidence bound models)
containing the true model at a given confidence level.  A good variable
selection method is the one of which the model confidence bound under a certain
confidence level has the shortest width.  When visualizing the variability of
model selection and comparing different model selection procedures, model
uncertainty curve is a good graphical tool.  A good variable selection method is
the one of whose model uncertainty curve will tend to arch towards the upper
left corner.  This function aims to obtain the model confidence bound and draw
the model uncertainty curve of certain single model selection method under a
coverage rate equal or little higher than user-given confidential level.  About
what model confidence bound is and how it work please see Li,Y., Luo,Y.,
Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable
Selection.  Biometrics, 75:392-403. <DOI:10.1111/biom.13024>.  Besides, flare is
needed only you apply the SQRT or LAD method ('mcb totally has 8 methods).
Although flare has been archived by CRAN, you can still get it in
<https://CRAN.R-project.org/package=flare> and the latest version is useful for
mcb'.")
    (license license:gpl2+)))

(define-public r-mcavariants
  (package
    (name "r-mcavariants")
    (version "2.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCAvariants" version))
       (sha256
        (base32 "0hsm60z1xkjda74f1ab3m5d2l9rv5r88ms7fi0bndrys6dd9fzgl"))))
    (properties `((upstream-name . "MCAvariants")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly r-gridextra r-ggrepel r-ggplot2))
    (home-page "https://www.R-project.org")
    (synopsis "Multiple Correspondence Analysis Variants")
    (description
     "This package provides two variants of multiple correspondence analysis (ca):
multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.")
    (license license:gpl3+)))

(define-public r-mcauchyd
  (package
    (name "r-mcauchyd")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mcauchyd" version))
       (sha256
        (base32 "1n0vgp526wlyf553n2cvxkawzxdvvzqhgar5sqnhiidas7iri3zc"))))
    (properties `((upstream-name . "mcauchyd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-mass r-lifecycle r-data-table))
    (home-page "https://forgemia.inra.fr/imhorphen/mcauchyd")
    (synopsis "Multivariate Cauchy Distribution; Kullback-Leibler Divergence")
    (description
     "Distance between multivariate Cauchy distributions, as presented by N. Bouhlel
and D. Rousseau (2022) <doi:10.3390/e24060838>.  Manipulation of multivariate
Cauchy distributions.")
    (license license:gpl3+)))

(define-public r-mcartest
  (package
    (name "r-mcartest")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MCARtest" version))
       (sha256
        (base32 "0vcw8ysxkv2ajiv44cf6pf8dgn6bmjzs01fsdijs29ky9467alw5"))))
    (properties `((upstream-name . "MCARtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-rcsdp
                             r-rcpp
                             r-rcdd
                             r-pracma
                             r-norm
                             r-misty
                             r-missmethods
                             r-matrix
                             r-mass
                             r-lpsolve
                             r-highs
                             r-gtools
                             r-epi
                             r-copula))
    (home-page "https://cran.r-project.org/package=MCARtest")
    (synopsis "Optimal Nonparametric Testing of Missing Completely at Random")
    (description
     "This package provides functions for carrying out nonparametric hypothesis tests
of the MCAR hypothesis based on the theory of Frechet classes and compatibility.
 Also gives functions for computing halfspace representations of the marginal
polytope and related geometric objects.")
    (license license:expat)))

(define-public r-mc-heterogeneity
  (package
    (name "r-mc-heterogeneity")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mc.heterogeneity" version))
       (sha256
        (base32 "1nsqbc4cqyg6g3s6ish3fbhqygy1kay68smbv0rhivp0x0dv7can"))))
    (properties `((upstream-name . "mc.heterogeneity")))
    (build-system r-build-system)
    (propagated-inputs (list r-metafor r-boot-heterogeneity))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mc.heterogeneity")
    (synopsis "Monte Carlo Based Heterogeneity Test for Meta-Analysis")
    (description
     "This package implements a Monte Carlo 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
likelihood, so installation of R package metafor is required.  This approach
refers to the studies of 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-mbvs
  (package
    (name "r-mbvs")
    (version "1.92")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mBvs" version))
       (sha256
        (base32 "1r6dv8w4y6d4m4yw84cnsvl7f3r5dhj3qrrzk7qb23gzds8pcv84"))))
    (properties `((upstream-name . "mBvs")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=mBvs")
    (synopsis "Bayesian Variable Selection Methods for Multivariate Data")
    (description
     "Bayesian variable selection methods for data with multivariate responses and
multiple covariates.  The package contains implementations of multivariate
Bayesian variable selection methods for continuous data (Lee et al., Biometrics,
2017 <doi:10.1111/biom.12557>) and zero-inflated count data (Lee et al.,
Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).")
    (license license:gpl2+)))

(define-public r-mbsts
  (package
    (name "r-mbsts")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbsts" version))
       (sha256
        (base32 "0qgb74ah1jq6rla2qkid1i75xjp2l9qxkpbbqilhzqg64rkcn9ha"))))
    (properties `((upstream-name . "mbsts")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-pscl
                             r-mcmcpack
                             r-matrixstats
                             r-matrix
                             r-mass
                             r-kfas
                             r-ggplot2
                             r-bbmisc))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mbsts")
    (synopsis "Multivariate Bayesian Structural Time Series")
    (description
     "This package provides tools for data analysis with multivariate Bayesian
structural time series (MBSTS) models.  Specifically, the package provides
facilities for implementing general structural time series models, flexibly
adding on different time series components (trend, season, cycle, and
regression), simulating them, fitting them to multivariate correlated time
series data, conducting feature selection on the regression component.")
    (license license:lgpl2.1)))

(define-public r-mbsp
  (package
    (name "r-mbsp")
    (version "4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBSP" version))
       (sha256
        (base32 "1rfjykm2363m67ycm9vsxnddcsmlqxg5gx8sfg9ma8alwnh0s3fb"))))
    (properties `((upstream-name . "MBSP")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mcmcpack r-gigrvg))
    (home-page "https://cran.r-project.org/package=MBSP")
    (synopsis "Multivariate Bayesian Model with Shrinkage Priors")
    (description
     "Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage
priors (MBSP), using the three parameter beta normal family.  The method is
described in Bai and Ghosh (2018) <doi:10.1016/j.jmva.2018.04.010>.")
    (license license:gpl3)))

(define-public r-mbsgs
  (package
    (name "r-mbsgs")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBSGS" version))
       (sha256
        (base32 "1h5vhmpgrqza12j7ivhwgd5wyb7kkb8mjbxl5w756vyid8g03flz"))))
    (properties `((upstream-name . "MBSGS")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-mnormt r-mgcv r-mcmcpack r-mass))
    (home-page "https://cran.r-project.org/package=MBSGS")
    (synopsis
     "Multivariate Bayesian Sparse Group Selection with Spike and Slab")
    (description
     "An implementation of a Bayesian sparse group model using spike and slab priors
in a regression context.  It is designed for regression with a multivariate
response variable, but also provides an implementation for univariate response.")
    (license license:gpl2+)))

(define-public r-mbrglm
  (package
    (name "r-mbrglm")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbrglm" version))
       (sha256
        (base32 "0yxq1xk8qy5hpiqqldyrs78lp4ggdp5lj2lmh8rqq1xvsfr6nrh4"))))
    (properties `((upstream-name . "mbrglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv r-enrichwith))
    (home-page "https://cran.r-project.org/package=mbrglm")
    (synopsis "Median Bias Reduction in Binomial-Response GLMs")
    (description
     "Fit generalized linear models with binomial responses using a median modified
score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to
median bias reduction.  This method respects equivariance under
reparameterizations for each parameter component and also solves the infinite
estimates problem (data separation).")
    (license license:gpl2)))

(define-public r-mbres
  (package
    (name "r-mbres")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbRes" version))
       (sha256
        (base32 "19my8lwyn7i0fcsffz0w334apxrffs6sph1svh5mwa9syyvspxaz"))))
    (properties `((upstream-name . "mbRes")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-data-table
                             r-cowplot))
    (home-page "https://cran.r-project.org/package=mbRes")
    (synopsis "Exploration of Multiple Biomarker Responses using Effect Size")
    (description
     "Summarize multiple biomarker responses of aquatic organisms to contaminants
using Cliffâs delta, as described in Pham & Sokolova (2023)
<doi:10.1002/ieam.4676>.")
    (license license:gpl3)))

(define-public r-mbreaks
  (package
    (name "r-mbreaks")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbreaks" version))
       (sha256
        (base32 "1vnrn7f19j52jn707ps9mjz5p0rgcm7mmwbq585hf4c4mbyglrbz"))))
    (properties `((upstream-name . "mbreaks")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/RoDivinity/mbreaks")
    (synopsis
     "Estimation and Inference for Structural Breaks in Linear Regression Models")
    (description
     "This package provides functions provide comprehensive treatments for estimating,
inferring, testing and model selecting in linear regression models with
structural breaks.  The tests, estimation methods, inference and information
criteria implemented are discussed in Bai and Perron (1998) \"Estimating and
Testing Linear Models with Multiple Structural Changes\" <doi:10.2307/2998540>.")
    (license license:expat)))

(define-public r-mbrdr
  (package
    (name "r-mbrdr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbrdr" version))
       (sha256
        (base32 "1ck0l2zp10xlg2d99glb76fxhc1r21aknk8x8452v25gpaqvir4c"))))
    (properties `((upstream-name . "mbrdr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mbrdr")
    (synopsis "Model-Based Response Dimension Reduction")
    (description
     "This package provides functions for model-based response dimension reduction.
Usual dimension reduction methods in multivariate regression focus on the
reduction of predictors, not responses.  The response dimension reduction is
theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>.
Later, three model-based response dimension reduction approaches are proposed in
Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019)
<doi:10.1016/j.jkss.2019.02.001>.  The method by Yoo and Cook (2008) is based on
non-parametric ordinary least squares, but the model-based approaches are done
through maximum likelihood estimation.  For two model-based response dimension
reduction methods called principal fitted response reduction and unstructured
principal fitted response reduction, chi-squared tests are provided for
determining the dimension of the response subspace.")
    (license license:gpl2+)))

(define-public r-mbr
  (package
    (name "r-mbr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbr" version))
       (sha256
        (base32 "08534d0ssj5zli7880a2wzl46ydf87gn5xlv6xp0iph71wlpc52y"))))
    (properties `((upstream-name . "mbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-matrix r-mass r-dplr r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ntthung/mbr")
    (synopsis "Mass Balance Reconstruction")
    (description
     "Mass-balance-adjusted Regression algorithm for streamflow reconstruction at
sub-annual resolution (e.g., seasonal or monthly).  The algorithm implements a
penalty term to minimize the differences between the total sub-annual flows and
the annual flow.  The method is described in Nguyen et al (2020)
<DOI:10.1002/essoar.10504791.1>.")
    (license license:gpl2+)))

(define-public r-mbnmatime
  (package
    (name "r-mbnmatime")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBNMAtime" version))
       (sha256
        (base32 "1ny438dy62zrafyf7nng2hq5v311l1zc5xd39452fgq6ij0rnrni"))))
    (properties `((upstream-name . "MBNMAtime")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags
                             r-reshape2
                             r-rdpack
                             r-r2jags
                             r-magrittr
                             r-knitr
                             r-gridextra
                             r-dplyr
                             r-checkmate))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://hugaped.github.io/MBNMAtime/")
    (synopsis
     "Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models")
    (description
     "Fits Bayesian time-course models for model-based network meta-analysis (MBNMA)
that allows inclusion of multiple time-points from studies.  Repeated measures
over time are accounted for within studies by applying different time-course
functions, following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>.
 The method allows synthesis of studies with multiple follow-up measurements
that can account for time-course for a single or multiple treatment comparisons.
 Several general time-course functions are provided; others may be added by the
user.  Various characteristics can be flexibly added to the models, such as
correlation between time points and shared class effects.  The consistency of
direct and indirect evidence in the network can be assessed using unrelated mean
effects models and/or by node-splitting.")
    (license license:gpl3)))

(define-public r-mbnmadose
  (package
    (name "r-mbnmadose")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBNMAdose" version))
       (sha256
        (base32 "1dqh7z6wpnryl9djfsacbdm4b4my08kh4l63vd31xs742sywv6i0"))))
    (properties `((upstream-name . "MBNMAdose")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-scales
                             r-rjags
                             r-reshape2
                             r-rdpack
                             r-r2jags
                             r-magrittr
                             r-igraph
                             r-dplyr
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://hugaped.github.io/MBNMAdose/")
    (synopsis "Dose-Response MBNMA Models")
    (description
     "Fits Bayesian dose-response model-based network meta-analysis (MBNMA) that
incorporate multiple doses within an agent by modelling different dose-response
functions, as described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>.  By
modelling dose-response relationships this can connect networks of evidence that
might otherwise be disconnected, and can improve precision on treatment
estimates.  Several common dose-response functions are provided; others may be
added by the user.  Various characteristics and assumptions can be flexibly
added to the models, such as shared class effects.  The consistency of direct
and indirect evidence in the network can be assessed using unrelated mean
effects models and/or by node-splitting at the treatment level.")
    (license license:gpl3)))

(define-public r-mbmixture
  (package
    (name "r-mbmixture")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbmixture" version))
       (sha256
        (base32 "0g74jzhdf8grrp7pipj1m6hvyv4akycabm0ingy505a5a546ddzg"))))
    (properties `((upstream-name . "mbmixture")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kbroman/mbmixture")
    (synopsis "Microbiome Mixture Analysis")
    (description
     "Evaluate whether a microbiome sample is a mixture of two samples, by fitting a
model for the number of read counts as a function of single nucleotide
polymorphism (SNP) allele and the genotypes of two potential source samples.
Lobo et al. (2021) <doi:10.1093/g3journal/jkab308>.")
    (license license:expat)))

(define-public r-mbmethpred
  (package
    (name "r-mbmethpred")
    (version "0.1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBMethPred" version))
       (sha256
        (base32 "0qpflcydkf2k8a7kqklvp7mskppviqkffkr98rfsklk8iqf8x9c9"))))
    (properties `((upstream-name . "MBMethPred")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-tensorflow
                             r-stringr
                             r-snftool
                             r-rtsne
                             r-rgl
                             r-reticulate
                             r-reshape2
                             r-readr
                             r-randomforest
                             r-proc
                             r-mass
                             r-keras
                             r-ggplot2
                             r-e1071
                             r-dplyr
                             r-class
                             r-catools
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sharifrahmanie/MBMethPred")
    (synopsis "Medulloblastoma Subgroups Prediction")
    (description
     "Utilizing a combination of machine learning models (Random Forest, Naive Bayes,
K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and
Linear Discriminant Analysis) and a deep Artificial Neural Network model,
MB@code{MethPred} can predict medulloblastoma subgroups, including wingless
(WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta
values.  See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and
Modhukur V (2023), MB@code{MethPred}: a computational framework for the accurate
classification of childhood medulloblastoma subgroups using data integration and
AI-based approaches.  Front.  Genet.  14:1233657. <doi:
10.3389/fgene.2023.1233657> for more details.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mbmca
  (package
    (name "r-mbmca")
    (version "1.0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBmca" version))
       (sha256
        (base32 "08ls1rc440jjmbwm6nx09xyr8nb8q6wwid0dyvb97fv4l85qwn19"))))
    (properties `((upstream-name . "MBmca")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-chippcr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PCRuniversum/MBmca/")
    (synopsis "Nucleic Acid Melting Curve Analysis")
    (description
     "Lightweight utilities for nucleic acid melting curve analysis are important in
life sciences and diagnostics.  This software can be used for the analysis and
presentation of melting curve data from microbead-based assays (surface melting
curve analysis) and reactions in solution (e.g., quantitative PCR (@code{qPCR}),
real-time isothermal Amplification).  Further information are described in
detail in two publications in The R Journal [
<https://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf>;
<https://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf>].")
    (license license:gpl2+)))

(define-public r-mbir
  (package
    (name "r-mbir")
    (version "1.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbir" version))
       (sha256
        (base32 "10sd5gk2k94cmi49nbpywqvh9bxj9q3psa3d00zmw1a7d6c0jjpd"))))
    (properties `((upstream-name . "mbir")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-effsize))
    (native-inputs (list r-knitr))
    (home-page "http://mbir-project.us/")
    (synopsis "Magnitude-Based Inferences")
    (description
     "Allows practitioners and researchers a wholesale approach for deriving
magnitude-based inferences from raw data.  A major goal of mbir is to
programmatically detect appropriate statistical tests to run in lieu of relying
on practitioners to determine correct stepwise procedures independently.")
    (license license:gpl2)))

(define-public r-mbhdesign
  (package
    (name "r-mbhdesign")
    (version "2.3.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBHdesign" version))
       (sha256
        (base32 "03v6809drwjwr9xk19pr4n5wnqy5kh3vizfk0mlml3sn40fg3kxj"))))
    (properties `((upstream-name . "MBHdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-randtoolbox
                             r-mvtnorm
                             r-mgcv
                             r-geometry
                             r-class))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MBHdesign")
    (synopsis "Spatial Designs for Ecological and Environmental Surveys")
    (description
     "This package provides spatially survey balanced designs using the quasi-random
number method described Robinson et al. (2013) <doi:10.1111/biom.12059> and
adjusted in Robinson et al. (2017) <doi:10.1016/j.spl.2017.05.004>.  Designs
using MBHdesign can: 1) accommodate, without substantial detrimental effects on
spatial balance, legacy sites (Foster et al., 2017
<doi:10.1111/2041-210X.12782>); 2) be based on points or transects (foster et
al.  2020 <doi:10.1111/2041-210X.13321> and produce clustered samples (Foster et
al. (in press).  Additional information about the package use itself is given in
Foster (2021) <doi:10.1111/2041-210X.13535>.")
    (license license:gpl2+)))

(define-public r-mbest
  (package
    (name "r-mbest")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbest" version))
       (sha256
        (base32 "1x0f7y5hj6a35wq1xn6g7jyjn9c4zryahwlf07qrypgrcnj2m8vx"))))
    (properties `((upstream-name . "mbest")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme
                             r-logging
                             r-lme4
                             r-foreach
                             r-bigmemory
                             r-abind))
    (home-page "https://github.com/patperry/r-mbest")
    (synopsis "Moment-Based Estimation for Hierarchical Models")
    (description
     "Fast moment-based hierarchical model fitting.  Implements methods from the
papers \"Fast Moment-Based Estimation for Hierarchical Models,\" by Perry (2017)
and \"Fitting a Deeply Nested Hierarchical Model to a Large Book Review Dataset
Using a Moment-Based Estimator,\" by Zhang, Schmaus, and Perry (2018).")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mbend
  (package
    (name "r-mbend")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbend" version))
       (sha256
        (base32 "0g7hqjdhaj99rd2jqfb6x7k7gsfcvhdibai3093qcvhv6grb3v7m"))))
    (properties `((upstream-name . "mbend")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nilforooshan/mbend")
    (synopsis "Matrix Bending")
    (description
     "Bending non-positive-definite (symmetric) matrices to positive-definite, using
weighted and unweighted methods.  Jorjani, H., et al. (2003)
<doi:10.3168/jds.S0022-0302(03)73646-7>.  Schaeffer, L. R. (2014)
<http://animalbiosciences.uoguelph.ca/~lrs/ELARES/PDforce.pdf>.")
    (license license:gpl3)))

(define-public r-mbcbook
  (package
    (name "r-mbcbook")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBCbook" version))
       (sha256
        (base32 "0av7mswr1l02jkjpmh780phjgwjazqzyl07hkb282ba2h95dwlwx"))))
    (properties `((upstream-name . "MBCbook")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmixmod r-mvtnorm r-mclust r-mass))
    (home-page "https://github.com/cbouveyron/MBCbook")
    (synopsis
     "Companion Package for the Book \"Model-Based Clustering and Classification for Data Science\"")
    (description
     "The companion package provides all original data sets and functions that are
used in the book \"Model-Based Clustering and Classification for Data Science\" by
Charles Bouveyron, Gilles Celeux, T. Brendan Murphy and Adrian E. Raftery (2019,
ISBN:9781108644181).")
    (license license:gpl2+)))

(define-public r-mbc
  (package
    (name "r-mbc")
    (version "0.10-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBC" version))
       (sha256
        (base32 "1680lnzga21qk5yddrciiaifxkkb9kk744m9fbj0hg5xw6v9dxic"))))
    (properties `((upstream-name . "MBC")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-fnn r-energy))
    (home-page "https://cran.r-project.org/package=MBC")
    (synopsis "Multivariate Bias Correction of Climate Model Outputs")
    (description
     "Calibrate and apply multivariate bias correction algorithms for climate model
simulations of multiple climate variables.  Three methods described by Cannon
(2016) <doi:10.1175/JCLI-D-15-0679.1> and Cannon (2018)
<doi:10.1007/s00382-017-3580-6> are implemented â (i) MBC Pearson correlation
(MBCp), (ii) MBC rank correlation (MBCr), and (iii) MBC N-dimensional PDF
transform (MBCn) â as is the Rank Resampling for Distributions and Dependences
(R2D2) method.")
    (license license:gpl2)))

(define-public r-mbbefdlite
  (package
    (name "r-mbbefdlite")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBBEFDLite" version))
       (sha256
        (base32 "1cczc45gw0ksisagjhyjf7zck00qqpdpa02w2wkbdx8bhvllkl62"))))
    (properties `((upstream-name . "MBBEFDLite")))
    (build-system r-build-system)
    (home-page "https://github.com/aadler/MBBEFDLite")
    (synopsis
     "Statistical Functions for the Maxwell-Boltzmann-Bose-Einstein-Fermi-Dirac (MBBEFD) Family of Distributions")
    (description
     "This package provides probability mass, distribution, quantile, random variate
generation, and method-of-moments parameter fitting for the MBBEFD family of
distributions used in insurance modeling as described in Bernegger (1997)
<doi:10.2143/AST.27.1.563208> without any external dependencies.")
    (license (license:fsdg-compatible "MPL-2.0"))))

(define-public r-mbbefd
  (package
    (name "r-mbbefd")
    (version "0.8.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbbefd" version))
       (sha256
        (base32 "107mld5bq62zj7rar0g7402xiiy6rx2xwg4mhna0pdjq31plb27b"))))
    (properties `((upstream-name . "mbbefd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-mass
                             r-gsl
                             r-fitdistrplus
                             r-alabama
                             r-actuar))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/spedygiorgio/mbbefd")
    (synopsis
     "Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and Destruction Rate Modelling")
    (description
     "Distributions that are typically used for exposure rating in general insurance,
in particular to price reinsurance contracts.  The vignette shows code snippets
to fit the distribution to empirical data.  See, e.g., Bernegger (1997)
<doi:10.2143/AST.27.1.563208> freely available on-line.")
    (license license:gpl2)))

(define-public r-mbbe
  (package
    (name "r-mbbe")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mbbe" version))
       (sha256
        (base32 "0m6nn4vv7lxr5y3bbpqjab8ahal9qbl7v6fxr848s1ida3zvkkxm"))))
    (properties `((upstream-name . "mbbe")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tictoc
                             r-stringr
                             r-ps
                             r-processx
                             r-pknca
                             r-nlme
                             r-magrittr
                             r-jsonlite
                             r-ggplot2
                             r-future
                             r-furrr
                             r-emmeans
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/certara/mbbe")
    (synopsis "Model Based Bio-Equivalence")
    (description
     "Uses several Nonlinear Mixed effect (NONMEM) models (as NONMEM control files) to
perform bootstrap model averaging and Monte Carlo Simulation for Model Based
Bio-Equivalence (MBBE).  Power is returned as the fraction of the simulations
with successful bioequivalence (BE) test, for maximum concentration (Cmax), Area
under the curve to the last observed value (AUClast) and Area under the curve
extrapolate to infinity (AUCinf).  See Hooker, A. (2020) Improved bioequivalence
assessment through model-informed and model-based strategies
<https://www.fda.gov/media/138035/download>.")
    (license license:lgpl3)))

(define-public r-mbanalysis
  (package
    (name "r-mbanalysis")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBAnalysis" version))
       (sha256
        (base32 "1b1s7s1qa82y8054f3mdpq7s13lk7dks900li5k0gr7sqfqdg1fz"))))
    (properties `((upstream-name . "MBAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggrepel r-ggplot2))
    (home-page "https://cran.r-project.org/package=MBAnalysis")
    (synopsis "Multiblock Exploratory and Predictive Data Analysis")
    (description
     "Exploratory and predictive methods for the analysis of several blocks of
variables measured on the same individuals.")
    (license license:gpl3+)))

(define-public r-mba
  (package
    (name "r-mba")
    (version "0.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MBA" version))
       (sha256
        (base32 "14jfy9wqm4k2ljn2rz4lm1zzzkjm7nn4g92lrb1a7gm7gysa3dd5"))))
    (properties `((upstream-name . "MBA")))
    (build-system r-build-system)
    (propagated-inputs (list r-bh))
    (home-page "https://cran.r-project.org/package=MBA")
    (synopsis "Multilevel B-Spline Approximation")
    (description
     "This package provides functions to interpolate irregularly and regularly spaced
data using Multilevel B-spline Approximation (MBA).  Functions call portions of
the SINTEF Multilevel B-spline Library written by Ãyvind Hjelle which
implements methods developed by Lee, Wolberg and Shin (1997;
<doi:10.1109/2945.620490>).")
    (license license:gpl2+)))

(define-public r-mb
  (package
    (name "r-mb")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MB" version))
       (sha256
        (base32 "15ynrx2w5h5m18ws63syp29n8qc9dfk8q0riidskynijz3p7wz7w"))))
    (properties `((upstream-name . "MB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MB")
    (synopsis "The Use of Marginal Distributions in Conditional Forecasting")
    (description
     "This package provides a new way to predict time series using the marginal
distribution table in the absence of the significance of traditional models.")
    (license license:gpl3)))

(define-public r-mazing
  (package
    (name "r-mazing")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mazing" version))
       (sha256
        (base32 "1nabv5hxb58d56r4z8v4glhwiz5lf8j9gdlwncxazm6j2fcjh26s"))))
    (properties `((upstream-name . "mazing")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mazing")
    (synopsis "Utilities for Making and Plotting Mazes")
    (description
     "Functionality for generating and plotting random mazes.  The mazes are based on
matrices, so can only consist of vertical and horizontal lines along a regular
grid.  But there is no need to use every possible space, so they can take on
many different shapes.")
    (license license:expat)))

(define-public r-mazeinda
  (package
    (name "r-mazeinda")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mazeinda" version))
       (sha256
        (base32 "0c32vh75d4wjpyxrqwyf16fw6jbmig037py3c1417hx3621782kh"))))
    (properties `((upstream-name . "mazeinda")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://cran.r-project.org/package=mazeinda")
    (synopsis "Monotonic Association on Zero-Inflated Data")
    (description
     "This package provides methods for calculating and testing the significance of
pairwise monotonic association from and based on the work of Pimentel (2009)
<doi:10.4135/9781412985291.n2>.  Computation of association of vectors from one
or multiple sets can be performed in parallel thanks to the packages foreach and
@code{doMC}'.")
    (license license:gpl3)))

(define-public r-mazegen
  (package
    (name "r-mazegen")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mazeGen" version))
       (sha256
        (base32 "192xygg3l4rpqp49sgd5hpp4h3f8wjhyldn0l8abxhsks7jd2kfb"))))
    (properties `((upstream-name . "mazeGen")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph))
    (home-page "https://cran.r-project.org/package=mazeGen")
    (synopsis "Elithorn Maze Generator")
    (description
     "This package provides a maze generator that creates the Elithorn Maze (HTML
file) and the functions to calculate the associated maze parameters (i.e.
Difficulty and Ability).")
    (license license:gpl3)))

(define-public r-mazealls
  (package
    (name "r-mazealls")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mazealls" version))
       (sha256
        (base32 "14j6syw1zkgs4cnmxzqpm6klmgvck2gg97wkzmy9al6n0biy8gmb"))))
    (properties `((upstream-name . "mazealls")))
    (build-system r-build-system)
    (propagated-inputs (list r-turtlegraphics))
    (home-page "https://github.com/shabbychef/mazealls")
    (synopsis "Generate Recursive Mazes")
    (description
     "Supports the generation of parallelogram, equilateral triangle, regular hexagon,
isosceles trapezoid, Koch snowflake, hexaflake', Sierpinski triangle, Sierpinski
carpet and Sierpinski trapezoid mazes via @code{TurtleGraphics}'.  Mazes are
generated by the recursive method: the domain is divided into sub-domains in
which mazes are generated, then dividing lines with holes are drawn between
them, see J. Buck, Recursive Division,
<http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.")
    (license license:lgpl3)))

(define-public r-maze
  (package
    (name "r-maze")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAZE" version))
       (sha256
        (base32 "01vxglgfn82m8avsq6s144gmwclw08arzfrb8lwlcynr62ifzaim"))))
    (properties `((upstream-name . "MAZE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-pracma
                             r-numderiv
                             r-mass
                             r-foreach
                             r-flexmix
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/meilinjiang/MAZE")
    (synopsis "Mediation Analysis for Zero-Inflated Mediators")
    (description
     "This package provides a novel mediation analysis approach to address
zero-inflated mediators containing true zeros and false zeros.  See Jiang et al
(2023) \"A Novel Causal Mediation Analysis Approach for Zero-Inflated Mediators\"
<@code{arXiv:2301.10064>} for more details.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-mazamatimeseries
  (package
    (name "r-mazamatimeseries")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MazamaTimeSeries" version))
       (sha256
        (base32 "10w8nffv9rls32bj3rfa48l6nyly0238ywqbqwfkvr16i7g63dsb"))))
    (properties `((upstream-name . "MazamaTimeSeries")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-mazamarollutils
                             r-mazamacoreutils
                             r-magrittr
                             r-lubridate
                             r-geodist
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MazamaScience/MazamaTimeSeries")
    (synopsis "Core Functionality for Environmental Time Series")
    (description
     "Utility functions for working with environmental time series data from known
locations.  The compact data model is structured as a list with two dataframes.
A meta dataframe contains spatial and measuring device metadata associated with
deployments at known locations.  A data dataframe contains a datetime column
followed by columns of measurements associated with each \"device-deployment\".
Ephemerides calculations are based on code originally found in NOAA's \"Solar
Calculator\" <https://gml.noaa.gov/grad/solcalc/>.")
    (license license:gpl3)))

(define-public r-mazamaspatialutils
  (package
    (name "r-mazamaspatialutils")
    (version "0.8.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MazamaSpatialUtils" version))
       (sha256
        (base32 "18dpnal2rkj2lkrlnr6wn4rc4n2xb7w6g6kywppc9d1qd29b0njk"))))
    (properties `((upstream-name . "MazamaSpatialUtils")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sf
                             r-rmapshaper
                             r-rlang
                             r-mazamacoreutils
                             r-magrittr
                             r-dplyr
                             r-countrycode))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MazamaScience/MazamaSpatialUtils")
    (synopsis "Spatial Data Download and Utility Functions")
    (description
     "This package provides a suite of conversion functions to create internally
standardized spatial polygons data frames.  Utility functions use these data
sets to return values such as country, state, time zone, watershed, etc.
associated with a set of longitude/latitude pairs. (They also make cool maps.).")
    (license license:gpl2)))

(define-public r-mazamaspatialplots
  (package
    (name "r-mazamaspatialplots")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MazamaSpatialPlots" version))
       (sha256
        (base32 "1i8gqywl2w7zjl1m5qw8vmy7wn44cp8fkiw4m2iwvf4s9q43xjnc"))))
    (properties `((upstream-name . "MazamaSpatialPlots")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmap
                             r-sf
                             r-rlang
                             r-mazamaspatialutils
                             r-mazamacoreutils
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MazamaScience/MazamaSpatialPlots")
    (synopsis "Thematic Plots for Mazama Spatial Datasets")
    (description
     "This package provides a suite of convenience functions for generating US state
and county thematic maps using datasets from the @code{MazamaSpatialUtils}
package.")
    (license license:gpl3)))

(define-public r-mazamarollutils
  (package
    (name "r-mazamarollutils")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MazamaRollUtils" version))
       (sha256
        (base32 "1433xy64n3wpxg9d0jlwdfp8hlvmq0ndg3fv6kfxq3pasm600jgc"))))
    (properties `((upstream-name . "MazamaRollUtils")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MazamaScience/MazamaRollUtils")
    (synopsis "Efficient Rolling Functions")
    (description
     "This package provides a suite of compiled functions calculating rolling mins,
means, maxes and other statistics.  This package is designed to meet the needs
of data processing systems for environmental time series.")
    (license license:gpl3)))

(define-public r-mazamalocationutils
  (package
    (name "r-mazamalocationutils")
    (version "0.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MazamaLocationUtils" version))
       (sha256
        (base32 "1xyfashhapzw7j5svzgv6p7ngxy09zpsp5da9x51w4r3z5f4id7w"))))
    (properties `((upstream-name . "MazamaLocationUtils")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidygeocoder
                             r-stringr
                             r-rlang
                             r-readr
                             r-mazamaspatialutils
                             r-mazamacoreutils
                             r-magrittr
                             r-lubridate
                             r-leaflet
                             r-jsonlite
                             r-httr
                             r-geodist
                             r-dplyr
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MazamaScience/MazamaLocationUtils")
    (synopsis "Manage Spatial Metadata for Known Locations")
    (description
     "Utility functions for discovering and managing metadata associated with
spatially unique \"known locations\".  Applications include all fields of
environmental monitoring (e.g. air and water quality) where data are collected
at stationary sites.")
    (license license:gpl3)))

(define-public r-mazamacoreutils
  (package
    (name "r-mazamacoreutils")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MazamaCoreUtils" version))
       (sha256
        (base32 "1j698zjvpzynh1zsb39fs4d2vmgn0fsiaqjn1rx9vvppl5dgagar"))))
    (properties `((upstream-name . "MazamaCoreUtils")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-stringr
                             r-rvest
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-geohashtools
                             r-futile-logger
                             r-dplyr
                             r-digest
                             r-devtools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MazamaScience/MazamaCoreUtils")
    (synopsis "Utility Functions for Production R Code")
    (description
     "This package provides a suite of utility functions providing functionality
commonly needed for production level projects such as logging, error handling,
cache management and date-time parsing.  Functions for date-time parsing and
formatting require that time zones be specified explicitly, avoiding a common
source of error when working with environmental time series.")
    (license license:gpl3)))

(define-public r-maybe
  (package
    (name "r-maybe")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maybe" version))
       (sha256
        (base32 "11ajilchzl433790i90hx9qja3vixzzgx5k35pf9xryba6xbdchl"))))
    (properties `((upstream-name . "maybe")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (home-page "https://github.com/armcn/maybe")
    (synopsis "The Maybe Monad")
    (description
     "The maybe type represents the possibility of some value or nothing.  It is often
used instead of throwing an error or returning `NULL`.  The advantage of using a
maybe type over `NULL` is that it is both composable and requires the developer
to explicitly acknowledge the potential absence of a value, helping to avoid the
existence of unexpected behaviour.")
    (license license:expat)))

(define-public r-maxstablepca
  (package
    (name "r-maxstablepca")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maxstablePCA" version))
       (sha256
        (base32 "02lxvkg9ia25cvwppf8qf84mwzjkfc8w9acc99wnisddrdq7l1h2"))))
    (properties `((upstream-name . "maxstablePCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-nloptr))
    (home-page "https://cran.r-project.org/package=maxstablePCA")
    (synopsis
     "Apply a PCA Like Procedure Suited for Multivariate Extreme Value Distributions")
    (description
     "Dimension reduction for multivariate data of extreme events with a PCA like
procedure as described in Reinbott, JanÃen, (2024),
<doi:10.48550/@code{arXiv.2408.10650>}.  Tools for necessary transformations of
the data are provided.")
    (license license:expat)))

(define-public r-maxskew
  (package
    (name "r-maxskew")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MaxSkew" version))
       (sha256
        (base32 "0x5x0jpvhp189jnmgh6d1h3ya0dicj5qfcrgr2fhp1v8jjhl125m"))))
    (properties `((upstream-name . "MaxSkew")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MaxSkew")
    (synopsis "Orthogonal Data Projections with Maximal Skewness")
    (description
     "It finds Orthogonal Data Projections with Maximal Skewness.  The first data
projection in the output is the most skewed among all linear data projections.
The second data projection in the output is the most skewed among all data
projections orthogonal to the first one, and so on.")
    (license license:gpl2)))

(define-public r-maxpro
  (package
    (name "r-maxpro")
    (version "4.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MaxPro" version))
       (sha256
        (base32 "19a2wp93kx1wkbzd5bvljbm1rr96gr14w4fb0ppbgr2y2a4g8i1v"))))
    (properties `((upstream-name . "MaxPro")))
    (build-system r-build-system)
    (propagated-inputs (list r-nloptr))
    (home-page "https://cran.r-project.org/package=MaxPro")
    (synopsis "Maximum Projection Designs")
    (description
     "Generate maximum projection (@code{MaxPro}) designs for quantitative and/or
qualitative factors.  Details of the @code{MaxPro} criterion can be found in:
(1) Joseph, Gul, and Ba. (2015) \"Maximum Projection Designs for Computer
Experiments\", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018)
\"Designing Computer Experiments with Multiple Types of Factors: The
@code{MaxPro} Approach\", Journal of Quality Technology, to appear.")
    (license license:lgpl2.1)))

(define-public r-maxnet
  (package
    (name "r-maxnet")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maxnet" version))
       (sha256
        (base32 "05l24dksdgb43dpi4py4qkh8j9ynrnsglydy3gph1b61ygnfa8gx"))))
    (properties `((upstream-name . "maxnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet))
    (home-page "https://github.com/mrmaxent/maxnet")
    (synopsis "Fitting 'Maxent' Species Distribution Models with 'glmnet'")
    (description
     "Procedures to fit species distributions models from occurrence records and
environmental variables, using glmnet for model fitting.  Model structure is the
same as for the Maxent Java package, version 3.4.0, with the same feature types
and regularization options.  See the Maxent website
<http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.")
    (license license:expat)))

(define-public r-maxmc
  (package
    (name "r-maxmc")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MaxMC" version))
       (sha256
        (base32 "04zvdnm2j3dfxh03d771vxs5q0qvizx69fqxghnx1kzv64fv5pny"))))
    (properties `((upstream-name . "MaxMC")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-pso r-nmof r-gensa r-ga))
    (home-page "https://github.com/julienneves/MaxMC")
    (synopsis "Maximized Monte Carlo")
    (description
     "An implementation of the Monte Carlo techniques described in details by Dufour
(2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007)
<doi:10.1002/9780470996249.ch24>.  The two main features available are the Monte
Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized
Monte Carlo, mmc(), for statistics with nuisance parameters.")
    (license license:gpl3+)))

(define-public r-maxmatching
  (package
    (name "r-maxmatching")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maxmatching" version))
       (sha256
        (base32 "1xbwrhmr8gzvlcprib7nzvqrcd355bhx7wkqxrvfk4g7dvskf9ka"))))
    (properties `((upstream-name . "maxmatching")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph))
    (home-page "https://cran.r-project.org/package=maxmatching")
    (synopsis "Maximum Matching for General Weighted Graph")
    (description
     "Computes the maximum matching for unweighted graph and maximum matching for
(un)weighted bipartite graph efficiently.")
    (license license:cc0)))

(define-public r-maxlike
  (package
    (name "r-maxlike")
    (version "0.1-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maxlike" version))
       (sha256
        (base32 "1s81vqx10nckr2g9c1h8m0a2xj0k3pyw4970zshhjmbdljs7ppkd"))))
    (properties `((upstream-name . "maxlike")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster))
    (home-page "https://cran.r-project.org/package=maxlike")
    (synopsis
     "Model Species Distributions by Estimating the Probability of Occurrence Using Presence-Only Data")
    (description
     "This package provides a likelihood-based approach to modeling species
distributions using presence-only data.  In contrast to the popular software
program MAXENT, this approach yields estimates of the probability of occurrence,
which is a natural descriptor of a species distribution.")
    (license license:gpl3+)))

(define-public r-maximininfer
  (package
    (name "r-maximininfer")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MaximinInfer" version))
       (sha256
        (base32 "0l4sglb3f7f1na6dbb4rd73sb4kqzlz78sj09f84rbqghjrzbxl0"))))
    (properties `((upstream-name . "MaximinInfer")))
    (build-system r-build-system)
    (propagated-inputs (list r-sihr r-mass r-intervals r-glmnet r-cvxr))
    (home-page "https://cran.r-project.org/package=MaximinInfer")
    (synopsis "Inference for Maximin Effects in High-Dimensional Settings")
    (description
     "Implementation of the sampling and aggregation method for the covariate shift
maximin effect, which was proposed in <@code{arXiv:2011.07568>}.  It constructs
the confidence interval for any linear combination of the high-dimensional
maximin effect.")
    (license license:gpl3)))

(define-public r-maximin
  (package
    (name "r-maximin")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maximin" version))
       (sha256
        (base32 "1nk2qdj9kap16fakwzvswzhc3id7p06xhw48j8nnz5ffg79ai604"))))
    (properties `((upstream-name . "maximin")))
    (build-system r-build-system)
    (propagated-inputs (list r-plgp))
    (home-page "https://cran.r-project.org/package=maximin")
    (synopsis "Space-Filling Design under Maximin Distance")
    (description
     "Constructs a space-filling design under the criterion of maximum-minimum
distance.  Both discrete and continuous searches are provided.")
    (license license:lgpl2.0+)))

(define-public r-maxentvariableselection
  (package
    (name "r-maxentvariableselection")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MaxentVariableSelection" version))
       (sha256
        (base32 "1cmxfdkm5k85b4ivlfy5521hkfj0gq2pb1qlxxklh0fprw87kp9c"))))
    (properties `((upstream-name . "MaxentVariableSelection")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MaxentVariableSelection")
    (synopsis
     "Selecting the Best Set of Relevant Environmental Variables along with the Optimal Regularization Multiplier for Maxent Niche Modeling")
    (description
     "Complex niche models show low performance in identifying the most important
range-limiting environmental variables and in transferring habitat suitability
to novel environmental conditions (Warren and Seifert, 2011
<DOI:10.1890/10-1171.1>; Warren et al., 2014 <DOI:10.1111/ddi.12160>).  This
package helps to identify the most important set of uncorrelated variables and
to fine-tune Maxent's regularization multiplier.  In combination, this allows to
constrain complexity and increase performance of Maxent niche models (assessed
by information criteria, such as AICc (Akaike, 1974
<DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver operating
characteristic (AUC) (Fielding and Bell, 1997 <DOI:10.1017/S0376892997000088>).
Users of this package should be familiar with Maxent niche modelling.")
    (license license:gpl2+)))

(define-public r-maxent-ot
  (package
    (name "r-maxent-ot")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maxent.ot" version))
       (sha256
        (base32 "1pqbrc1x98hf363rmlfyxlbdxq67p05nz6v3b5rkalym9hrzfybg"))))
    (properties `((upstream-name . "maxent.ot")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/connormayer/maxent.ot")
    (synopsis
     "Perform Phonological Analyses using Maximum Entropy Optimality Theory")
    (description
     "Fit Maximum Entropy Optimality Theory models to data sets, generate the
predictions made by such models for novel data, and compare the fit of different
models using a variety of metrics.  The package is described in Mayer, C., Tan,
A., Zuraw, K. (in press)
<https://sites.socsci.uci.edu/~cjmayer/papers/cmayer_et_al_maxent_ot_accepted.pdf>.")
    (license license:gpl3+)))

(define-public r-maxcombo
  (package
    (name "r-maxcombo")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maxcombo" version))
       (sha256
        (base32 "1hwv38a07ry5fv53r2vizifj83xf98znix8nx4zshqyk38vblgp5"))))
    (properties `((upstream-name . "maxcombo")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rlang
                             r-purrr
                             r-mvtnorm
                             r-mstate
                             r-mcmcpack
                             r-magrittr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=maxcombo")
    (synopsis
     "The Group Sequential Max-Combo Test for Comparing Survival Curves")
    (description
     "This package provides functions for comparing survival curves using the
max-combo test at a single timepoint or repeatedly at successive respective
timepoints while controlling type I error (i.e., the group sequential setting),
as published by Prior (2020) <doi:10.1177/0962280220931560>.  The max-combo test
is a generalization of the weighted log-rank test, which itself is a
generalization of the log-rank test, which is a commonly used statistical test
for comparing survival curves, e.g., during or after a clinical trial as part of
an effort to determine if a new drug or therapy is more effective at delaying
undesirable outcomes than an established drug or therapy or a placebo.")
    (license license:gpl2)))

(define-public r-maxaltall
  (package
    (name "r-maxaltall")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maxaltall" version))
       (sha256
        (base32 "13px8r6rirvycvr5s4i39h1bxdfr5c1qw2lgzd8yv6yg65xk8rhj"))))
    (properties `((upstream-name . "maxaltall")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-magrittr r-dplyr r-data-table))
    (home-page "https://cran.r-project.org/package=maxaltall")
    (synopsis
     "'FASTA' ML and âaltallâ Sequences from IQ-TREE .state Files")
    (description
     "Takes a .state file generated by IQ-TREE as an input and, for each ancestral
node present in the file, generates a FASTA-formatted maximum likelihood (ML)
sequence as well as an â@code{AltAllâ} sequence in which uncertain sites,
determined by the two parameters thres_1 and thres_2, have the maximum
likelihood state swapped with the next most likely state as described in Geeta
N. Eick, Jamie T. Bridgham, Douglas P. Anderson, Michael J. Harms, and Joseph W.
Thornton (2017), \"Robustness of Reconstructed Ancestral Protein Functions to
Statistical Uncertainty\" <doi:10.1093/molbev/msw223>.")
    (license license:gpl3+)))

(define-public r-mave
  (package
    (name "r-mave")
    (version "1.3.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAVE" version))
       (sha256
        (base32 "01n204bxabbm8pcpayy2s0jvhg73r5cv0026lb3vbk0m40z02kcz"))))
    (properties `((upstream-name . "MAVE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MAVE")
    (synopsis "Methods for Dimension Reduction")
    (description
     "This package provides functions for dimension reduction, using MAVE (Minimum
Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced
inverse regression of kernel version).  Methods for selecting the best dimension
are also included.  Xia (2002) <doi:10.1111/1467-9868.03411>; Xia (2007)
<doi:10.1214/009053607000000352>; Wang (2008) <doi:10.1198/016214508000000418>.")
    (license license:gpl2+)))

(define-public r-mauricer
  (package
    (name "r-mauricer")
    (version "2.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mauricer" version))
       (sha256
        (base32 "0v0wwyfq3zj6p1w6qx6jmqm4cr5jg6l3caq8ycxbylqcmrqqp6ij"))))
    (properties `((upstream-name . "mauricer")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-stringr r-beastier))
    (native-inputs (list r-knitr))
    (home-page
     "https://docs.ropensci.org/mauricer/https://github.com/ropensci/mauricer")
    (synopsis "Work with 'BEAST2' Packages")
    (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 (<https://www.beast2.org>), which, among others, allows one to install
BEAST2 package.  This package allows to work with BEAST2 packages from R'.")
    (license license:gpl3)))

(define-public r-mau
  (package
    (name "r-mau")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mau" version))
       (sha256
        (base32 "1wgiai8f1kbjh9hfwv4m0kavd44ib5xb33p8m16zpawnw14m7sj5"))))
    (properties `((upstream-name . "mau")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rdpack
                             r-rcolorbrewer
                             r-igraph
                             r-gtools
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pedroguarderas/mau")
    (synopsis "Decision Models with Multi Attribute Utility Theory")
    (description
     "This package provides functions for the creation, evaluation and test of
decision models based in Multi Attribute Utility Theory (MAUT).  Can process and
evaluate local risk aversion utilities for a set of indexes, compute utilities
and weights for the whole decision tree defining the decision model and simulate
weights employing Dirichlet distributions under addition constraints in weights.")
    (license license:lgpl3)))

(define-public r-matur
  (package
    (name "r-matur")
    (version "0.0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matuR" version))
       (sha256
        (base32 "01nxs4czzs1kcxk63nwr2gnvffmlnx3yfllrhi8khdkj498s6sx1"))))
    (properties `((upstream-name . "matuR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-magrittr
                             r-lubridate
                             r-ggrepel
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/josedv82/matuR")
    (synopsis "Athlete Maturation and Biobanding")
    (description
     "Identifying maturation stages across young athletes is paramount for talent
identification.  Furthermore, the concept of biobanding, or grouping of athletes
based on their biological development, instead of their chronological age, has
been widely researched.  The goal of this package is to help professionals
working in the field of strength & conditioning and talent ID obtain common
maturation metrics and as well as to quickly visualize this information via
several plotting options.  For the methods behind the computed maturation
metrics implemented in this package refer to Khamis, H. J., & Roche, A. F.
(1994) <https://pubmed.ncbi.nlm.nih.gov/7936860/>, Mirwald, R.L et al., (2002)
<https://pubmed.ncbi.nlm.nih.gov/11932580/> and Cumming, Sean P. et al., (2017)
<doi:10.1519/SSC.0000000000000281>.")
    (license license:expat)))

(define-public r-matskew
  (package
    (name "r-matskew")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatSkew" version))
       (sha256
        (base32 "019v2b1r0bvw84xs50smjb3jdz7gd2adz5d84xd8zq43lnml96d1"))))
    (properties `((upstream-name . "MatSkew")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MatSkew")
    (synopsis "Matrix Skew-T Parameter Estimation")
    (description
     "This package performs matrix skew-t parameter estimation, Gallaugher and
@code{McNicholas} (2017) <doi: 10.1002/sta4.143>.")
    (license license:gpl2+)))

(define-public r-matsindf
  (package
    (name "r-matsindf")
    (version "0.4.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matsindf" version))
       (sha256
        (base32 "0jvgf87zrd1mrs3rrk4g1mfmf5ja1ww0pqf5cjvdw2id9vvc11jq"))))
    (properties `((upstream-name . "matsindf")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-matsbyname
                             r-magrittr
                             r-lifecycle
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MatthewHeun/matsindf")
    (synopsis "Matrices in Data Frames")
    (description
     "This package provides functions to collapse a tidy data frame into matrices in a
data frame and expand a data frame of matrices into a tidy data frame.")
    (license license:expat)))

(define-public r-matsbyname
  (package
    (name "r-matsbyname")
    (version "0.6.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matsbyname" version))
       (sha256
        (base32 "1444a6gl24ra64p5zldia0kyn4hq01g5r49rc2mv0zq0gygxv67d"))))
    (properties `((upstream-name . "matsbyname")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-rclabels
                             r-purrr
                             r-matrixcalc
                             r-matrix
                             r-magrittr
                             r-lifecycle
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MatthewHeun/matsbyname")
    (synopsis
     "An Implementation of Matrix Mathematics that Respects Row and Column Names")
    (description
     "An implementation of matrix mathematics wherein operations are performed \"by
name.\".")
    (license license:expat)))

(define-public r-matrixstructest
  (package
    (name "r-matrixstructest")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixStrucTest" version))
       (sha256
        (base32 "06la9xmpi1viyjml4m1akbna3lwkz5lnq95v1nnf73i9zpfwz03k"))))
    (properties `((upstream-name . "matrixStrucTest")))
    (build-system r-build-system)
    (home-page "https://github.com/bdsegal/matrixStrucTest")
    (synopsis "Tests of Matrix Structure for Construct Validation")
    (description
     "Tests for block-diagonal structure in symmetric matrices (e.g. correlation
matrices) under the null hypothesis of exchangeable off-diagonal elements.  As
described in Segal et al. (2019), these tests can be useful for construct
validation either by themselves or as a complement to confirmatory factor
analysis.  Monte Carlo methods are used to approximate the permutation p-value
with Hubert's Gamma (Hubert, 1976) and a t-statistic.  This package also
implements the chi-squared statistic described by Steiger (1980).  Please see
Segal, et al. (2019) <doi:10.1007/s11336-018-9647-4> for more information.")
    (license license:gpl3+)))

(define-public r-matrixset
  (package
    (name "r-matrixset")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixset" version))
       (sha256
        (base32 "0ijh8nlg86f31az7x82px1m0805mrfal5xdpwzlalgzbq0irg69v"))))
    (properties `((upstream-name . "matrixset")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rcpp
                             r-r6
                             r-purrr
                             r-pillar
                             r-matrix
                             r-dplyr
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pascalcroteau/matrixset")
    (synopsis "Creating, Manipulating and Annotating Matrix Ensemble")
    (description
     "This package creates an object that stores a matrix ensemble, matrices that
share the same common properties, where rows and columns can be annotated.
Matrices must have the same dimension and dimnames.  Operators to manipulate
these objects are provided as well as mechanisms to apply functions to these
objects.")
    (license license:expat)))

(define-public r-matrixsampling
  (package
    (name "r-matrixsampling")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixsampling" version))
       (sha256
        (base32 "05dbn7rj07jx1hj7i6k3akf11hysxg6v32kav9fvsqmiy2zkippa"))))
    (properties `((upstream-name . "matrixsampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-keep))
    (home-page "https://github.com/stla/matrixsampling")
    (synopsis "Simulations of Matrix Variate Distributions")
    (description
     "This package provides samplers for various matrix variate distributions:
Wishart, inverse-Wishart, normal, t, inverted-t, Beta type I, Beta type II,
Gamma, confluent hypergeometric.  Allows to simulate the noncentral Wishart
distribution without the integer restriction on the degrees of freedom.")
    (license license:gpl3)))

(define-public r-matrixprofiler
  (package
    (name "r-matrixprofiler")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixprofiler" version))
       (sha256
        (base32 "1kqpxg0mvyc494nr2whrcwjaa51jm5n2mxjwhdcr87jqp5y3hma6"))))
    (properties `((upstream-name . "matrixprofiler")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppthread r-rcppprogress r-rcppparallel r-rcpp
                             r-checkmate))
    (home-page "https://github.com/matrix-profile-foundation/matrixprofiler")
    (synopsis "Matrix Profile for R")
    (description
     "This is the core functions needed by the tsmp package.  The low level and
carefully checked mathematical functions are here.  These are implementations of
the Matrix Profile concept that was created by CS-UCR
<http://www.cs.ucr.edu/~eamonn/@code{MatrixProfile.html>}.")
    (license license:gpl3)))

(define-public r-matrixprofile
  (package
    (name "r-matrixprofile")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixProfile" version))
       (sha256
        (base32 "0nd54k878xf7hzwxawh03c3na4jfvb68afarkwrmmffjlp3i8a9g"))))
    (properties `((upstream-name . "matrixProfile")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-ttr r-signal r-fftw))
    (home-page "https://github.com/ainsuotain/matrixprofile")
    (synopsis "Matrix Profile")
    (description
     "This package provides a simple and the early stage package for matrix profile
based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan
Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and
Eamonn Keogh (2016) <DOI:10.1109/ICDM.2016.0179>.  This package calculates
all-pairs-similarity for a given window size for time series data.")
    (license license:gpl3)))

(define-public r-matrixnormal
  (package
    (name "r-matrixnormal")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixNormal" version))
       (sha256
        (base32 "0l89s33sjxjb7dyzfny6v7cjw8qmlps4z1bjmlzvcka4qbn6kc2z"))))
    (properties `((upstream-name . "matrixNormal")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=matrixNormal")
    (synopsis "The Matrix Normal Distribution")
    (description
     "Computes densities, probabilities, and random deviates of the Matrix Normal
(Pocuca et al. (2019) <doi:10.48550/@code{arXiv.1910.02859>}).  Also includes
simple but useful matrix functions.  See the vignette for more information.")
    (license license:gpl3)))

(define-public r-matrixmodp
  (package
    (name "r-matrixmodp")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixmodp" version))
       (sha256
        (base32 "0axl3nvzsalxdmislw1vm9fg7dw39xqs7yr5jfl49h4l7h6ljzix"))))
    (properties `((upstream-name . "matrixmodp")))
    (build-system r-build-system)
    (home-page "https://github.com/rhigginbottom/matrixmodp")
    (synopsis "Working with Matrices over Finite Prime Fields")
    (description
     "This package provides functions for row-reducing and inverting matrices with
entries in many of the finite fields (those with a prime number of elements).
With this package, users will be able to find the reduced row echelon form
(RREF) of a matrix and calculate the inverse of a (square, invertible) matrix.")
    (license license:gpl2+)))

(define-public r-matrixmixtures
  (package
    (name "r-matrixmixtures")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatrixMixtures" version))
       (sha256
        (base32 "0l9f7sicigp88rqi2bn4bmlrb130qvyikn4mlfphcpa16rvk37dq"))))
    (properties `((upstream-name . "MatrixMixtures")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-snow r-foreach r-dosnow))
    (home-page "https://cran.r-project.org/package=MatrixMixtures")
    (synopsis "Model-Based Clustering via Matrix-Variate Mixture Models")
    (description
     "This package implements finite mixtures of matrix-variate contaminated normal
distributions via expectation conditional-maximization algorithm for model-based
clustering, as described in Tomarchio et al.(2020) <@code{arXiv:2005.03861>}.
One key advantage of this model is the ability to automatically detect potential
outlying matrices by computing their a posteriori probability of being typical
or atypical points.  Finite mixtures of matrix-variate t and matrix-variate
normal distributions are also implemented by using expectation-maximization
algorithms.")
    (license license:gpl2+)))

(define-public r-matrixlda
  (package
    (name "r-matrixlda")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatrixLDA" version))
       (sha256
        (base32 "0fixq35gp0fzmin75sfld2ln0zvnjgzrhk3b5yi7wdqzp99200yy"))))
    (properties `((upstream-name . "MatrixLDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-plyr r-glasso))
    (home-page "ajmolstad@github.io")
    (synopsis "Penalized Matrix-Normal Linear Discriminant Analysis")
    (description
     "Fits the penalized matrix-normal model to be used for linear discriminant
analysis with matrix-valued predictors.  For a description of the method, see
Molstad and Rothman (2018) <doi:10.1080/10618600.2018.1476249>.")
    (license license:gpl2)))

(define-public r-matrixlaplacian
  (package
    (name "r-matrixlaplacian")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixLaplacian" version))
       (sha256
        (base32 "1ixqdv3sz3sc79wn3vqhlq79j7x7cvl878m5hgvbcg6klfvlmmci"))))
    (properties `((upstream-name . "matrixLaplacian")))
    (build-system r-build-system)
    (propagated-inputs (list r-scatterplot3d))
    (home-page "https://cran.r-project.org/package=matrixLaplacian")
    (synopsis "Normalized Laplacian Matrix and Laplacian Map")
    (description
     "Constructs the normalized Laplacian matrix of a square matrix, returns the
eigenvectors (singular vectors) and visualization of normalized Laplacian map.")
    (license license:gpl2)))

(define-public r-matrixhmm
  (package
    (name "r-matrixhmm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatrixHMM" version))
       (sha256
        (base32 "09jzhyzszra40jbxwr1np3s2s59n0gnz6x49c4gzib4fdlngxbdg"))))
    (properties `((upstream-name . "MatrixHMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tidyr
                             r-tensor
                             r-snow
                             r-progress
                             r-mclust
                             r-laplacesdemon
                             r-foreach
                             r-dosnow
                             r-data-table))
    (home-page "https://cran.r-project.org/package=MatrixHMM")
    (synopsis
     "Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data")
    (description
     "This package implements three families of parsimonious hidden Markov models
(HMMs) for matrix-variate longitudinal data using the Expectation-Conditional
Maximization (ECM) algorithm.  The package supports matrix-variate normal, t,
and contaminated normal distributions as emission distributions.  For each
hidden state, parsimony is achieved through the eigen-decomposition of the
covariance matrices associated with the emission distribution.  This approach
results in a comprehensive set of 98 parsimonious HMMs for each type of emission
distribution.  Atypical matrix detection is also supported, utilizing the fitted
(heavy-tailed) models.")
    (license license:gpl3+)))

(define-public r-matrixeqtl
  (package
    (name "r-matrixeqtl")
    (version "2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatrixEQTL" version))
       (sha256
        (base32 "10r6h5bidj44kfbwiml9pkysbikqphfb21ig61qnx0qa12xcl6l8"))))
    (properties `((upstream-name . "MatrixEQTL")))
    (build-system r-build-system)
    (home-page
     "http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/")
    (synopsis
     "Matrix eQTL: Ultra Fast eQTL Analysis via Large Matrix Operations")
    (description
     "Matrix @code{eQTL} is designed for fast @code{eQTL} analysis on large datasets.
Matrix @code{eQTL} can test for association between genotype and gene expression
using linear regression with either additive or ANOVA genotype effects.  The
models can include covariates to account for factors as population
stratification, gender, and clinical variables.  It also supports models with
heteroscedastic and/or correlated errors, false discovery rate estimation and
separate treatment of local (cis) and distant (trans) @code{eQTLs}.  For more
details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.")
    (license license:lgpl3)))

(define-public r-matrixdist
  (package
    (name "r-matrixdist")
    (version "1.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixdist" version))
       (sha256
        (base32 "1lf4ar7fl3agcydcv01q0zqlnwdd501fk132nlbwb03ng6dmd2im"))))
    (properties `((upstream-name . "matrixdist")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-rcpparmadillo r-rcpp r-nnet))
    (home-page "https://github.com/martinbladt/matrixdist_1.0")
    (synopsis "Statistics for Matrix Distributions")
    (description
     "This package provides tools for phase-type distributions including the following
variants: continuous, discrete, multivariate, in-homogeneous, right-censored,
and regression.  Methods for functional evaluation, simulation and estimation
using the expectation-maximization (EM) algorithm are provided for all models.
The methods of this package are based on the following references.  Asmussen,
S., Nerman, O., & Olsson, M. (1996).  Fitting phase-type distributions via the
EM algorithm, Olsson, M. (1996).  Estimation of phase-type distributions from
censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>,
Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>,
Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022)
<doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022)
<doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>,
Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M.,
& Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023)
<doi:10.1016/j.insmatheco.2023.02.008>.")
    (license license:gpl3)))

(define-public r-matrixcut
  (package
    (name "r-matrixcut")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrixcut" version))
       (sha256
        (base32 "1q1m6yd42cwxdnysjgcnp7mg8f9cryphd59k0wzsrirqnj8c1jz4"))))
    (properties `((upstream-name . "matrixcut")))
    (build-system r-build-system)
    (propagated-inputs (list r-inflection r-igraph))
    (home-page "https://cran.r-project.org/package=matrixcut")
    (synopsis "Determines Clustering Threshold Based on Similarity Values")
    (description
     "The user must supply a matrix filled with similarity values.  The software will
search for significant differences between similarity values at different
hierarchical levels.  The algorithm will return a Loess-smoothed plot of the
similarity values along with the inflection point, if there are any.  There is
the option to search for an inflection point within a specified range.  The
package also has a function that will return the matrix components at a
specified cutoff.  References: Mullner. <@code{ArXiv:1109.2378>}; Cserhati,
Carter. (2020, Journal of Creation 34(3):41-50),
<https://dl0.creation.com/articles/p137/c13759/j34-3_64-73.pdf>.")
    (license license:gpl3+)))

(define-public r-matrixcorrelation
  (package
    (name "r-matrixcorrelation")
    (version "0.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatrixCorrelation" version))
       (sha256
        (base32 "1na3y1crlj57c1xq9ja3v94cx2dr0myrlx9bxvhhwss3q3r1lgby"))))
    (properties `((upstream-name . "MatrixCorrelation")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-rcpparmadillo
                             r-rcpp
                             r-progress
                             r-pracma
                             r-plotrix))
    (home-page "https://github.com/khliland/MatrixCorrelation/")
    (synopsis "Matrix Correlation Coefficients")
    (description
     "Computation and visualization of matrix correlation coefficients.  The main
method is the Similarity of Matrices Index, while various related measures like
r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's linear correlation
and Coxhead's coefficient are included for comparison and flexibility.")
    (license license:gpl2)))

(define-public r-matrisk
  (package
    (name "r-matrisk")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrisk" version))
       (sha256
        (base32 "1qfhpbxza1iz0075hq3ghs6djgr6l942yx1q7pjng35rs4xj2d48"))))
    (properties `((upstream-name . "matrisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-quantreg r-plot3d r-dfoptim))
    (home-page "https://cran.r-project.org/package=matrisk")
    (synopsis "Macroeconomic-at-Risk")
    (description
     "The Macroeconomics-at-Risk (@code{MaR}) approach is based on a two-step
semi-parametric estimation procedure that allows to forecast the full
conditional distribution of an economic variable at a given horizon, as a
function of a set of factors.  These density forecasts are then be used to
produce coherent forecasts for any downside risk measure, e.g., value-at-risk,
expected shortfall, downside entropy.  Initially introduced by Adrian et al.
(2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth
to financial conditions, the @code{MaR} approach is currently extensively used
by international financial institutions to provide Value-at-Risk (@code{VaR})
type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk).
 This package provides methods for estimating these models.  Datasets for the US
and the Eurozone are available to allow testing of the Adrian et al (2019)
model.  This package constitutes a useful toolbox (data and functions) for
private practitioners, scholars as well as policymakers.")
    (license license:gpl3)))

(define-public r-matriks
  (package
    (name "r-matriks")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matRiks" version))
       (sha256
        (base32 "0npl1dkjc4q4rgfhqd80xg7v751dkxbfc3rnj93rw3z2mdwkk50i"))))
    (properties `((upstream-name . "matRiks")))
    (build-system r-build-system)
    (propagated-inputs (list r-desctools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=matRiks")
    (synopsis "Generates Raven-Like Matrices According to Rules")
    (description
     "Generates Raven like matrices according to different rules and the response list
associated to the matrix.  The package can generate matrices composed of 4 or 9
cells, along with a response list of 11 elements (the correct response + 10
incorrect responses).  The matrices can be generated according to both logical
rules (i.e., the relationships between the elements in the matrix are
manipulated to create the matrix) and visual-spatial rules (i.e., the visual or
spatial characteristics of the elements are manipulated to generate the matrix).
 The graphical elements of this package are based on the @code{DescTools}
package.  This package has been developed within the PRIN2020 Project (Prot.
20209WKCLL) titled \"Computerized, Adaptive and Personalized Assessment of
Executive Functions and Fluid Intelligence\" and founded by the Italian Ministry
of Education and Research.")
    (license license:expat)))

(define-public r-matricks
  (package
    (name "r-matricks")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matricks" version))
       (sha256
        (base32 "0ik61jxd4hpw149dbbmis3wsfsim60mw9d0qjwh047qfg6sm1vsc"))))
    (properties `((upstream-name . "matricks")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-reshape2 r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/krzjoa/matricks")
    (synopsis "Useful Tricks for Matrix Manipulation")
    (description
     "This package provides functions, which make matrix creation conciser (such as
the core package's function m() for rowwise matrix definition or runifm() for
random value matrices).  Allows to set multiple matrix values at once, by using
list of formulae.  Provides additional matrix operators and dedicated plotting
function.")
    (license license:expat)))

(define-public r-matrans
  (package
    (name "r-matrans")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matrans" version))
       (sha256
        (base32 "0j9a82wccdrgd3jqhwqv8bjf2c5w55hk9cl74ylzqrgnv45xr1cd"))))
    (properties `((upstream-name . "matrans")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-mass r-formatr r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=matrans")
    (synopsis "Model Averaging-Assisted Optimal Transfer Learning")
    (description
     "Transfer learning, as a prevailing technique in computer sciences, aims to
improve the performance of a target model by leveraging auxiliary information
from heterogeneous source data.  We provide novel tools for multi-source
transfer learning under statistical models based on model averaging strategies,
including linear regression models, partially linear models.  Unlike existing
transfer learning approaches, this method integrates the auxiliary information
through data-driven weight assignments to avoid negative transfer.  This is the
first package for transfer learning based on the optimal model averaging
frameworks, providing efficient implementations for practitioners in
multi-source data modeling.  The details are described in Hu and Zhang (2023)
<https://jmlr.org/papers/v24/23-0030.html>.")
    (license license:gpl3+)))

(define-public r-matpow
  (package
    (name "r-matpow")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matpow" version))
       (sha256
        (base32 "1gjrq9bs94xqyms6sfyxp00f0x2gv4dfqcsz29ficzgl4iapns95"))))
    (properties `((upstream-name . "matpow")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=matpow")
    (synopsis "Matrix Powers")
    (description
     "This package provides a general framework for computing powers of matrices.  A
key feature is the capability for users to write callback functions, called
after each iteration, thus enabling customization for specific applications.
Diverse types of matrix classes/matrix multiplication are accommodated.  If the
multiplication type computes in parallel, then the package computation is also
parallel.")
    (license license:gpl2+)))

(define-public r-matman
  (package
    (name "r-matman")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matman" version))
       (sha256
        (base32 "0mwxxqqlg6yydc06hrjqk5ly686s21w5i5z40axxwkmpzyz84sqv"))))
    (properties `((upstream-name . "matman")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-shinywidgets
                             r-shinydashboard
                             r-shiny
                             r-plotly
                             r-parsedate
                             r-lubridate
                             r-forecast
                             r-dt
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=matman")
    (synopsis "Material Management")
    (description
     "This package provides a set of functions, classes and methods for performing ABC
and ABC/XYZ analyses, identifying overperforming, underperforming and constantly
performing items, and plotting, analyzing as well as predicting the temporal
development of items.")
    (license license:gpl3)))

(define-public r-matlib
  (package
    (name "r-matlib")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matlib" version))
       (sha256
        (base32 "1cbip4xnd854zb42pkr44094bg9jm4gxq37pf3837p1mwkb0w4vc"))))
    (properties `((upstream-name . "matlib")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-rstudioapi
                             r-rmarkdown
                             r-rgl
                             r-mass
                             r-knitr
                             r-dplyr
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/friendly/matlib")
    (synopsis
     "Matrix Functions for Teaching and Learning Linear Algebra and Multivariate Statistics")
    (description
     "This package provides a collection of matrix functions for teaching and learning
matrix linear algebra as used in multivariate statistical methods.  These
functions are mainly for tutorial purposes in learning matrix algebra ideas
using R. In some cases, functions are provided for concepts available elsewhere
in R, but where the function call or name is not obvious.  In other cases,
functions are provided to show or demonstrate an algorithm.  In addition, a
collection of functions are provided for drawing vector diagrams in 2D and 3D.")
    (license license:gpl2+)))

(define-public r-matlabr
  (package
    (name "r-matlabr")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matlabr" version))
       (sha256
        (base32 "1ba6cajnzldb1gmn7rf0b198h6mdh58r3frm0z0cmvbinspwz3x7"))))
    (properties `((upstream-name . "matlabr")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-stringr))
    (home-page "https://cran.r-project.org/package=matlabr")
    (synopsis "An Interface for MATLAB using System Calls")
    (description
     "This package provides users to call MATLAB from using the \"system\" command.
Allows users to submit lines of code or MATLAB m files.  This is in comparison
to R.matlab', which creates a MATLAB server.")
    (license license:gpl2)))

(define-public r-matlab2r
  (package
    (name "r-matlab2r")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matlab2r" version))
       (sha256
        (base32 "0fzp9l34mc0bfl3a9phq4rh9idbb22a2d4j00rvxb4b7yzj2p553"))))
    (properties `((upstream-name . "matlab2r")))
    (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-styler))
    (home-page "https://ocbe-uio.github.io/matlab2r/")
    (synopsis "Translation Layer from MATLAB to R")
    (description
     "Allows users familiar with MATLAB to use MATLAB-named functions in R. Several
basic MATLAB functions are written in this package to mimic the behavior of
their original counterparts, with more to come as this package grows.")
    (license license:gpl3+)))

(define-public r-matlab
  (package
    (name "r-matlab")
    (version "1.0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matlab" version))
       (sha256
        (base32 "0l7xqim6sziz3wlxzxc0rjfjrbwidj2l0wri9w5bzabk1dbblgzw"))))
    (properties `((upstream-name . "matlab")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=matlab")
    (synopsis "'MATLAB' Emulation Package")
    (description "Emulate MATLAB code using R'.")
    (license license:artistic2.0)))

(define-public r-mathpix
  (package
    (name "r-mathpix")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mathpix" version))
       (sha256
        (base32 "0zzzgz94cmm0gh007mb5jviqcm93ynac9gdkhikza2rp626wnj3x"))))
    (properties `((upstream-name . "mathpix")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi r-purrr r-magick r-httr r-base64enc))
    (home-page "https://github.com/jonocarroll/mathpix")
    (synopsis "Support for the 'Mathpix' API (Image to 'LaTeX')")
    (description
     "Given an image of a formula (typeset or handwritten) this package provides calls
to the Mathpix service to produce the @code{LaTeX} code which should generate
that image, and pastes it into a (e.g. an rmarkdown') document.  See
<https://docs.mathpix.com/> for full details.  Mathpix is an external service
and use of the API is subject to their terms and conditions.")
    (license license:gpl3+)))

(define-public r-mathml
  (package
    (name "r-mathml")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mathml" version))
       (sha256
        (base32 "11mrrz0w9mzi9621d9cgnialyrhv8v91ak9slrcni5gx3i3fww84"))))
    (properties `((upstream-name . "mathml")))
    (build-system r-build-system)
    (propagated-inputs (list r-rolog r-knitr))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/mgondan/mathml")
    (synopsis "Translate R Expressions to 'MathML' and 'LaTeX'/'MathJax'")
    (description
     "Translate R expressions to @code{MathML} or @code{MathJax'/'LaTeX} so that they
can be rendered in R Markdown documents and shiny apps.  This package depends on
R package rolog', which requires an installation of the SWI-Prolog runtime
either from swi-prolog.org or from R package rswipl'.")
    (license (license:fsdg-compatible "FreeBSD"))))

(define-public r-materialmodifier
  (package
    (name "r-materialmodifier")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "materialmodifier" version))
       (sha256
        (base32 "0vawqviwh7acbi1f6jlfnwsh6axzwp1hvr697j5ws5g2hz16ib5r"))))
    (properties `((upstream-name . "materialmodifier")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-readbitmap
                             r-png
                             r-moments
                             r-magrittr
                             r-jpeg
                             r-imager
                             r-downloader))
    (home-page "https://github.com/tsuda16k/materialmodifier")
    (synopsis "Apply Photo Editing Effects")
    (description
     "You can apply image processing effects that modifies the perceived material
properties of objects in photos, such as gloss, smoothness, and blemishes.  This
is an implementation of the algorithm proposed by Boyadzhiev et al. (2015)
\"Band-Sifting Decomposition for Image Based Material Editing\".  Documentation
and practical tips of the package is available at
<https://github.com/tsuda16k/materialmodifier>.")
    (license license:expat)))

(define-public r-mateable
  (package
    (name "r-mateable")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mateable" version))
       (sha256
        (base32 "0w5l4dkrz2bgk0pdiqpbbgkf4hyd6gdldrby8bivz03g93rnpq9g"))))
    (properties `((upstream-name . "mateable")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-rcpp r-fnn))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stuartWagenius/mateable")
    (synopsis "Assess Mating Potential in Space and Time")
    (description
     "Simulate, manage, visualize, and analyze spatially and temporally explicit
datasets of mating potential.  Implements methods to calculate synchrony,
proximity, and compatibility.Synchrony calculations are based on methods
described in Augspurger (1983) <doi:10.2307/2387650>, Kempenaers (1993)
<doi:10.2307/3676415>, Ison et al. (2014) <doi:10.3732/ajb.1300065>, and
variations on these, as described.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-matconv
  (package
    (name "r-matconv")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matconv" version))
       (sha256
        (base32 "1iyy344p3fsfx3gw9kzr0x9zhsplba22fpzl1mhk10wvf99xiwb3"))))
    (properties `((upstream-name . "matconv")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=matconv")
    (synopsis "Code Converter from the Matlab/Octave Language to R")
    (description
     "Transferring over a code base from Matlab to R is often a repetitive and
inefficient use of time.  This package provides a translator for Matlab / Octave
code into R code.  It does some syntax changes, but most of the heavy lifting is
in the function changes since the languages are so similar.  Options for
different data structures and the functions that can be changed are given.  The
Matlab code should be mostly in adherence to the standard style guide but some
effort has been made to accommodate different number of spaces and other small
syntax issues.  This will not make the code more R friendly and may not even run
afterwards.  However, the rudimentary syntax, base function and data structure
conversion is done quickly so that the maintainer can focus on changes to the
design structure.")
    (license license:gpl2+)))

(define-public r-matchthem
  (package
    (name "r-matchthem")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatchThem" version))
       (sha256
        (base32 "12kaaji3nhqggdfhb3hkydsg66p9qpxh95g5z5sf4wym9ks0qhi3"))))
    (properties `((upstream-name . "MatchThem")))
    (build-system r-build-system)
    (propagated-inputs (list r-weightit r-survey r-rlang r-mice r-matchit))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/FarhadPishgar/MatchThem")
    (synopsis "Matching and Weighting Multiply Imputed Datasets")
    (description
     "This package provides essential tools for the pre-processing techniques of
matching and weighting multiply imputed datasets.  The package includes
functions for matching within and across multiply imputed datasets using various
methods, estimating weights for units in the imputed datasets using multiple
weighting methods, calculating causal effect estimates in each matched or
weighted dataset using parametric or non-parametric statistical models, and
pooling the resulting estimates according to Rubin's rules (please see
<https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).")
    (license license:gpl2+)))

(define-public r-matchr
  (package
    (name "r-matchr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matchr" version))
       (sha256
        (base32 "01nvas5ngkhzhr98n1786zsih2n2lbxx3ajwaqfd96ai0yqw2jnh"))))
    (properties `((upstream-name . "matchr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang))
    (home-page "https://cran.r-project.org/package=matchr")
    (synopsis "Pattern Matching and Enumerated Types in R")
    (description
     "Inspired by pattern matching and enum types in Rust and many functional
programming languages, this package offers an updated version of the switch
function called Match that accepts atomic values, functions, expressions, and
enum variants.  Conditions and return expressions are separated by -> and
multiple conditions can be associated with the same return expression using |'.
Match also includes support for fallthrough'.  The package also replicates the
Result and Option enums from Rust.")
    (license license:expat)))

(define-public r-matchmulti
  (package
    (name "r-matchmulti")
    (version "1.1.12.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matchMulti" version))
       (sha256
        (base32 "07c02dwim46f511fswbkr7s25inli7nblvf11gb5npz7p7yjc9bw"))))
    (properties `((upstream-name . "matchMulti")))
    (build-system r-build-system)
    (propagated-inputs (list r-weights
                             r-sandwich
                             r-rlang
                             r-rcbsubset
                             r-plyr
                             r-mvtnorm
                             r-mass
                             r-magrittr
                             r-dplyr
                             r-coin))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=matchMulti")
    (synopsis "Optimal Multilevel Matching using a Network Algorithm")
    (description
     "This package performs multilevel matches for data with cluster- level treatments
and individual-level outcomes using a network optimization algorithm.  Functions
for checking balance at the cluster and individual levels are also provided, as
are methods for permutation-inference-based outcome analysis.  Details in
Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>.  The optmatch package, which
is useful for running many of the provided functions, may be downloaded from
Github at <https://github.com/markmfredrickson/optmatch> if not available on
CRAN.")
    (license license:expat)))

(define-public r-matchmaker
  (package
    (name "r-matchmaker")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matchmaker" version))
       (sha256
        (base32 "1sa2cah8gjvg0159rv1sfw9p0aljg66xmij31mz2vfk5683r4b00"))))
    (properties `((upstream-name . "matchmaker")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-forcats r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://www.repidemicsconsortium.org/matchmaker")
    (synopsis "Flexible Dictionary-Based Cleaning")
    (description
     "This package provides flexible dictionary-based cleaning that allows users to
specify implicit and explicit missing data, regular expressions for both data
and columns, and global matches, while respecting ordering of factors.  This
package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit
for outbreak analysis.")
    (license license:gpl3)))

(define-public r-matchlinreg
  (package
    (name "r-matchlinreg")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatchLinReg" version))
       (sha256
        (base32 "1spkjpm4pviqv52nknhww1vcjc61sgsfsvp0ilpp4srh0ykc8ihh"))))
    (properties `((upstream-name . "MatchLinReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-matching r-hmisc))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=MatchLinReg")
    (synopsis "Combining Matching and Linear Regression for Causal Inference")
    (description
     "Core functions as well as diagnostic and calibration tools for combining
matching and linear regression for causal inference in observational studies.")
    (license license:gpl2+)))

(define-public r-matchit
  (package
    (name "r-matchit")
    (version "4.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatchIt" version))
       (sha256
        (base32 "17l5b6i4bmg3gjjhzagy451zcv2i5xj4j6zzxgiqf955sgywlfdf"))))
    (properties `((upstream-name . "MatchIt")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rcppprogress r-rcpp r-chk r-backports))
    (native-inputs (list r-knitr))
    (home-page "https://kosukeimai.github.io/MatchIt/")
    (synopsis "Nonparametric Preprocessing for Parametric Causal Inference")
    (description
     "Selects matched samples of the original treated and control groups with similar
covariate distributions -- can be used to match exactly on covariates, to match
on propensity scores, or perform a variety of other matching procedures.  The
package also implements a series of recommendations offered in Ho, Imai, King,
and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The gurobi package, which is not on
CRAN, is optional and comes with an installation of the Gurobi Optimizer,
available at <https://www.gurobi.com>.).")
    (license license:gpl2+)))

(define-public r-matchingr
  (package
    (name "r-matchingr")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matchingR" version))
       (sha256
        (base32 "1wr1faaixn5scx2c8g2jfvfm489k0ah9isza67gz0db8knja9nig"))))
    (properties `((upstream-name . "matchingR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jtilly/matchingR/")
    (synopsis "Matching Algorithms in R and C++")
    (description
     "Computes matching algorithms quickly using Rcpp.  Implements the Gale-Shapley
Algorithm to compute the stable matching for two-sided markets, such as the
stable marriage problem and the college-admissions problem.  Implements Irving's
Algorithm for the stable roommate problem.  Implements the top trading cycle
algorithm for the indivisible goods trading problem.")
    (license license:gpl2+)))

(define-public r-matchingmarkets
  (package
    (name "r-matchingmarkets")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matchingMarkets" version))
       (sha256
        (base32 "0rrwzbzb8nmqriw0kvk07ypmvbxg429xfv79a60s79qqffc1vryd"))))
    (properties `((upstream-name . "matchingMarkets")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-partitions
                             r-lpsolve
                             r-lattice))
    (native-inputs (list r-knitr))
    (home-page "https://matchingMarkets.org")
    (synopsis "Analysis of Stable Matchings")
    (description
     "This package implements structural estimators to correct for the sample
selection bias from observed outcomes in matching markets.  This includes
one-sided matching of agents into groups (Klein, 2015)
<https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1521.pdf> as well
as two-sided matching of students to schools (Aue et al., 2020)
<https://ftp.zew.de/pub/zew-docs/dp/dp20032.pdf>.  The package also contains
algorithms to find stable matchings in the three most common matching problems:
the stable roommates problem (Irving, 1985) <doi:10.1016/0196-6774(85)90033-1>,
the college admissions problem (Gale and Shapley, 1962) <doi:10.2307/2312726>,
and the house allocation problem (Shapley and Scarf, 1974)
<doi:10.1016/0304-4068(74)90033-0>.")
    (license license:gpl2+)))

(define-public r-matching
  (package
    (name "r-matching")
    (version "4.10-15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Matching" version))
       (sha256
        (base32 "1l6ygj6jqb7w4n6x87j02bhd7411mh4zf7ajr9414frwj1dqzv9h"))))
    (properties `((upstream-name . "Matching")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://github.com/JasjeetSekhon/Matching")
    (synopsis
     "Multivariate and Propensity Score Matching with Balance Optimization")
    (description
     "This package provides functions for multivariate and propensity score matching
and for finding optimal balance based on a genetic search algorithm.  A variety
of univariate and multivariate metrics to determine if balance has been obtained
are also provided.  For details, see the paper by Jasjeet Sekhon (2007,
<doi:10.18637/jss.v042.i07>).")
    (license license:gpl3)))

(define-public r-matchgate
  (package
    (name "r-matchgate")
    (version "0.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MatchGATE" version))
       (sha256
        (base32 "1kj60sjpmmab8zjcncp7q6nb0j84kzznz3cspp3j0a5w8fjfn9gp"))))
    (properties `((upstream-name . "MatchGATE")))
    (build-system r-build-system)
    (propagated-inputs (list r-locpol))
    (home-page "https://cran.r-project.org/package=MatchGATE")
    (synopsis "Estimate Group Average Treatment Effects with Matching")
    (description
     "Two novel matching-based methods for estimating group average treatment effects
(GATEs).  The match_y1y0() and match_y1y0_bc() functions are used for imputing
the potential outcomes based on matching and bias-corrected matching techniques,
respectively.  The @code{EstGATE}() function is employed to estimate the GATE
after imputing the potential outcomes.")
    (license license:gpl3)))

(define-public r-matchfeat
  (package
    (name "r-matchfeat")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matchFeat" version))
       (sha256
        (base32 "0jh484rr71b7887igfslbg7xbr661l9c34d650xd7ajx4gfpn540"))))
    (properties `((upstream-name . "matchFeat")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-clue))
    (home-page "https://cran.r-project.org/package=matchFeat")
    (synopsis "One-to-One Feature Matching")
    (description
     "Statistical methods to match feature vectors between multiple datasets in a
one-to-one fashion.  Given a fixed number of classes/distributions, for each
unit, exactly one vector of each class is observed without label.  The goal is
to label the feature vectors using each label exactly once so to produce the
best match across datasets, e.g. by minimizing the variability within classes.
Statistical solutions based on empirical loss functions and probabilistic
modeling are provided.  The Gurobi software and its R interface package are
required for one of the package functions (match.2x()) and can be obtained at
<https://www.gurobi.com/> (free academic license).  For more details, refer to
Degras (2022) <doi:10.1080/10618600.2022.2074429> \"Scalable feature matching for
large data collections\" and Bandelt, Maas, and Spieksma (2004)
<doi:10.1057/palgrave.jors.2601723> \"Local search heuristics for multi-index
assignment problems with decomposable costs\".")
    (license license:gpl2)))

(define-public r-matahari
  (package
    (name "r-matahari")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "matahari" version))
       (sha256
        (base32 "03mhn4qh1vcz2hr0mxp6gijwgrzvbpfb8dq7lqhchbk5gq5g3fnh"))))
    (properties `((upstream-name . "matahari")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rstudioapi
                             r-rlang
                             r-readr
                             r-purrr
                             r-jsonlite
                             r-clipr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jhudsl/matahari")
    (synopsis "Spy on Your R Session")
    (description
     "Conveniently log everything you type into the R console.  Logs are are stored as
tidy data frames which can then be analyzed using tidyverse style tools.")
    (license license:expat)))

(define-public r-mata
  (package
    (name "r-mata")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MATA" version))
       (sha256
        (base32 "0qydfp856qlmiwf3a2vs2dfk203sx8vvzrqn4hga9wi7bxdylk9f"))))
    (properties `((upstream-name . "MATA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MATA")
    (synopsis
     "Model-Averaged Tail Area (MATA) Confidence Interval and Distribution")
    (description
     "Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence intervals, and
MATA-Wald confidence densities and distributions, which are constructed using
single-model frequentist estimators and model weights.  See Turek and Fletcher
(2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019)
<doi:10.1007/s10651-019-00432-5> for details.")
    (license license:gpl2)))

(define-public r-mat
  (package
    (name "r-mat")
    (version "2.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAT" version))
       (sha256
        (base32 "0zihjan1kg3h4fkg51m9pfnl9763024q0r83r1yk83ckb78v2s88"))))
    (properties `((upstream-name . "MAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=MAT")
    (synopsis "Multidimensional Adaptive Testing")
    (description
     "Simulates Multidimensional Adaptive Testing using the multidimensional
three-parameter logistic model as described in Segall (1996)
<doi:10.1007/BF02294343>, van der Linden (1999) <doi:10.3102/10769986024004398>,
Reckase (2009) <doi:10.1007/978-0-387-89976-3>, and Mulder & van der Linden
(2009) <doi:10.1007/s11336-008-9097-5>.")
    (license (license:fsdg-compatible "GPL (>= 2.10)"))))

(define-public r-mastif
  (package
    (name "r-mastif")
    (version "2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mastif" version))
       (sha256
        (base32 "04m2cpcq1n27zmckj6hz6yvzcs6ray53z4nwab3vza9pjaa0m6ab"))))
    (properties `((upstream-name . "mastif")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-stringr
                             r-stringi
                             r-robustbase
                             r-repmis
                             r-rcpparmadillo
                             r-rcpp
                             r-rann
                             r-corrplot
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mastif")
    (synopsis "Mast Inference and Forecasting")
    (description
     "Analyzes production and dispersal of seeds dispersed from trees and recovered in
seed traps.  Motivated by long-term inventory plots where seed collections are
used to infer seed production by each individual plant.")
    (license license:gpl2+)))

(define-public r-masswater
  (package
    (name "r-masswater")
    (version "2.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MassWateR" version))
       (sha256
        (base32 "0hfjddm19r53hig48sfxkkjqib4ip651q2asfgp3639w4akn8pzx"))))
    (properties `((upstream-name . "MassWateR")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-units
                             r-tidyr
                             r-tibble
                             r-sf
                             r-rmarkdown
                             r-readxl
                             r-rcolorbrewer
                             r-raster
                             r-prettymapr
                             r-lubridate
                             r-httr
                             r-ggspatial
                             r-ggrepel
                             r-ggplot2
                             r-flextable
                             r-dplyr
                             r-curl))
    (home-page "<https://github.com/massbays-tech/MassWateR>")
    (synopsis
     "Quality Control and Analysis of Massachusetts Water Quality Data")
    (description
     "This package provides methods for quality control and exploratory analysis of
surface water quality data collected in Massachusetts, USA. Functions are
developed to facilitate data formatting for the Water Quality Exchange Network
<https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting of
data quality objectives to state agencies.  Quality control methods are from
Massachusetts Department of Environmental Protection (2020)
<https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.")
    (license license:cc0)))

(define-public r-masstimate
  (package
    (name "r-masstimate")
    (version "2.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MASSTIMATE" version))
       (sha256
        (base32 "1dvidwjw4zgflms5njbd9n85g1gbgfg6hk0fb63q5n65iyxxfmyh"))))
    (properties `((upstream-name . "MASSTIMATE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MASSTIMATE")
    (synopsis "Body Mass Estimation Equations for Vertebrates")
    (description
     "Estimation equations are from a variety of sources and associated error
estimation.")
    (license license:gpl2+)))

(define-public r-massivegst
  (package
    (name "r-massivegst")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "massiveGST" version))
       (sha256
        (base32 "13qhn15gr4fvri1p843zz7y1q6701qmkxq5lysydfmdaxp5hsaxd"))))
    (properties `((upstream-name . "massiveGST")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexls r-visnetwork r-msigdbr r-igraph
                             r-formattable))
    (native-inputs (list r-knitr))
    (home-page "<https://github.com/stefanoMP/massiveGST>")
    (synopsis "Competitive Gene Sets Test with the Mann-Whitney-Wilcoxon Test")
    (description
     "Friendly implementation of the Mann-Whitney-Wilcoxon test for competitive gene
set enrichment analysis.")
    (license license:gpl3+)))

(define-public r-massign
  (package
    (name "r-massign")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Massign" version))
       (sha256
        (base32 "09i32lsrx40rxrnb9alfipfrkipy4mza5nr4gcpn2a5h86ak9290"))))
    (properties `((upstream-name . "Massign")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Massign")
    (synopsis "Simple Matrix Construction")
    (description
     "Constructing matrices for quick prototyping can be a nuisance, requiring the
user to think about how to fill the matrix with values using the matrix()
function.  The %<-% operator solves that issue by allowing the user to construct
matrices using code that shows the actual matrices.")
    (license license:expat)))

(define-public r-massextra
  (package
    (name "r-massextra")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MASSExtra" version))
       (sha256
        (base32 "0agr8kf7hh7mjpa98y486bql9wxjwif9f03wz468srzz8jjin39h"))))
    (properties `((upstream-name . "MASSExtra")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-demokde))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MASSExtra")
    (synopsis "Some 'MASS' Enhancements")
    (description
     "Some enhancements, extensions and additions to the facilities of the recommended
MASS package that are useful mainly for teaching purposes, with more convenient
default settings and user interfaces.  Key functions from MASS are imported and
re-exported to avoid masking conflicts.  In addition we provide some additional
functions mainly used to illustrate coding paradigms and techniques, such as
Gramm-Schmidt orthogonalisation and generalised eigenvalue problems.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-masscor
  (package
    (name "r-masscor")
    (version "0.0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "masscor" version))
       (sha256
        (base32 "1153gx648csmzc2znf7l84f134qzp238hqj2s60r9najk1pczgf6"))))
    (properties `((upstream-name . "masscor")))
    (build-system r-build-system)
    (propagated-inputs (list r-metrology))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=masscor")
    (synopsis "Mass Measurement Corrections")
    (description
     "Mass measurement corrections and uncertainties using calibration data, as
recommended by EURAMET's guideline No.  18 (2015) ISBN:978-3-942992-40-4 .  The
package provides classes, functions, and methods for storing information
contained in calibration certificates and converting balance readings to both
conventional mass and real mass.  For the latter, the Magnitude of the Air
Buoyancy Correction factor employs models (such as the CIMP-2007 formula revised
by Picard, Davis, GlÃ¤ser, and Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to
estimate the local air density using measured environmental conditions.")
    (license license:gpl3+)))

(define-public r-maskranger
  (package
    (name "r-maskranger")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maskRangeR" version))
       (sha256
        (base32 "1pdj0jdxrqss6hp20163fh1ywdd6wmwjl4s8gnpd3m9d1j955gr3"))))
    (properties `((upstream-name . "maskRangeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-raster
                             r-magrittr
                             r-lubridate
                             r-e1071
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=maskRangeR")
    (synopsis "Mask Species Geographic Ranges")
    (description
     "Mask ranges based on expert knowledge or remote sensing layers.  These tools can
be combined to quantitatively and reproducibly generate a new map or to update
an existing map.  Methods include expert opinion and data-driven tools to
generate thresholds for binary masks.")
    (license license:gpl3)))

(define-public r-maskr
  (package
    (name "r-maskr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maskr" version))
       (sha256
        (base32 "0vqxwhmyp9w0i76px6407k5fcyhs4s552xv45wdig4b8268mpnmj"))))
    (properties `((upstream-name . "maskr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-rlang r-pillar r-cli))
    (home-page "https://github.com/inpowell/maskr")
    (synopsis "Visual Class for Vectors with Non-Publishing Requirements")
    (description
     "Create vectors with sticky flags for elements that should not be displayed.
Numeric vectors have basic subset and arithmetic methods implemented.")
    (license license:expat)))

(define-public r-mashr
  (package
    (name "r-mashr")
    (version "0.2.79")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mashr" version))
       (sha256
        (base32 "11pqhnzp1svhs565hm0v15ip1pgnl609ssm4d0qzl8vm4xq3d2qm"))))
    (properties `((upstream-name . "mashr")))
    (build-system r-build-system)
    (propagated-inputs (list r-softimpute
                             r-rmeta
                             r-rcppgsl
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-mvtnorm
                             r-assertthat
                             r-ashr
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stephenslab/mashr")
    (synopsis "Multivariate Adaptive Shrinkage")
    (description
     "This package implements the multivariate adaptive shrinkage (mash) method of
Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing
large numbers of effects in many conditions (or many outcomes).  Mash takes an
empirical Bayes approach to testing and effect estimation; it estimates patterns
of similarity among conditions, then exploits these patterns to improve accuracy
of the effect estimates.  The core linear algebra is implemented in C++ for fast
model fitting and posterior computation.")
    (license license:bsd-3)))

(define-public r-mase
  (package
    (name "r-mase")
    (version "0.1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mase" version))
       (sha256
        (base32 "0fvbsw6y5whysm70wr74pzj5knnw91gzpihi9snq69q1axg7yc17"))))
    (properties `((upstream-name . "mase")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survey
                             r-rpms
                             r-rdpack
                             r-rcppeigen
                             r-rcpp
                             r-glmnet
                             r-ellipsis
                             r-dplyr
                             r-boot))
    (home-page "https://cran.r-project.org/package=mase")
    (synopsis "Model-Assisted Survey Estimators")
    (description
     "This package provides a set of model-assisted survey estimators and
corresponding variance estimators for single stage, unequal probability, without
replacement sampling designs.  All of the estimators can be written as a
generalized regression estimator with the Horvitz-Thompson, ratio,
post-stratified, and regression estimators summarized by Sarndal et al. (1992,
ISBN:978-0-387-40620-6).  Two of the estimators employ a statistical learning
model as the assisting model: the elastic net regression estimator, which is an
extension of the lasso regression estimator given by @code{McConville} et al.
(2017) <doi:10.1093/jssam/smw041>, and the regression tree estimator described
in @code{McConville} and Toth (2017) <@code{arXiv:1712.05708>}.  The variance
estimators which approximate the joint inclusion probabilities can be found in
Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the bootstrap
variance estimator is presented in Mashreghi et al. (2016)
<doi:10.1214/16-SS113>.")
    (license license:gpl2)))

(define-public r-masae
  (package
    (name "r-masae")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maSAE" version))
       (sha256
        (base32 "0453rvfhi4zvjhbkjammy81gxi6ax75gs8iqpnphbrv60agz0bjc"))))
    (properties `((upstream-name . "maSAE")))
    (build-system r-build-system)
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://gitlab.com/fvafrCU/maSAE")
    (synopsis "Mandallaz' Model-Assisted Small Area Estimators")
    (description
     "An S4 implementation of the unbiased extension of the model- assisted
synthetic-regression estimator proposed by Mandallaz (2013)
<DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013)
<DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>.
It yields smaller variances than the standard bias correction, the generalised
regression estimator.")
    (license license:bsd-2)))

(define-public r-mas
  (package
    (name "r-mas")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mas" version))
       (sha256
        (base32 "1d6ca7dj8bxrq0ka552sjb38j004bl1lzhfm14syr4h4wp08028m"))))
    (properties `((upstream-name . "mas")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=mas")
    (synopsis "Marker Association Studies")
    (description
     "Genome-wide association analysis that accommodate membership information,
variance adjustment, and correlated responses.")
    (license license:gpl3)))

(define-public r-marzic
  (package
    (name "r-marzic")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MarZIC" version))
       (sha256
        (base32 "16f09s5f95jsafd3s8w01vc9i98nlwm3abrwfdhymaallq4kcban"))))
    (properties `((upstream-name . "MarZIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-nlcoptim
                             r-mathjaxr
                             r-foreach
                             r-doparallel
                             r-dirmult
                             r-betareg))
    (native-inputs (list r-knitr))
    (home-page "https://www.mdpi.com/2073-4425/13/6/1049")
    (synopsis
     "Marginal Mediation Effects with Zero-Inflated Compositional Mediator")
    (description
     "This package provides a way to estimate and test marginal mediation effects for
zero-inflated compositional mediators.  Estimates of Natural Indirect Effect
(NIE), Natural Direct Effect (NDE) of each taxon, as well as their standard
errors and confident intervals, were provided as outputs.  Zeros will not be
imputed during analysis.  See Wu et al. (2022) <doi:10.3390/genes13061049>.")
    (license license:gpl2)))

(define-public r-marvel
  (package
    (name "r-marvel")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MARVEL" version))
       (sha256
        (base32 "1zsgj3dbqpfjv2x6xpp75n9jxf04svqffiy9gxlbxkns3clwqz5i"))))
    (properties `((upstream-name . "MARVEL")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-plyr r-matrix r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MARVEL")
    (synopsis "Revealing Splicing Dynamics at Single-Cell Resolution")
    (description
     "Alternative splicing represents an additional and underappreciated layer of
complexity underlying gene expression profiles.  Nevertheless, there remains
hitherto a paucity of software to investigate splicing dynamics at single-cell
resolution.  MARVEL enables splicing analysis of single-cell RNA-sequencing data
generated from plate- and droplet-based library preparation methods.")
    (license license:gpl3)))

(define-public r-marssvrhybrid
  (package
    (name "r-marssvrhybrid")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MARSSVRhybrid" version))
       (sha256
        (base32 "10b8d75q5hiqf8id1qsvjx379xyx4vphq5r1iwvykjaw4zxbyx65"))))
    (properties `((upstream-name . "MARSSVRhybrid")))
    (build-system r-build-system)
    (propagated-inputs (list r-earth r-e1071))
    (home-page "https://cran.r-project.org/package=MARSSVRhybrid")
    (synopsis "MARS SVR Hybrid")
    (description
     "Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression
(SVR) hybrid model is combined Machine learning hybrid approach which selects
important variables using MARS and then fits SVR on the extracted important
variables.")
    (license license:gpl3)))

(define-public r-marss
  (package
    (name "r-marss")
    (version "3.11.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MARSS" version))
       (sha256
        (base32 "0iipvlf6franhvbpg5c7wdnyzl9wl27xhnl1k66mi9m8ihjcczrm"))))
    (properties `((upstream-name . "MARSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mvtnorm r-kfas r-generics))
    (native-inputs (list r-knitr))
    (home-page "https://atsa-es.github.io/MARSS/")
    (synopsis "Multivariate Autoregressive State-Space Modeling")
    (description
     "The MARSS package provides maximum-likelihood parameter estimation for
constrained and unconstrained linear multivariate autoregressive state-space
(MARSS) models, including partially deterministic models.  MARSS models are a
class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.
 Fitting available via Expectation-Maximization (EM), BFGS (using optim), and
TMB (using the @code{marssTMB} companion package).  Functions are provided for
parametric and innovations bootstrapping, Kalman filtering and smoothing, model
selection criteria including bootstrap AICb, confidences intervals via the
Hessian approximation or bootstrapping, and all conditional residual types.  See
the user guide for examples of dynamic factor analysis, dynamic linear models,
outlier and shock detection, and multivariate AR-p models.  Online workshops
(lectures, @code{eBook}, and computer labs) at <https://atsa-es.github.io/>.")
    (license license:gpl2)))

(define-public r-marsgwr
  (package
    (name "r-marsgwr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MARSGWR" version))
       (sha256
        (base32 "1c64xlyy7bw4na3g0sqh9b0grkp3qhmp11cbsb7jv30gjwrkgj9b"))))
    (properties `((upstream-name . "MARSGWR")))
    (build-system r-build-system)
    (propagated-inputs (list r-qpdf r-numbers r-earth))
    (home-page "https://cran.r-project.org/package=MARSGWR")
    (synopsis
     "Hybrid Spatial Model for Capturing Spatially Varying Relationships Between Variables in the Data")
    (description
     "It is a hybrid spatial model that combines the strength of two widely used
regression models, MARS (Multivariate Adaptive Regression Splines) and GWR
(Geographically Weighted Regression) to provide an effective approach for
predicting a response variable at unknown locations.  The MARS model is used in
the first step of the development of a hybrid model to identify the most
important predictor variables that assist in predicting the response variable.
For method details see, Friedman, J.H. (1991). <DOI:10.1214/aos/1176347963>.The
GWR model is then used to predict the response variable at testing locations
based on these selected variables that account for spatial variations in the
relationships between the variables.  This hybrid model can improve the accuracy
of the predictions compared to using an individual model alone.This developed
hybrid spatial model can be useful particularly in cases where the relationship
between the response variable and predictor variables is complex and non-linear,
and varies across locations.")
    (license license:gpl2+)))

(define-public r-marsannhybrid
  (package
    (name "r-marsannhybrid")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MARSANNhybrid" version))
       (sha256
        (base32 "1xs0gjdjl9c5i1i8ds9s3sq0764267h6gngkbwimfa8miq287idm"))))
    (properties `((upstream-name . "MARSANNhybrid")))
    (build-system r-build-system)
    (propagated-inputs (list r-neuralnet r-earth))
    (home-page "https://cran.r-project.org/package=MARSANNhybrid")
    (synopsis "MARS Based ANN Hybrid Model")
    (description
     "Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network
(ANN) hybrid model is combined Machine learning hybrid approach which selects
important variables using MARS and then fits ANN on the extracted important
variables.")
    (license license:gpl3)))

(define-public r-marradistrees
  (package
    (name "r-marradistrees")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marradistrees" version))
       (sha256
        (base32 "1pl7mw354wczyplylk0w6k026ngf741dn4lq7cd87hnjcfifn86i"))))
    (properties `((upstream-name . "marradistrees")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=marradistrees")
    (synopsis
     "Plots a Tree-Like Representation of a Numerical Variable (Marradi's Tree)")
    (description
     "This package provides a single function plotting Marradi's trees: a graphical
representation of a numerical variable for comparing the variable mean and
standard deviation across subgroups.  See A. Marradi \"L'analisi monovariata\"
(1993, ISBN: 9788820496876).")
    (license license:gpl3)))

(define-public r-marquee
  (package
    (name "r-marquee")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marquee" version))
       (sha256
        (base32 "150rnn4pk8wc5h1gpb7grsyxxbf3xjjain03xfc0hnx8s6c3fjzg"))))
    (properties `((upstream-name . "marquee")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-textshaping
                             r-systemfonts
                             r-rlang
                             r-png
                             r-jpeg
                             r-glue
                             r-cpp11
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://marquee.r-lib.org")
    (synopsis "Markdown Parser and Renderer for R Graphics")
    (description
     "This package provides the mean to parse and render markdown text with grid along
with facilities to define the styling of the text.")
    (license license:expat)))

(define-public r-marqlevalg
  (package
    (name "r-marqlevalg")
    (version "2.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marqLevAlg" version))
       (sha256
        (base32 "172zr3jg48wr631l61811vd04llm3chirdhbp2mc8zryn3bbcd6n"))))
    (properties `((upstream-name . "marqLevAlg")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-doparallel))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=marqLevAlg")
    (synopsis
     "Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm")
    (description
     "This algorithm provides a numerical solution to the problem of unconstrained
local minimization (or maximization).  It is particularly suited for complex
problems and more efficient than the Gauss-Newton-like algorithm when starting
from points very far from the final minimum (or maximum).  Each iteration is
parallelized and convergence relies on a stringent stopping criterion based on
the first and second derivatives.  See Philipps et al, 2021
<doi:10.32614/RJ-2021-089>.")
    (license license:gpl2+)))

(define-public r-marp
  (package
    (name "r-marp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marp" version))
       (sha256
        (base32 "0vywqsvmhghldrpnh2dpkgz41r7q463whfhgrn38pg8jnyhliiwi"))))
    (properties `((upstream-name . "marp")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-statmod r-gtools))
    (home-page "https://github.com/kanji709/marp")
    (synopsis "Model-Averaged Renewal Process")
    (description
     "To implement a model-averaging approach with different renewal models, with a
primary focus on forecasting large earthquakes.  Based on six renewal models
(i.e., Poisson, Gamma, Log-Logistics, Weibull, Log-Normal and BPT),
model-averaged point estimates are calculated using AIC (or BIC) weights.
Additionally, both percentile and studentized bootstrapped model-averaged
confidence intervals are constructed.  In comparison, point and interval
estimation from the individual or \"best\" model (determined via model selection)
can be retrieved.")
    (license license:expat)))

(define-public r-marmot
  (package
    (name "r-marmot")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MARMoT" version))
       (sha256
        (base32 "0mykzfs7kyyxsmjzvp4gc8pz3z1j9vs5f81qlxhkmk96pqdjp4d9"))))
    (properties `((upstream-name . "MARMoT")))
    (build-system r-build-system)
    (propagated-inputs (list r-parsec))
    (home-page "https://cran.r-project.org/package=MARMoT")
    (synopsis
     "Matching on Poset-Based Average Rank for Multiple Treatments (MARMoT)")
    (description
     "It contains the function to apply MAR@code{MoT} balancing technique discussed
in: Silan, Boccuzzo, Arpino (2021) <DOI:10.1002/sim.9192>, Silan, Belloni,
Boccuzzo, (2023) <DOI:10.1007/s10260-023-00695-0>; furthermore it contains a
function for computing the Deloof's approximation of the average rank (and also
a parallelized version) and a function to compute the Absolute Standardized
Bias.")
    (license license:gpl3+)))

(define-public r-marmap
  (package
    (name "r-marmap")
    (version "1.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marmap" version))
       (sha256
        (base32 "0zmik6hpc44syknn7k9pbmy01jpnlp4sc9n3rhslrpnks39l0hvq"))))
    (properties `((upstream-name . "marmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-shape
                             r-rsqlite
                             r-reshape2
                             r-raster
                             r-plotrix
                             r-ncdf4
                             r-ggplot2
                             r-geosphere
                             r-gdistance
                             r-dbi
                             r-adehabitatma))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/ericpante/marmap")
    (synopsis "Import, Plot and Analyze Bathymetric and Topographic Data")
    (description
     "Import xyz data from the NOAA (National Oceanic and Atmospheric Administration,
<https://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans,
<https://www.gebco.net>) and other sources, plot xyz data to prepare
publication-ready figures, analyze xyz data to extract transects, get depth /
altitude based on geographical coordinates, or calculate z-constrained
least-cost paths.")
    (license license:gpl3+)))

(define-public r-marlod
  (package
    (name "r-marlod")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marlod" version))
       (sha256
        (base32 "1sncr219jbkl4vfazdrjj2rv714zy2jvzmywdfaxv7d1aakl2s5l"))))
    (properties `((upstream-name . "marlod")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg r-miwqs r-mass r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=marlod")
    (synopsis "Marginal Modeling for Exposure Data with Values Below the LOD")
    (description
     "This package provides functions of marginal mean and quantile regression models
are used to analyze environmental exposure and biomonitoring data with repeated
measurements and non-detects, as well as longitudinal exposure data that include
non-detects and time-dependent covariates.")
    (license license:gpl3)))

(define-public r-markowitzr
  (package
    (name "r-markowitzr")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MarkowitzR" version))
       (sha256
        (base32 "18bs6hbj8bhs1var72vd3gkzjjx80f5cyq0a6fm99fmsggw1lmqa"))))
    (properties `((upstream-name . "MarkowitzR")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-gtools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/shabbychef/MarkowitzR")
    (synopsis "Statistical Significance of the Markowitz Portfolio")
    (description
     "This package provides a collection of tools for analyzing significance of
Markowitz portfolios, using the delta method on the second moment matrix,
<arxiv:1312.0557>.")
    (license license:lgpl3)))

(define-public r-markowitz
  (package
    (name "r-markowitz")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markowitz" version))
       (sha256
        (base32 "03fifi14mskbs5yl5i48frswij0i7nkx3nlvf1k7rgz3abcs6v32"))))
    (properties `((upstream-name . "markowitz")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse r-tidyr r-magrittr r-dplyr))
    (home-page "https://github.com/luana1909/Markowitiz")
    (synopsis "Markowitz Criterion")
    (description
     "The Markowitz criterion is a multicriteria decision-making method that stands
out in risk and uncertainty analysis in contexts where probabilities are known.
This approach represents an evolution of Pascal's criterion by incorporating the
dimension of variability.  In this framework, the expected value reflects the
anticipated return, while the standard deviation serves as a measure of risk.
The markowitz package provides a practical and accessible tool for implementing
this method, enabling researchers and professionals to perform analyses without
complex calculations.  Thus, the package facilitates the application of the
Markowitz criterion.  More details on the method can be found in Octave
Jokung-NguÃ©na (2001, ISBN 2100055372).")
    (license license:gpl3)))

(define-public r-markovmsm
  (package
    (name "r-markovmsm")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markovMSM" version))
       (sha256
        (base32 "1ip96xl8xzh2mhrmd1kmjih7rm2lxf9k4ndf6wcq86r3jzqp4zf0"))))
    (properties `((upstream-name . "markovMSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mstate))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=markovMSM")
    (synopsis
     "Methods for Checking the Markov Condition in Multi-State Survival Data")
    (description
     "The inference in multi-state models is traditionally performed under a Markov
assumption that claims that past and future of the process are independent given
the present state.  In this package, we consider tests of the Markov assumption
that are applicable to general multi-state models.  Three approaches using
existing methodology are considered: a simple method based on including
covariates depending on the history in Cox models for the transition
intensities; methods based on measuring the discrepancy of the non-Markov
estimators of the transition probabilities to the Markov Aalen-Johansen
estimators; and, finally, methods that were developed by considering summaries
from families of log-rank statistics where patients are grouped by the state
occupied of the process at a particular time point (see Soutinho G,
Meira-Machado L (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H
(2020) <doi:10.1093/biostatistics/kxaa030>).")
    (license license:gpl3)))

(define-public r-markovmix
  (package
    (name "r-markovmix")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markovmix" version))
       (sha256
        (base32 "07fzyg7szs4662jww6xpdlvki5wc60g2aw16884ghd8h9ch625yi"))))
    (properties `((upstream-name . "markovmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-rcpp
                             r-purrr
                             r-pillar
                             r-forcats
                             r-dplyr))
    (home-page "https://github.com/zhuxr11/markovmix")
    (synopsis
     "Mixture of Markov Chains with Support of Higher Orders and Multiple Sequences")
    (description
     "Fit mixture of Markov chains of higher orders from multiple sequences.  It is
also compatible with ordinary 1-component, 1-order or single-sequence Markov
chains.  Various utility functions are provided to derive transition patterns,
transition probabilities per component and component priors.  In addition,
print(), predict() and component extracting/replacing methods are also defined
as a convention of mixture models.")
    (license license:expat)))

(define-public r-markovchart
  (package
    (name "r-markovchart")
    (version "2.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Markovchart" version))
       (sha256
        (base32 "0rvpj7zpcq2jv2sajnlhv36g76r3c3dlr0p1yyi96qi1q653yvjj"))))
    (properties `((upstream-name . "Markovchart")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimparallel r-metr r-ggplot2 r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Markovchart")
    (synopsis "Markov Chain-Based Cost-Optimal Control Charts")
    (description
     "This package provides functions for cost-optimal control charts with a focus on
health care applications.  Compared to assumptions in traditional control chart
theory, here, we allow random shift sizes, random repair and random sampling
times.  The package focuses on X-bar charts with a sample size of 1
(representing the monitoring of a single patient at a time).  The methods are
described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni
(2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019)
<http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-markovchain
  (package
    (name "r-markovchain")
    (version "0.9.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markovchain" version))
       (sha256
        (base32 "0z67i4j676gwvxyf1gw73x7jib7jm5jgyzqvami47mfvc3b2dygf"))))
    (properties `((upstream-name . "markovchain")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-igraph
                             r-expm))
    (native-inputs (list r-rticles r-rmarkdown r-knitr r-bookdown))
    (home-page "https://github.com/spedygiorgio/markovchain/")
    (synopsis "Easy Handling Discrete Time Markov Chains")
    (description
     "This package provides functions and S4 methods to create and manage discrete
time Markov chains more easily.  In addition functions to perform statistical
(fitting and drawing random variates) and probabilistic (analysis of their
structural proprieties) analysis are provided.  See Spedicato (2017)
<doi:10.32614/RJ-2017-036>.  Some functions for continuous times Markov chains
depends on the suggested ctmcd package, that, as May 2023, can be retrieved from
<https://cran.r-project.org/src/contrib/Archive/ctmcd/ctmcd_1.4.2.tar.gz>.")
    (license license:expat)))

(define-public r-markophylo
  (package
    (name "r-markophylo")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markophylo" version))
       (sha256
        (base32 "0w6csdvlybfx8azjsrz40mykafgqkl0w23bf2mm2ff5jh6cym66a"))))
    (properties `((upstream-name . "markophylo")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-phangorn
                             r-numderiv
                             r-geiger
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=markophylo")
    (synopsis "Markov Chain Models for Phylogenetic Trees")
    (description
     "Allows for fitting of maximum likelihood models using Markov chains on
phylogenetic trees for analysis of discrete character data.  Examples of such
discrete character data include restriction sites, gene family presence/absence,
intron presence/absence, and gene family size data.  Hypothesis-driven user-
specified substitution rate matrices can be estimated.  Allows for biologically
realistic models combining constrained substitution rate matrices, site rate
variation, site partitioning, branch-specific rates, allowing for non-stationary
prior root probabilities, correcting for sampling bias, etc.  See Dang and
Golding (2016) <doi:10.1093/bioinformatics/btv541> for more details.")
    (license license:gpl2+)))

(define-public r-markmyassignment
  (package
    (name "r-markmyassignment")
    (version "0.8.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markmyassignment" version))
       (sha256
        (base32 "07jhcw662ihx4ykmjsbrs6spvq0b2kdwd6slqzky3zk47dkrsv1r"))))
    (properties `((upstream-name . "markmyassignment")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-testthat
                             r-rlang
                             r-httr
                             r-codetools
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=markmyassignment")
    (synopsis "Automatic Marking of R Assignments")
    (description
     "Automatic marking of R assignments for students and teachers based on testthat
test suites.")
    (license license:bsd-2)))

(define-public r-markets
  (package
    (name "r-markets")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markets" version))
       (sha256
        (base32 "1by2cm3hlw6jgdd5l30frjm5q83llizp09z6v1ajmb51lkrdnqf5"))))
    (properties `((upstream-name . "markets")))
    (build-system r-build-system)
    (inputs (list tbb))
    (propagated-inputs (list r-rlang
                             r-rcppparallel
                             r-rcppgsl
                             r-rcpp
                             r-mass
                             r-formula
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pi-kappa-devel/markets/")
    (synopsis
     "Estimation Methods for Markets in Equilibrium and Disequilibrium")
    (description
     "This package provides estimation methods for markets in equilibrium and
disequilibrium.  Supports the estimation of an equilibrium and four
disequilibrium models with both correlated and independent shocks.  Also
provides post-estimation analysis tools, such as aggregation, marginal effect,
and shortage calculations.  See Karapanagiotis (2024)
<doi:10.18637/jss.v108.i02> for an overview of the functionality and examples.
The estimation methods are based on full information maximum likelihood
techniques given in Maddala and Nelson (1974) <doi:10.2307/1914215>.  They are
implemented using the analytic derivative expressions calculated in
Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>.  Standard errors can be
estimated by adjusting for heteroscedasticity or clustering.  The equilibrium
estimation constitutes a case of a system of linear, simultaneous equations.
Instead, the disequilibrium models replace the market-clearing condition with a
non-linear, short-side rule and allow for different specifications of price
dynamics.")
    (license license:expat)))

(define-public r-marketr
  (package
    (name "r-marketr")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marketr" version))
       (sha256
        (base32 "12nhlz40n80i6zg7y5w9zknq9qpk9qwd3wbvvya1ff8gq195147w"))))
    (properties `((upstream-name . "marketr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-tibble r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=marketr")
    (synopsis "Tidy Calculation of Marketing Metrics Plus Quick Analysis")
    (description
     "Facilitates tidy calculation of popular quantitative marketing metrics.  It also
includes functions for doing analysis that will help marketers and data analysts
better understand the drivers and/or trends of these metrics.  These metrics
include Customer Experience Index <https://go.forrester.com/analytics/cx-index/>
and Net Promoter Score <https://www.netpromoter.com/know/>.")
    (license license:cc0)))

(define-public r-marketmatching
  (package
    (name "r-marketmatching")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MarketMatching" version))
       (sha256
        (base32 "0ggs8b4zi1q0sqicjp8dzhhbwmdfypbmadajw6wjq0vi1ilylfb0"))))
    (properties `((upstream-name . "MarketMatching")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-utf8
                             r-tidyr
                             r-scales
                             r-reshape2
                             r-iterators
                             r-ggplot2
                             r-foreach
                             r-dtw
                             r-dplyr
                             r-doparallel
                             r-causalimpact
                             r-bsts
                             r-boom))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MarketMatching")
    (synopsis "Market Matching and Causal Impact Inference")
    (description
     "For a given test market find the best control markets using time series matching
and analyze the impact of an intervention.  The intervention could be a
marketing event or some other local business tactic that is being tested.  The
workflow implemented in the Market Matching package utilizes dynamic time
warping (the dtw package) to do the matching and the @code{CausalImpact} package
to analyze the causal impact.  In fact, this package can be considered a
\"workflow wrapper\" for those two packages.  In addition, if you don't have a
chosen set of test markets to match, the Market Matching package can provide
suggested test/control market pairs and pseudo prospective power analysis
(measuring causal impact at fake interventions).")
    (license license:gpl3+)))

(define-public r-markerpen
  (package
    (name "r-markerpen")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markerpen" version))
       (sha256
        (base32 "0i9md9vbqkxibjkn37givxzq8mq0232cvv2fyz6g8cj6flxm6vzr"))))
    (properties `((upstream-name . "markerpen")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-rcppeigen r-rcpp))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=markerpen")
    (synopsis
     "Marker Gene Detection via Penalized Principal Component Analysis")
    (description
     "Implementation of the @code{MarkerPen} algorithm, short for marker gene
detection via penalized principal component analysis, described in the paper by
Qiu, Wang, Lei, and Roeder (2020, <doi:10.1101/2020.11.07.373043>).
@code{MarkerPen} is a semi-supervised algorithm for detecting marker genes by
combining prior marker information with bulk transcriptome data.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-marked
  (package
    (name "r-marked")
    (version "1.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marked" version))
       (sha256
        (base32 "1i5vay60g6ddqnnqdj89yhsqxdcw9yzqpwq9gv7qc1psb8f77052"))))
    (properties `((upstream-name . "marked")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-tmb
                             r-rcpp
                             r-r2admb
                             r-optimx
                             r-numderiv
                             r-matrix
                             r-lme4
                             r-knitr
                             r-kableextra
                             r-expm
                             r-data-table
                             r-coda
                             r-bookdown))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=marked")
    (synopsis "Mark-Recapture Analysis for Survival and Abundance Estimation")
    (description
     "This package provides functions for fitting various models to capture-recapture
data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and
the multi-variate state model structure for survival estimation and POPAN
structured Jolly-Seber models for abundance estimation.  There are also Hidden
Markov model (HMM) implementations of CJS and multistate models with and without
state uncertainty and a simulation capability for HMM models.")
    (license license:gpl2+)))

(define-public r-markdowninput
  (package
    (name "r-markdowninput")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "markdownInput" version))
       (sha256
        (base32 "1qxil2liikbbn6w8niyc03fwqvwvdv6wiy98ip8j77729i7g2hln"))))
    (properties `((upstream-name . "markdownInput")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyace r-shiny r-markdown))
    (home-page "https://github.com/juliendiot42/markdownInput")
    (synopsis "Shiny Module for a Markdown Input with Result Preview")
    (description
     "An R-Shiny module containing a \"@code{markdownInput}\".  This input allows the
user to write some markdown code and to preview the result.  This input has been
inspired by the \"comment\" window of <https://github.com/>.")
    (license license:gpl3)))

(define-public r-mark
  (package
    (name "r-mark")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mark" version))
       (sha256
        (base32 "0azc4smx9l77wb8qdr8n4gzrngg3ffwqr4pb5g1p7gmch1w1s1pf"))))
    (properties `((upstream-name . "mark")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-fuj r-fs r-cli))
    (home-page "https://CRAN.R-project.org/package=mark")
    (synopsis "Miscellaneous, Analytic R Kernels")
    (description
     "Miscellaneous functions and wrappers for development in other packages created,
maintained by Jordan Mark Barbone.")
    (license license:expat)))

(define-public r-marima
  (package
    (name "r-marima")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marima" version))
       (sha256
        (base32 "03nvh93r4052li84yjqzc106gwh1542q9s192s980gs6sgy8scm6"))))
    (properties `((upstream-name . "marima")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=marima")
    (synopsis "Multivariate ARIMA and ARIMA-X Analysis")
    (description
     "Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm (marima())
and simulation (marima.sim()).")
    (license license:gpl2)))

(define-public r-margins
  (package
    (name "r-margins")
    (version "0.3.28")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "margins" version))
       (sha256
        (base32 "01wlx5f2kjc4n9md0zr9m73li6iwlngk1rdwifhhwc22hf4jijc9"))))
    (properties `((upstream-name . "margins")))
    (build-system r-build-system)
    (propagated-inputs (list r-prediction r-mass r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bbolker/margins")
    (synopsis "Marginal Effects for Model Objects")
    (description
     "An R port of the margins command from Stata', which can be used to calculate
marginal (or partial) effects from model objects.")
    (license license:expat)))

(define-public r-marginalmediation
  (package
    (name "r-marginalmediation")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MarginalMediation" version))
       (sha256
        (base32 "0a19ww8i04z8a4a6hia4myssfam57fdx59mc4n9dzrbyy293ckx2"))))
    (properties `((upstream-name . "MarginalMediation")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rstudioapi
                             r-purrr
                             r-magrittr
                             r-furniture
                             r-crayon
                             r-cli
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MarginalMediation")
    (synopsis "Marginal Mediation")
    (description
     "This package provides the ability to perform \"Marginal Mediation\"--mediation
wherein the indirect and direct effects are in terms of the average marginal
effects (Bartus, 2005,
<https://@code{EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>}).
 The style of the average marginal effects stems from Thomas Leeper's work on
the \"margins\" package.  This framework allows the use of categorical mediators
and outcomes with little change in interpretation from the continuous
mediators/outcomes.  See <doi:10.13140/RG.2.2.18465.92001> for more details on
the method.")
    (license license:gpl2)))

(define-public r-marginalizedrisk
  (package
    (name "r-marginalizedrisk")
    (version "2024.5-17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marginalizedRisk" version))
       (sha256
        (base32 "1f0qql4nvd5bkvkx0wn8w7fjzplabm5is8ms8rl6bpayfhaa6zai"))))
    (properties `((upstream-name . "marginalizedRisk")))
    (build-system r-build-system)
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=marginalizedRisk")
    (synopsis "Estimating Marginalized Risk")
    (description
     "Estimates risk as a function of a marker by integrating over other covariates in
a conditional risk model.")
    (license license:gpl2+)))

(define-public r-marginaleffects
  (package
    (name "r-marginaleffects")
    (version "0.23.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marginaleffects" version))
       (sha256
        (base32 "0q6zqasb99rlp4nnmbbnlpipl2xwqnpzlz3a85x6jvws050pj4ll"))))
    (properties `((upstream-name . "marginaleffects")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcppeigen
                             r-rcpp
                             r-insight
                             r-generics
                             r-data-table
                             r-checkmate))
    (home-page "https://marginaleffects.com/")
    (synopsis
     "Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests")
    (description
     "Compute and plot predictions, slopes, marginal means, and comparisons
(contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and
machine learning models in R. Conduct linear and non-linear hypothesis tests, or
equivalence tests.  Calculate uncertainty estimates using the delta method,
bootstrapping, or simulation-based inference.")
    (license license:gpl3+)))

(define-public r-margcond
  (package
    (name "r-margcond")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MargCond" version))
       (sha256
        (base32 "07qh1lgy8ds11cwlb4rxkl6aiph91zq29sy27b8wwxalycdgjrg8"))))
    (properties `((upstream-name . "MargCond")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-lme4 r-gee))
    (home-page "https://cran.r-project.org/package=MargCond")
    (synopsis "Joint Marginal-Conditional Model")
    (description
     "Fits joint marginal conditional models for multivariate longitudinal data, as in
Proudfoot, Faig, Natarajan, and Xu (2018) <doi:10.1002/sim.7552>.  Development
of this package was supported by the UCSD Altman Translational Research
Institute, NIH grant UL1TR001442.  The content is solely the responsibility of
the authors and does not necessarily represent the official views of the NIH.")
    (license license:gpl2)))

(define-public r-margaret
  (package
    (name "r-margaret")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "margaret" version))
       (sha256
        (base32 "1hyflwfw9pphcbxnx9wf0alqi17s5k3j7wvh95ka2h9fvyrpj11r"))))
    (properties `((upstream-name . "margaret")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-widyr
                             r-usethis
                             r-treemapify
                             r-tidyverse
                             r-tidytext
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-scholar
                             r-rvest
                             r-rlang
                             r-readr
                             r-purrr
                             r-lubridate
                             r-igraph
                             r-httr
                             r-dplyr
                             r-devtools))
    (home-page "https://github.com/coreofscience/margaret")
    (synopsis "Scientometric Analysis Minciencias")
    (description
     "The target of margaret is help to extract data from Minciencias to analyze
scientific production in Colombia.")
    (license license:expat)))

(define-public r-marg
  (package
    (name "r-marg")
    (version "1.2-2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marg" version))
       (sha256
        (base32 "1r6rl7dhvm8dghm7pm4hfw3924qja65hczl6d47v9ixnv4rxyzr9"))))
    (properties `((upstream-name . "marg")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-statmod))
    (home-page "https://www.r-project.org")
    (synopsis "Approximate Marginal Inference for Regression-Scale Models")
    (description
     "Likelihood inference based on higher order approximations for linear nonnormal
regression models.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file LICENCE")))))

(define-public r-mareymap
  (package
    (name "r-mareymap")
    (version "1.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MareyMap" version))
       (sha256
        (base32 "0d98pammyqa69cqgihinfbg2zay7akzvh92fy77s3vbdfnk8svg5"))))
    (properties `((upstream-name . "MareyMap")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MareyMap")
    (synopsis "Estimation of Meiotic Recombination Rates Using Marey Maps")
    (description
     "Local recombination rates are graphically estimated across a genome using Marey
maps.")
    (license license:gpl2+)))

(define-public r-marelac
  (package
    (name "r-marelac")
    (version "2.1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marelac" version))
       (sha256
        (base32 "0wl1z4sq61qq8d3szcd772rw35mlcrf7d9g8q9dr0znwvfj9gqdh"))))
    (properties `((upstream-name . "marelac")))
    (build-system r-build-system)
    (propagated-inputs (list r-shape r-seacarb))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=marelac")
    (synopsis "Tools for Aquatic Sciences")
    (description
     "Datasets, constants, conversion factors, and utilities for MArine', Riverine',
Estuarine', LAcustrine and Coastal science.  The package contains among others:
(1) chemical and physical constants and datasets, e.g. atomic weights, gas
constants, the earths bathymetry; (2) conversion factors (e.g. gram to mol to
liter, barometric units, temperature, salinity); (3) physical functions, e.g. to
estimate concentrations of conservative substances, gas transfer and diffusion
coefficients, the Coriolis force and gravity; (4) thermophysical properties of
the seawater, as from the UNESCO polynomial or from the more recent derivation
based on a Gibbs function.")
    (license license:gpl2+)))

(define-public r-marcher
  (package
    (name "r-marcher")
    (version "0.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marcher" version))
       (sha256
        (base32 "0hcxbfrwniih4kvx5sxhlffz1pc87kf52v8hvaf193rhhdykis5q"))))
    (properties `((upstream-name . "marcher")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-scales
                             r-rcolorbrewer
                             r-plyr
                             r-numderiv
                             r-mvtnorm
                             r-minpack-lm
                             r-matrix
                             r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=marcher")
    (synopsis "Migration and Range Change Estimation in R")
    (description
     "This package provides a set of tools for likelihood-based estimation, model
selection and testing of two- and three-range shift and migration models for
animal movement data as described in Gurarie et al. (2017) <doi:
10.1111/1365-2656.12674>.  Provided movement data (X, Y and Time), including
irregularly sampled data, functions estimate the time, duration and location of
one or two range shifts, as well as the ranging area and auto-correlation
structure of the movment.  Tests assess, for example, whether the shift was
\"significant\", and whether a two-shift migration was a true return migration.")
    (license license:gpl2)))

(define-public r-march
  (package
    (name "r-march")
    (version "3.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "march" version))
       (sha256
        (base32 "1qdjajb5mkq2r7p224kbxhf01s75hry5c5hx1cnsb48dfzd2s5xg"))))
    (properties `((upstream-name . "march")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=march")
    (synopsis "Markov Chains")
    (description
     "Computation of various Markovian models for categorical data including
homogeneous Markov chains of any order, MTD models, Hidden Markov models, and
Double Chain Markov Models.")
    (license license:gpl2)))

(define-public r-marble
  (package
    (name "r-marble")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "marble" version))
       (sha256
        (base32 "1jrk7dvb6ni950skdfqav40wr9s942w6fiysg8y1mk6q69aihzf6"))))
    (properties `((upstream-name . "marble")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/xilustat/marble")
    (synopsis
     "Robust Marginal Bayesian Variable Selection for Gene-Environment Interactions")
    (description
     "Recently, multiple marginal variable selection methods have been developed and
shown to be effective in Gene-Environment interactions studies.  We propose a
novel marginal Bayesian variable selection method for Gene-Environment
interactions studies.  In particular, our marginal Bayesian method is robust to
data contamination and outliers in the outcome variables.  With the
incorporation of spike-and-slab priors, we have implemented the Gibbs sampler
based on Markov Chain Monte Carlo.  The core algorithms of the package have been
developed in C++'.")
    (license license:gpl2)))

(define-public r-maraca
  (package
    (name "r-maraca")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maraca" version))
       (sha256
        (base32 "0sy5vmgkbcr1gi7l0hkdzdmibkgs1yvbgj14sfi4jc8zabwbpkz2"))))
    (properties `((upstream-name . "maraca")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-patchwork
                             r-lifecycle
                             r-hce
                             r-ggplot2
                             r-dplyr
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AstraZeneca/maraca")
    (synopsis
     "The Maraca Plot: Visualization of Hierarchical Composite Endpoints in Clinical Trials")
    (description
     "Library that supports visual interpretation of hierarchical composite endpoints
(HCEs).  HCEs are complex constructs used as primary endpoints in clinical
trials, combining outcomes of different types into ordinal endpoints, in which
each patient contributes the most clinically important event (one and only one)
to the analysis.  See Karpefors M et al. (2022) <doi:10.1177/17407745221134949>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-mar1s
  (package
    (name "r-mar1s")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mar1s" version))
       (sha256
        (base32 "1ljkvgxrramfx1rnp2rg6516mjxaa6s96amrnmqymnyjz7wlf37l"))))
    (properties `((upstream-name . "mar1s")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-fda r-cmrutils))
    (home-page "https://github.com/aparamon/mar1s")
    (synopsis "Multiplicative AR(1) with Seasonal Processes")
    (description
     "Multiplicative AR(1) with Seasonal is a stochastic process model built on top of
AR(1).  The package provides the following procedures for MAR(1)S processes:
fit, compose, decompose, advanced simulate and predict.")
    (license license:gpl3+)))

(define-public r-mar
  (package
    (name "r-mar")
    (version "1.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mAr" version))
       (sha256
        (base32 "1yrsa2k9v11b3jczblf7fwdmyyybbmychl5cqlfj309s986zry5d"))))
    (properties `((upstream-name . "mAr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=mAr")
    (synopsis "Multivariate AutoRegressive Analysis")
    (description
     "R functions for the estimation and eigen-decomposition of multivariate
autoregressive models.")
    (license license:gpl2+)))

(define-public r-maq
  (package
    (name "r-maq")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maq" version))
       (sha256
        (base32 "1rfwnwx9qglr49ymhhpglywrc6w2ilryiwxykxy0rdawml4zcw3j"))))
    (properties `((upstream-name . "maq")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/grf-labs/maq")
    (synopsis "Multi-Armed Qini")
    (description
     "Evaluate treatment rules for costly and mutually exclusive treatment arms with
Qini curves as proposed in Sverdrup, Wu, Athey, and Wager (2023)
<doi:10.48550/@code{arXiv.2306.11979>}.")
    (license license:expat)))

(define-public r-mapview
  (package
    (name "r-mapview")
    (version "2.11.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapview" version))
       (sha256
        (base32 "1c18fc8r5anc9wn541dzhchys2pmxc7pp797bq065brs5drpyka1"))))
    (properties `((upstream-name . "mapview")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-sf
                             r-servr
                             r-scales
                             r-satellite
                             r-raster
                             r-png
                             r-leafpop
                             r-leaflet
                             r-leafem
                             r-lattice
                             r-htmlwidgets
                             r-htmltools
                             r-base64enc))
    (home-page "https://github.com/r-spatial/mapview")
    (synopsis "Interactive Viewing of Spatial Data in R")
    (description
     "Quickly and conveniently create interactive visualisations of spatial data with
or without background maps.  Attributes of displayed features are fully
queryable via pop-up windows.  Additional functionality includes methods to
visualise true- and false-color raster images and bounding boxes.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-maptpx
  (package
    (name "r-maptpx")
    (version "1.9-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maptpx" version))
       (sha256
        (base32 "0f85i3vwr5rvz261z6j7x15gsahxrq4qqddfb0qyhypgihvwh857"))))
    (properties `((upstream-name . "maptpx")))
    (build-system r-build-system)
    (propagated-inputs (list r-slam))
    (home-page "http://taddylab.com")
    (synopsis "MAP Estimation of Topic Models")
    (description
     "Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet
Allocation) in text analysis, as described in Taddy (2012) On estimation and
selection for topic models'.  Previous versions of this code were included as
part of the textir package.  If you want to take advantage of openmp
parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.")
    (license license:gpl3)))

(define-public r-maptiles
  (package
    (name "r-maptiles")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maptiles" version))
       (sha256
        (base32 "064lxy2qzawbbdv69zpzkn2nbchvlcw4zjpxl5h44qrriivd60hh"))))
    (properties `((upstream-name . "maptiles")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-slippymath
                             r-sf
                             r-png
                             r-digest
                             r-curl))
    (home-page "https://github.com/riatelab/maptiles/")
    (synopsis "Download and Display Map Tiles")
    (description
     "To create maps from tiles, maptiles downloads, composes and displays tiles from
a large number of providers (e.g. @code{OpenStreetMap}', Stadia', Esri', CARTO',
or Thunderforest').")
    (license license:gpl3)))

(define-public r-mapstats
  (package
    (name "r-mapstats")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapStats" version))
       (sha256
        (base32 "1jf5dgf9i9339xzk9fbipy9b2fkwdv6sy1ciif921snwj176zck9"))))
    (properties `((upstream-name . "mapStats")))
    (build-system r-build-system)
    (propagated-inputs (list r-ttutils
                             r-survey
                             r-sp
                             r-sf
                             r-reshape2
                             r-rcolorbrewer
                             r-lattice
                             r-hmisc
                             r-colorspace
                             r-classint))
    (home-page "https://cran.r-project.org/package=mapStats")
    (synopsis "Geographic Display of Survey Data Statistics")
    (description
     "Automated calculation and visualization of survey data statistics on a
color-coded (choropleth) map.")
    (license license:gpl2+)))

(define-public r-mapsrinteractive
  (package
    (name "r-mapsrinteractive")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapsRinteractive" version))
       (sha256
        (base32 "0v2ijx54nyfgnxh77d47lsflvqfcbdzza3finfx3z1dwl6sdd056"))))
    (properties `((upstream-name . "mapsRinteractive")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-gstat))
    (home-page "https://CRAN.R-project.org/package=mapsRinteractive")
    (synopsis "Local Adaptation and Evaluation of Raster Maps")
    (description
     "Local adaptation and evaluation of maps of continuous attributes in raster
format by use of point location data.")
    (license license:expat)))

(define-public r-mapsperu
  (package
    (name "r-mapsperu")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapsPERU" version))
       (sha256
        (base32 "09z9hjpms039m8sjpb91q1f5knnncbyzciw7g5mhw11f9xsijj4k"))))
    (properties `((upstream-name . "mapsPERU")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/musajajorge/mapsPERU")
    (synopsis "Maps of Peru")
    (description
     "Information of the centroids and geographical limits of the regions,
departments, provinces and districts of Peru.")
    (license license:gpl3)))

(define-public r-mapspain
  (package
    (name "r-mapspain")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapSpain" version))
       (sha256
        (base32 "1jbwvw9r37yq32ynb6jq682ln6lh97k2vmc91g6sc1kqyc5kq059"))))
    (properties `((upstream-name . "mapSpain")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rappdirs r-giscor r-countrycode))
    (native-inputs (list r-knitr))
    (home-page "https://ropenspain.github.io/mapSpain/")
    (synopsis "Administrative Boundaries of Spain")
    (description
     "Administrative Boundaries of Spain at several levels (Autonomous Communities,
Provinces, Municipalities) based on the GISCO Eurostat database
<https://ec.europa.eu/eurostat/web/gisco> and @code{CartoBase} SIANE from
Instituto Geografico Nacional <https://www.ign.es/>.  It also provides a leaflet
plugin and the ability of downloading and processing static tiles.")
    (license license:gpl3)))

(define-public r-mapsfinland
  (package
    (name "r-mapsfinland")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapsFinland" version))
       (sha256
        (base32 "13kq22z0dpsq9dk6ni2rkgbkcxccr6dby3mnc3m5sj58am48zyky"))))
    (properties `((upstream-name . "mapsFinland")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mapsFinland")
    (synopsis "Maps of Finland")
    (description "Maps and other related data of Finland.")
    (license license:gpl2)))

(define-public r-mapsf
  (package
    (name "r-mapsf")
    (version "0.11.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapsf" version))
       (sha256
        (base32 "0vyy88fflkz5iaanbp77dxznw528ikr06q4q4srxcp0ylg71fq4m"))))
    (properties `((upstream-name . "mapsf")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-s2 r-maplegend r-classint))
    (native-inputs (list r-knitr))
    (home-page "https://riatelab.github.io/mapsf/")
    (synopsis "Thematic Cartography")
    (description
     "Create and integrate thematic maps in your workflow.  This package helps to
design various cartographic representations such as proportional symbols,
choropleth or typology maps.  It also offers several functions to display layout
elements that improve the graphic presentation of maps (e.g. scale bar, north
arrow, title, labels).  mapsf maps sf objects on base graphics.")
    (license license:gpl3+)))

(define-public r-mapscanner
  (package
    (name "r-mapscanner")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapscanner" version))
       (sha256
        (base32 "1fi8zsaylspbwxlh0sw82mhq8m4szl6sggvl3r1c2fwkpckn9qkn"))))
    (properties `((upstream-name . "mapscanner")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-slippymath
                             r-sf
                             r-rniftyreg
                             r-reproj
                             r-rcpp
                             r-raster
                             r-purrr
                             r-png
                             r-pdftools
                             r-memoise
                             r-magrittr
                             r-magick
                             r-glue
                             r-fs
                             r-curl
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/mapscanner")
    (synopsis "Print Maps, Draw on Them, Scan Them Back in")
    (description
     "Enables preparation of maps to be printed and drawn on.  Modified maps can then
be scanned back in, and hand-drawn marks converted to spatial objects.")
    (license (list license:gpl3 license:bsd-2))))

(define-public r-mapsapi
  (package
    (name "r-mapsapi")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapsapi" version))
       (sha256
        (base32 "0gk5k0zf3yzq7rzw17j7ph3fzpf6q6j777ri242wmi75xzkamrsc"))))
    (properties `((upstream-name . "mapsapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stars
                             r-sf
                             r-rgooglemaps
                             r-httr
                             r-bitops))
    (native-inputs (list r-knitr))
    (home-page "https://michaeldorman.github.io/mapsapi/")
    (synopsis "'sf'-Compatible Interface to 'Google Maps' APIs")
    (description
     "Interface to the Google Maps APIs: (1) routing directions based on the
Directions API, returned as sf objects, either as single feature per alternative
route, or a single feature per segment per alternative route; (2) travel
distance or time matrices based on the Distance Matrix API; (3) geocoded
locations based on the Geocode API, returned as sf objects, either points or
bounds; (4) map images using the Maps Static API, returned as stars objects.")
    (license license:expat)))

(define-public r-mappp
  (package
    (name "r-mappp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mappp" version))
       (sha256
        (base32 "19c8575w9r1w32hqwdmmggh21lqahy73vw8g5qbxxna6r77zg5c7"))))
    (properties `((upstream-name . "mappp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-purrr
                             r-progress
                             r-pbmcapply
                             r-parallelly
                             r-memoise))
    (home-page "https://github.com/cole-brokamp/mappp")
    (synopsis "Map in Parallel with Progress")
    (description
     "This package provides one function, which is a wrapper around purrr::map() with
some extras on top, including parallel computation, progress bars, error
handling, and result caching.")
    (license license:expat)))

(define-public r-mappoly
  (package
    (name "r-mappoly")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mappoly" version))
       (sha256
        (base32 "13wifh29i3g8m3gvw058nh3ma7fici2qijnpzb9ph27y67a3b6s9"))))
    (properties `((upstream-name . "mappoly")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-zoo
                             r-vcfr
                             r-smacof
                             r-rstudioapi
                             r-reshape2
                             r-rcurl
                             r-rcppparallel
                             r-rcpp
                             r-princurve
                             r-plotly
                             r-plot3d
                             r-magrittr
                             r-ggsci
                             r-ggpubr
                             r-ggplot2
                             r-fields
                             r-dplyr
                             r-dendextend
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mmollina/MAPpoly")
    (synopsis "Genetic Linkage Maps in Autopolyploids")
    (description
     "Construction of genetic maps in autopolyploid full-sib populations.  Uses
pairwise recombination fraction estimation as the first source of information to
sequentially position allelic variants in specific homologous chromosomes.  For
situations where pairwise analysis has limited power, the algorithm relies on
the multilocus likelihood obtained through a hidden Markov model (HMM).  For
more detail, please see Mollinari and Garcia (2019) <doi:10.1534/g3.119.400378>
and Mollinari et al. (2020) <doi:10.1534/g3.119.400620>.")
    (license license:gpl3)))

(define-public r-mappings
  (package
    (name "r-mappings")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mappings" version))
       (sha256
        (base32 "1f7m6nbdzdcxsk4m8rh8fzrck8pzxl2lgfz5c6wramwmp8rxczzb"))))
    (properties `((upstream-name . "mappings")))
    (build-system r-build-system)
    (home-page "https://github.com/benjaminrich/mappings")
    (synopsis "Functions for Transforming Categorical Variables")
    (description
     "Easily create functions to map between different sets of values, such as for
re-labelling categorical variables.")
    (license license:gpl3)))

(define-public r-mapping
  (package
    (name "r-mapping")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapping" version))
       (sha256
        (base32 "0c1kghgzkbicy35dgwbyw6085glagdnck4dkcj7z89i3mgrjm17s"))))
    (properties `((upstream-name . "mapping")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-tmaptools
                             r-tmap
                             r-stringr
                             r-stringi
                             r-sf
                             r-s2
                             r-mapview
                             r-leafsync
                             r-leafpop
                             r-leaflet
                             r-jsonlite
                             r-httr
                             r-htmltools
                             r-ggplot2
                             r-geojsonio
                             r-dplyr
                             r-curl
                             r-cartography))
    (home-page "https://mappinguniverse.github.io/mapping/index.html")
    (synopsis "Automatic Download, Linking, Manipulating Coordinates for Maps")
    (description
     "Maps are an important tool to visualise variables distribution across different
spatial objects.  The mapping process requires to link the data with coordinates
and then generate the correspondent map.  This package provide coordinates,
linking and mapping functions for an automatic, flexible and easy approach of
external functions.  The package provides an easy, flexible and automatic unit.
Geographical coordinates are provided in the package and automatically linked
with the input data to generate maps with internal provided functions or
external functions.  Provide an easy, flexible and automatic approach to
potentially download updated coordinates, to link statistical units with
coordinates and to aggregate variables based on the spatial hierarchy of units.
The object returned from the package can be used for thematic maps with the
build-in functions provided in mapping or with other packages already available.")
    (license license:gpl2+)))

(define-public r-mapperalgo
  (package
    (name "r-mapperalgo")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MapperAlgo" version))
       (sha256
        (base32 "0a4mn0hc2znys81cmydn4xsmcv5cgvjvm8hfjvlyjk89yv1nfrw5"))))
    (properties `((upstream-name . "MapperAlgo")))
    (build-system r-build-system)
    (home-page "https://github.com/kennywang112/MapperAlgo/")
    (synopsis "Topological Data Analysis: Mapper Algorithm")
    (description
     "The Mapper algorithm from Topological Data Analysis, the steps are as follows 1.
 Define a filter (lens) function on the data.  2.  Perform clustering within
each level set.  3.  Generate a complex from the clustering results.")
    (license license:expat)))

(define-public r-mapper
  (package
    (name "r-mapper")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mappeR" version))
       (sha256
        (base32 "1ybwpyh2qk9ik0wqc42yg0zkdpavxxvar94ar9k3xw3g6ivq3dzw"))))
    (properties `((upstream-name . "mappeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-fastcluster))
    (home-page "https://github.com/Uiowa-Applied-Topology/mappeR")
    (synopsis "Construct and Visualize TDA Mapper Graphs")
    (description
     "Topological data analysis (TDA) is a method of data analysis that uses
techniques from topology to analyze high-dimensional data.  Here we implement
Mapper, an algorithm from this area developed by Singh, MÃ©moli and Carlsson
(2007) which generalizes the concept of a Reeb graph
<https://en.wikipedia.org/wiki/Reeb_graph>.  The output graph is able to be
visualized in R using igraph or using a free network analysis software called
Cytoscape', available for download from at <https://cytoscape.org/>.")
    (license license:expat)))

(define-public r-mapmixture
  (package
    (name "r-mapmixture")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapmixture" version))
       (sha256
        (base32 "0wr7p5q0kin59cr9fayvbgb9c1jdm0nv8chy63nvznrhcw361xik"))))
    (properties `((upstream-name . "mapmixture")))
    (build-system r-build-system)
    (propagated-inputs (list r-waiter
                             r-tidyr
                             r-stringr
                             r-shinywidgets
                             r-shinyjs
                             r-shinyfeedback
                             r-shiny
                             r-sf
                             r-rnaturalearthdata
                             r-rlang
                             r-purrr
                             r-htmltools
                             r-ggspatial
                             r-ggplot2
                             r-dplyr
                             r-colourpicker
                             r-bslib))
    (home-page "https://github.com/Tom-Jenkins/mapmixture")
    (synopsis "Spatial Visualisation of Admixture on a Projected Map")
    (description
     "Visualise admixture as pie charts on a projected map, admixture as traditional
structure barplots or facet barplots, and scatter plots from genotype principal
components analysis.  A shiny app allows users to create admixture maps
interactively.  Jenkins TL (2024) <doi:10.1111/1755-0998.13943>.")
    (license license:gpl3+)))

(define-public r-mapmisc
  (package
    (name "r-mapmisc")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapmisc" version))
       (sha256
        (base32 "1syznp0kh4alv8p4sc6zmphkpqpk9si3sddfizqlprp3ypm5gi85"))))
    (properties `((upstream-name . "mapmisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-geosphere))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mapmisc")
    (synopsis "Utilities for Producing Maps")
    (description
     "This package provides a minimal, light-weight set of tools for producing nice
looking maps in R, with support for map projections.  See Brown (2016)
<doi:10.32614/RJ-2016-005>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-mapme-biodiversity
  (package
    (name "r-mapme-biodiversity")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapme.biodiversity" version))
       (sha256
        (base32 "031s6nnylrxa60in27vzjw3hvak360sxwim4v6j7d7x90kgcinl1"))))
    (properties `((upstream-name . "mapme.biodiversity")))
    (build-system r-build-system)
    (inputs (list proj gdal))
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-terra
                             r-sf
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-httr2
                             r-furrr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page
     "https://mapme-initiative.github.io/mapme.biodiversity/index.html")
    (synopsis "Efficient Monitoring of Global Biodiversity Portfolios")
    (description
     "Biodiversity areas, especially primary forest, serve a multitude of functions
for local economy, regional functionality of the ecosystems as well as the
global health of our planet.  Recently, adverse changes in human land use
practices and climatic responses to increased greenhouse gas emissions, put
these biodiversity areas under a variety of different threats.  The present
package helps to analyse a number of biodiversity indicators based on freely
available geographical datasets.  It supports computational efficient routines
that allow the analysis of potentially global biodiversity portfolios.  The
primary use case of the package is to support evidence based reporting of an
organization's effort to protect biodiversity areas under threat and to identify
regions were intervention is most duly needed.")
    (license license:gpl3+)))

(define-public r-maplegend
  (package
    (name "r-maplegend")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maplegend" version))
       (sha256
        (base32 "1jngbampps5gn3xdy292466qq4nhx0k7c08mams84xmmb9jbq0h0"))))
    (properties `((upstream-name . "maplegend")))
    (build-system r-build-system)
    (home-page "https://github.com/riatelab/maplegend/")
    (synopsis "Legends for Maps")
    (description
     "Create legends for maps and other graphics.  Thematic maps need to be
accompanied by legible legends to be fully comprehensible.  This package offers
a wide range of legends useful for cartography, some of which may also be useful
for other types of graphics.")
    (license license:gpl3)))

(define-public r-mapitr
  (package
    (name "r-mapitr")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAPITR" version))
       (sha256
        (base32 "0vmsdspja4qfs2z14p835g78zggk8h54v25cn268z1hg2p6zxish"))))
    (properties `((upstream-name . "MAPITR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-doparallel
                             r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mturchin20/MAPITR")
    (synopsis "MArginal ePIstasis Test for Regions")
    (description
     "This package provides a genetic analysis tool and variance component model for
identifying marginal epistasis between pathways and the rest of the genome.
MAPITR uses as input a matrix of genotypes, a vector of phenotypes, and a list
of pathways.  MAPITR then iteratively tests each pathway for epistasis between
any variants within the pathway versus any variants remaining in the rest of the
genome.  MAPITR returns results in the form of p-values for every pathway
indicating whether the null model of there being no epistatic interactions
between a pathway and the rest of the genome can be rejected.")
    (license license:expat)))

(define-public r-mapiso
  (package
    (name "r-mapiso")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapiso" version))
       (sha256
        (base32 "11mrvk45znna6irsd8mmkk8h63dm6wjlpsy0h8p3bldvk5mc8fi9"))))
    (properties `((upstream-name . "mapiso")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-isoband))
    (home-page "https://github.com/riatelab/mapiso")
    (synopsis "Create Contour Polygons from Regular Grids")
    (description
     "Regularly spaced grids containing continuous data are transformed to contour
polygons.  A grid can be defined by a data.frame (x, y, value), an sf object or
a raster from terra'.")
    (license license:gpl3+)))

(define-public r-mapinguari
  (package
    (name "r-mapinguari")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mapinguari" version))
       (sha256
        (base32 "19srflx5x33xifn56dl1xlzy4hi8gmmv6lgf2ffyyr2y5k8j2lqp"))))
    (properties `((upstream-name . "Mapinguari")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-stringr
                             r-rlang
                             r-raster
                             r-magrittr
                             r-dplyr))
    (home-page "https://github.com/gabrielhoc/Mapinguari")
    (synopsis "Process-Based Biogeographical Analysis")
    (description
     "Facilitates the incorporation of biological processes in biogeographical
analyses.  It offers conveniences in fitting, comparing and extrapolating models
of biological processes such as physiology and phenology.  These spatial
extrapolations can be informative by themselves, but also complement traditional
correlative species distribution models, by mixing environmental and
process-based predictors.  Caetano et al (2020) <doi:10.1111/oik.07123>.")
    (license license:gpl2)))

(define-public r-mapi
  (package
    (name "r-mapi")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapi" version))
       (sha256
        (base32 "1yljvapzkb43i2sbqsmn5aqp95hm8gjkz8m41x1chwyfddrzjsbw"))))
    (properties `((upstream-name . "mapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rcpp r-pbapply r-data-table))
    (home-page "https://www1.montpellier.inra.fr/CBGP/software/MAPI/")
    (synopsis "Mapping Averaged Pairwise Information")
    (description
     "Mapping Averaged Pairwise Information (MAPI) is an exploratory method providing
graphical representations summarizing the spatial variation of pairwise metrics
(eg.  distance, similarity coefficient, ...) computed between georeferenced
samples.")
    (license license:gpl3+)))

(define-public r-mapgl
  (package
    (name "r-mapgl")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapgl" version))
       (sha256
        (base32 "0rkj2zvf2hjq8pxbhd8j7nijms5hss5zawy4mnwgbx6vv7qbwcb2"))))
    (properties `((upstream-name . "mapgl")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sf
                             r-rlang
                             r-htmlwidgets
                             r-htmltools
                             r-geojsonsf
                             r-classint
                             r-base64enc))
    (home-page "https://walker-data.com/mapgl/")
    (synopsis "Interactive Maps with 'Mapbox GL JS' and 'MapLibre GL JS' in R")
    (description
     "This package provides an interface to the Mapbox GL JS
(<https://docs.mapbox.com/mapbox-gl-js/guides>) and the @code{MapLibre} GL JS
(<https://maplibre.org/maplibre-gl-js/docs/>) interactive mapping libraries to
help users create custom interactive maps in R. Users can create interactive
globe visualizations; layer sf objects to create filled maps, circle maps,
heatmaps', and three-dimensional graphics; and customize map styles and views.
The package also includes utilities to use Mapbox and @code{MapLibre} maps in
Shiny web applications.")
    (license license:expat)))

(define-public r-mapgam
  (package
    (name "r-mapgam")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MapGAM" version))
       (sha256
        (base32 "09y06fzw8ari3c06fiz8cl067c9frvfnc0zi484brb9nqlaq24rw"))))
    (properties `((upstream-name . "MapGAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sp
                             r-sf
                             r-pbsmapping
                             r-gam
                             r-colorspace))
    (home-page "https://cran.r-project.org/package=MapGAM")
    (synopsis "Mapping Smoothed Effect Estimates from Individual-Level Data")
    (description
     "This package contains functions for mapping odds ratios, hazard ratios, or other
effect estimates using individual-level data such as case-control study data,
using generalized additive models (GAMs) or Cox models for smoothing with a
two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures)
while adjusting linearly for confounding variables, using methods described by
Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020).
Includes convenient functions for mapping point estimates and confidence
intervals, efficient control sampling, and permutation tests for the null
hypothesis that the two-dimensional predictor is not associated with the outcome
variable (adjusting for confounders).")
    (license license:gpl3)))

(define-public r-mapfit
  (package
    (name "r-mapfit")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapfit" version))
       (sha256
        (base32 "0xdkc325y75mcx082b6ncp1n2mfq0yq67j2mkfdcpavnz6s8jdqg"))))
    (properties `((upstream-name . "mapfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-r6 r-matrix r-deformula))
    (home-page "https://github.com/okamumu/mapfit")
    (synopsis "PH/MAP Parameter Estimation")
    (description
     "Estimation methods for phase-type distribution (PH) and Markovian arrival
process (MAP) from empirical data (point and grouped data) and density function.
 The tool is based on the following researches: Okamura et al. (2009)
<doi:10.1109/TNET.2008.2008750>, Okamura and Dohi (2009)
<doi:10.1109/QEST.2009.28>, Okamura et al. (2011)
<doi:10.1016/j.peva.2011.04.001>, Okamura et al. (2013) <doi:10.1002/asmb.1919>,
Horvath and Okamura (2013) <doi:10.1007/978-3-642-40725-3_10>, Okamura and Dohi
(2016) <doi:10.15807/jorsj.59.72>.")
    (license license:expat)))

(define-public r-mapedit
  (package
    (name "r-mapedit")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapedit" version))
       (sha256
        (base32 "1ix0sjsqb6646xyi73am9ic97plw9y9p4pl8mrkpzvqzm0pxdhxx"))))
    (properties `((upstream-name . "mapedit")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-shiny
                             r-sf
                             r-scales
                             r-raster
                             r-miniui
                             r-mapview
                             r-leafpm
                             r-leaflet-extras
                             r-leaflet
                             r-leafem
                             r-jsonlite
                             r-htmlwidgets
                             r-htmltools
                             r-dplyr))
    (home-page "https://github.com/r-spatial/mapedit")
    (synopsis "Interactive Editing of Spatial Data in R")
    (description
     "Suite of interactive functions and helpers for selecting and editing geospatial
data.")
    (license license:expat)))

(define-public r-mapdeck
  (package
    (name "r-mapdeck")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapdeck" version))
       (sha256
        (base32 "17z3fd0k5qlmy7yzdlbi105kadak86nwmb6zw39rd10in6j3vfc2"))))
    (properties `((upstream-name . "mapdeck")))
    (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-spatialwidget
                             r-shiny
                             r-sfheaders
                             r-rcpp
                             r-rapidjsonr
                             r-magrittr
                             r-jsonify
                             r-interleave
                             r-htmlwidgets
                             r-googlepolylines
                             r-geometries
                             r-geojsonsf
                             r-colourvalues
                             r-bh))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://symbolixau.github.io/mapdeck/articles/mapdeck.html")
    (synopsis "Interactive Maps Using 'Mapbox GL JS' and 'Deck.gl'")
    (description
     "This package provides a mechanism to plot an interactive map using Mapbox GL
(<https://docs.mapbox.com/mapbox-gl-js/api/>), a javascript library for
interactive maps, and Deck.gl (<https://deck.gl/>), a javascript library which
uses @code{WebGL} for visualising large data sets.")
    (license license:gpl3)))

(define-public r-mapchina
  (package
    (name "r-mapchina")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapchina" version))
       (sha256
        (base32 "0f6ayq4izqxcj0hi2vfg0pc239hxgg7abb5476v5mfjjyi156mni"))))
    (properties `((upstream-name . "mapchina")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf))
    (home-page "https://github.com/xmc811/mapchina")
    (synopsis "China Administrative Divisions Geospatial Shapefile Data")
    (description
     "Geospatial shapefile data of China administrative divisions to the
county/district-level.")
    (license license:gpl3)))

(define-public r-mapcan
  (package
    (name "r-mapcan")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapcan" version))
       (sha256
        (base32 "1a4135wc1h4nzcv6xq0bg75i744wyq8p7q0ix7y2x2gb6s35w46k"))))
    (properties `((upstream-name . "mapcan")))
    (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=mapcan")
    (synopsis
     "Tools for Plotting Canadian Choropleth Maps and Choropleth Alternatives")
    (description
     "This package provides a variety of functions that make it easy to plot standard
choropleth maps as well as choropleth alternatives in ggplot2'.")
    (license license:expat)))

(define-public r-mapboxer
  (package
    (name "r-mapboxer")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapboxer" version))
       (sha256
        (base32 "0f873js87dgk8nqnhpxh88svvsbnk81vkm7gycb62mkr4xqsgqs7"))))
    (properties `((upstream-name . "mapboxer")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-purrr
                             r-magrittr
                             r-htmlwidgets
                             r-htmltools
                             r-geojsonsf))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/crazycapivara/mapboxer")
    (synopsis "An R Interface to 'Mapbox GL JS'")
    (description
     "Makes Mapbox GL JS <https://docs.mapbox.com/mapbox-gl-js/api/>, an open source
@code{JavaScript} library that uses @code{WebGL} to render interactive maps,
available within R via the htmlwidgets package.  Visualizations can be used from
the R console, in R Markdown documents and in Shiny apps.")
    (license license:expat)))

(define-public r-mapboxapi
  (package
    (name "r-mapboxapi")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapboxapi" version))
       (sha256
        (base32 "1sf01288m3hix7mgkp529vasnmxs1qw508v9i5w931nla03wsl3h"))))
    (properties `((upstream-name . "mapboxapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-tidyr
                             r-stringi
                             r-slippymath
                             r-sf
                             r-rlang
                             r-raster
                             r-purrr
                             r-protolite
                             r-png
                             r-magick
                             r-leaflet
                             r-jsonlite
                             r-jpeg
                             r-httr
                             r-htmltools
                             r-geojsonsf
                             r-dplyr
                             r-curl
                             r-aws-s3))
    (home-page "https://github.com/walkerke/mapboxapi")
    (synopsis "R Interface to 'Mapbox' Web Services")
    (description
     "Includes support for Mapbox Navigation APIs, including directions, isochrones,
and route optimization; the Search API for forward and reverse geocoding; the
Maps API for interacting with Mapbox vector tilesets and visualizing Mapbox maps
in R; and Mapbox Tiling Service and tippecanoe for generating map tiles.  See
<https://docs.mapbox.com/api/> for more information about the Mapbox APIs.")
    (license license:expat)))

(define-public r-mapbayr
  (package
    (name "r-mapbayr")
    (version "0.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapbayr" version))
       (sha256
        (base32 "07bl6vp86c7ydwln130d2ccvizwjvd7zq1l4ifl1nspbh6p0i359"))))
    (properties `((upstream-name . "mapbayr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-progress
                             r-mrgsolve
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/FelicienLL/mapbayr")
    (synopsis "MAP-Bayesian Estimation of PK Parameters")
    (description
     "This package performs maximum a posteriori Bayesian estimation of individual
pharmacokinetic parameters from a model defined in mrgsolve', typically for
model-based therapeutic drug monitoring.  Internally computes an objective
function value from model and data, performs optimization and returns
predictions in a convenient format.  The performance of the package was
described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.")
    (license license:gpl3)))

(define-public r-mapaccuracy
  (package
    (name "r-mapaccuracy")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mapaccuracy" version))
       (sha256
        (base32 "1bz1c346y70wzv900lvf63j59f3sk9fdf1nh8p81fz4f5dbck1qp"))))
    (properties `((upstream-name . "mapaccuracy")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mapaccuracy")
    (synopsis "Unbiased Thematic Map Accuracy and Area")
    (description
     "Unbiased estimators of overall and per-class thematic map accuracy and area
published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman
(2014) <doi:10.1080/01431161.2014.930207>.")
    (license license:expat)))

(define-public r-mapa
  (package
    (name "r-mapa")
    (version "2.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAPA" version))
       (sha256
        (base32 "1f50fkxsdisndxh03ylzqipwh2kr51dai6z8xjhiv28h9ymgz9br"))))
    (properties `((upstream-name . "MAPA")))
    (build-system r-build-system)
    (propagated-inputs (list r-smooth r-rcolorbrewer r-forecast))
    (home-page
     "https://kourentzes.com/forecasting/2014/04/19/multiple-aggregation-prediction-algorithm-mapa/")
    (synopsis "Multiple Aggregation Prediction Algorithm")
    (description
     "This package provides functions and wrappers for using the Multiple Aggregation
Prediction Algorithm (MAPA) for time series forecasting.  MAPA models and
forecasts time series at multiple temporal aggregation levels, thus
strengthening and attenuating the various time series components for better
holistic estimation of its structure.  For details see Kourentzes et al. (2014)
<doi:10.1016/j.ijforecast.2013.09.006>.")
    (license license:gpl2+)))

(define-public r-map2ncbi
  (package
    (name "r-map2ncbi")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Map2NCBI" version))
       (sha256
        (base32 "1ssqnm9g5p0nnhdyp9dqbbsz3hw8jzdwy0rv3csp8fxm0f1bi1kb"))))
    (properties `((upstream-name . "Map2NCBI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rentrez))
    (home-page "https://cran.r-project.org/package=Map2NCBI")
    (synopsis "Mapping Markers to the Nearest Genomic Feature")
    (description
     "Allows the user to generate a list of features (gene, pseudo, RNA, CDS, and/or
UTR) directly from NCBI database for any species with a current build available.
 Option to save downloaded and formatted files is available, and the user can
prioritize the feature list based on type and assembly builds present in the
current build used.  The user can then use the list of features generated or
provide a list to map a set of markers (designed for SNP markers with a single
base pair position available) to the closest feature based on the map build.
This function does require map positions of the markers to be provided and the
positions should be based on the build being queried through NCBI.")
    (license license:gpl2+)))

(define-public r-map
  (package
    (name "r-map")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAP" version))
       (sha256
        (base32 "19d0m30jh50ibbdgvcvq1vsz5i1vf8qvl0d0nbqklvnndch61jhk"))))
    (properties `((upstream-name . "MAP")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-flexmix))
    (home-page "https://cran.r-project.org/package=MAP")
    (synopsis "Multimodal Automated Phenotyping")
    (description
     "Electronic health records (EHR) linked with biorepositories are a powerful
platform for translational studies.  A major bottleneck exists in the ability to
phenotype patients accurately and efficiently.  Towards that end, we developed
an automated high-throughput phenotyping method integrating International
Classification of Diseases (ICD) codes and narrative data extracted using
natural language processing (NLP).  Specifically, our proposed method, called
MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent mixture
models on aggregated ICD and NLP counts along with healthcare utilization.  The
MAP algorithm yields a predicted probability of phenotype for each patient and a
threshold for classifying subjects with phenotype yes/no (See Katherine P. Liao,
et al. (2019) <doi:10.1101/587436>.).")
    (license license:gpl2)))

(define-public r-maotai
  (package
    (name "r-maotai")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maotai" version))
       (sha256
        (base32 "1axr16xs366011vkx3j8mljq98923kscz8wrl9m1zprw3pqif6jr"))))
    (properties `((upstream-name . "maotai")))
    (build-system r-build-system)
    (propagated-inputs (list r-shapes
                             r-rtsne
                             r-rspectra
                             r-rdpack
                             r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-rann
                             r-pracma
                             r-matrix
                             r-labdsv
                             r-fastcluster
                             r-dbscan
                             r-cluster))
    (home-page "https://github.com/kisungyou/maotai")
    (synopsis "Tools for Matrix Algebra, Optimization and Inference")
    (description
     "Matrix is an universal and sometimes primary object/unit in applied mathematics
and statistics.  We provide a number of algorithms for selected problems in
optimization and statistical inference.  For general exposition to the topic
with focus on statistical context, see the book by Banerjee and Roy (2014,
ISBN:9781420095388).")
    (license license:expat)))

(define-public r-maoea
  (package
    (name "r-maoea")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MaOEA" version))
       (sha256
        (base32 "0s8jvp7821mb6xff8xwnh78mhz4qfrbk4g7sp4dfnm2ii7ah9c6b"))))
    (properties `((upstream-name . "MaOEA")))
    (build-system r-build-system)
    (inputs (list python-numpy))
    (propagated-inputs (list r-stringr
                             r-reticulate
                             r-randtoolbox
                             r-pracma
                             r-nsga2r
                             r-nnet
                             r-mass
                             r-lhs
                             r-gtools
                             r-e1071))
    (home-page "https://github.com/dots26/MaOEA")
    (synopsis "Many Objective Evolutionary Algorithm")
    (description
     "This package provides a set of evolutionary algorithms to solve many-objective
optimization.  Hybridization between the algorithms are also facilitated.
Available algorithms are: SMS-EMOA <doi:10.1016/j.ejor.2006.08.008> NSGA-III
<doi:10.1109/TEVC.2013.2281535> MO-CMA-ES <doi:10.1145/1830483.1830573> The
following many-objective benchmark problems are also provided: DTLZ1'-'DTLZ4
from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and WFG4'-'WFG9 from
Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.")
    (license license:gpl3+)))

(define-public r-manytests
  (package
    (name "r-manytests")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ManyTests" version))
       (sha256
        (base32 "1mi7wvnkcd95126f1h8vl8skn397yd2zqvcswprqar54p161wgyi"))))
    (properties `((upstream-name . "ManyTests")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ManyTests")
    (synopsis
     "Multiple Testing Procedures of Cox (2011) and Wong and Cox (2007)")
    (description
     "This package performs the multiple testing procedures of Cox (2011)
<doi:10.5170/CERN-2011-006> and Wong and Cox (2007)
<doi:10.1080/02664760701240014>.")
    (license license:gpl2)))

(define-public r-manynet
  (package
    (name "r-manynet")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manynet" version))
       (sha256
        (base32 "191jbsj9bxiblvrj469ms5dfr7xxsdsdn13ffi9p41n1hlymklpf"))))
    (properties `((upstream-name . "manynet")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidygraph
                             r-pillar
                             r-network
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (home-page "https://stocnet.github.io/manynet/")
    (synopsis
     "Many Ways to Make, Modify, Map, Mark, and Measure Myriad Networks")
    (description
     "Many tools for making, modifying, mapping, marking, measuring, and motifs and
memberships of many different types of networks.  All functions operate with
matrices, edge lists, and igraph', network', and tidygraph objects, and on
one-mode, two-mode (bipartite), and sometimes three-mode networks.  The package
includes functions for importing and exporting, creating and generating
networks, modifying networks and node and tie attributes, and describing and
visualizing networks with sensible defaults.")
    (license license:expat)))

(define-public r-manymome-table
  (package
    (name "r-manymome-table")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manymome.table" version))
       (sha256
        (base32 "01znxw82k2ycp8cncbayn0c4jhkydd5nvydfyxv1bwgixdkad1ff"))))
    (properties `((upstream-name . "manymome.table")))
    (build-system r-build-system)
    (propagated-inputs (list r-manymome r-flextable))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/manymome.table/")
    (synopsis "Publication-Ready Tables for 'manymome' Results")
    (description
     "Converts results from the manymome package, presented in Cheung and Cheung
(2023) <doi:10.3758/s13428-023-02224-z>, to publication-ready tables.")
    (license license:gpl3+)))

(define-public r-manymome
  (package
    (name "r-manymome")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manymome" version))
       (sha256
        (base32 "19gxwxjl29323dq5idgwyirkzwszy3bqdh9lv8i3938bjkh8bz3y"))))
    (properties `((upstream-name . "manymome")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply
                             r-mass
                             r-lavaan
                             r-igraph
                             r-ggplot2
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/manymome/")
    (synopsis
     "Mediation, Moderation and Moderated-Mediation After Model Fitting")
    (description
     "Computes indirect effects, conditional effects, and conditional indirect effects
in a structural equation model or path model after model fitting, with no need
to define any user parameters or label any paths in the model syntax, using the
approach presented in Cheung and Cheung (2023) <doi:10.3758/s13428-023-02224-z>.
 Can also form bootstrap confidence intervals by doing bootstrapping only once
and reusing the bootstrap estimates in all subsequent computations.  Supports
bootstrap confidence intervals for standardized (partially or completely)
indirect effects, conditional effects, and conditional indirect effects as
described in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau,
Hui, and Vong (2022) <doi:10.1037/hea0001188>.  Model fitting can be done by
structural equation modeling using lavaan() or regression using lm().")
    (license license:gpl3+)))

(define-public r-manymodelr
  (package
    (name "r-manymodelr")
    (version "0.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manymodelr" version))
       (sha256
        (base32 "0kc4y9i21qkcg0pkwlas2mm06s54s70w5ybw4215qjwiy6xax268"))))
    (properties `((upstream-name . "manymodelr")))
    (build-system r-build-system)
    (propagated-inputs (list r-metrics
                             r-lme4
                             r-ggplot2
                             r-e1071
                             r-dplyr
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Nelson-Gon/manymodelr")
    (synopsis "Build and Tune Several Models")
    (description
     "Frequently one needs a convenient way to build and tune several models in one
go.The goal is to provide a number of machine learning convenience functions.
It provides the ability to build, tune and obtain predictions of several models
in one function.  The models are built using functions from caret with easier to
read syntax.  Kuhn(2014) <@code{arXiv:1405.6974>}.")
    (license license:gpl2)))

(define-public r-manydata
  (package
    (name "r-manydata")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manydata" version))
       (sha256
        (base32 "18v7sm5i3xnffcv3p17xb5hx0dnlyarahv6b7j0208d7hsjg2pf0"))))
    (properties `((upstream-name . "manydata")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-usethis
                             r-tidyr
                             r-stringr
                             r-remotes
                             r-purrr
                             r-plyr
                             r-messydates
                             r-jsonlite
                             r-httr
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/globalgov/manydata")
    (synopsis "Portal for Global Governance Data")
    (description
     "This is the core package for the many packages universe.  It includes functions
to help researchers work with and contribute to event datasets on global
governance.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-mantaid
  (package
    (name "r-mantaid")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MantaID" version))
       (sha256
        (base32 "0myy75psf2v22rl4ajg73sh4y7fvkfjcvdkbwi4bjg6rd1r5j49g"))))
    (properties `((upstream-name . "MantaID")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-scutr
                             r-reshape2
                             r-rcolorbrewer
                             r-purrr
                             r-paradox
                             r-mlr3tuning
                             r-mlr3
                             r-magrittr
                             r-keras
                             r-ggplot2
                             r-ggcorrplot
                             r-dplyr
                             r-data-table
                             r-caret
                             r-biomart))
    (home-page "https://molaison.github.io/MantaID/")
    (synopsis
     "Machine-Learning Based Tool to Automate the Identification of Biological Database IDs")
    (description
     "The number of biological databases is growing rapidly, but different databases
use different IDs to refer to the same biological entity.  The inconsistency in
IDs impedes the integration of various types of biological data.  To resolve the
problem, we developed @code{MantaID}', a data-driven, machine-learning based
approach that automates identifying IDs on a large scale.  The @code{MantaID}
model's prediction accuracy was proven to be 99%, and it correctly and
effectively predicted 100,000 ID entries within two minutes. @code{MantaID}
supports the discovery and exploitation of ID patterns from large quantities of
databases. (e.g., up to 542 biological databases).  An easy-to-use freely
available open-source software R package, a user-friendly web application, and
API were also developed for @code{MantaID} to improve applicability.  To our
knowledge, @code{MantaID} is the first tool that enables an automatic, quick,
accurate, and comprehensive identification of large quantities of IDs, and can
therefore be used as a starting point to facilitate the complex assimilation and
aggregation of biological data across diverse databases.")
    (license license:gpl3+)))

(define-public r-manta
  (package
    (name "r-manta")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manta" version))
       (sha256
        (base32 "1qlmpg4afplhi4m3maws0njx11am28f7hry9rb3k9mxlvss6cr66"))))
    (properties `((upstream-name . "manta")))
    (build-system r-build-system)
    (home-page "https://github.com/dgarrimar/manta")
    (synopsis "Multivariate Asymptotic Non-Parametric Test of Association")
    (description
     "The Multivariate Asymptotic Non-parametric Test of Association (MANTA) enables
non-parametric, asymptotic P-value computation for multivariate linear models.
MANTA relies on the asymptotic null distribution of the PERMANOVA test
statistic.  P-values are computed using a highly accurate approximation of the
corresponding cumulative distribution function.  Garrido-MartÃ­n et al. (2022)
<doi:10.1101/2022.06.06.493041>.")
    (license license:gpl3)))

(define-public r-manova-rm
  (package
    (name "r-manova-rm")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MANOVA.RM" version))
       (sha256
        (base32 "03y6qzbh696h282pq8as2zy16y7j95d04qvn3labivd5qygfwfhp"))))
    (properties `((upstream-name . "MANOVA.RM")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr
                             r-plotrix
                             r-multcomp
                             r-matrix
                             r-mass
                             r-magic
                             r-ellipse
                             r-data-table))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/smn74/MANOVA.RM")
    (synopsis
     "Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs")
    (description
     "Implemented are various tests for semi-parametric repeated measures and general
MANOVA designs that do neither assume multivariate normality nor covariance
homogeneity, i.e., the procedures are applicable for a wide range of general
multivariate factorial designs.  In addition to asymptotic inference methods,
novel bootstrap and permutation approaches are implemented as well.  These
provide more accurate results in case of small to moderate sample sizes.
Furthermore, post-hoc comparisons are provided for the multivariate analyses.
Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-manorm2
  (package
    (name "r-manorm2")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAnorm2" version))
       (sha256
        (base32 "0bs6x09mm8ln2c7himavm6bskdkxvx7id4dxpg20k4a5hcqpj3ab"))))
    (properties `((upstream-name . "MAnorm2")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-scales r-locfit))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tushiqi/MAnorm2")
    (synopsis "Tools for Normalizing and Comparing ChIP-seq Samples")
    (description
     "Chromatin immunoprecipitation followed by high-throughput sequencing
(@code{ChIP-seq}) is the premier technology for profiling genome-wide
localization of chromatin-binding proteins, including transcription factors and
histones with various modifications.  This package provides a robust method for
normalizing @code{ChIP-seq} signals across individual samples or groups of
samples.  It also designs a self-contained system of statistical models for
calling differential @code{ChIP-seq} signals between two or more biological
conditions as well as for calling hypervariable @code{ChIP-seq} signals across
samples.  Refer to Tu et al. (2021) <doi:10.1101/gr.262675.120> and Chen et al.
(2022) <doi:10.1186/s13059-022-02627-9> for associated statistical details.")
    (license license:gpl3)))

(define-public r-manlymix
  (package
    (name "r-manlymix")
    (version "0.1.15.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ManlyMix" version))
       (sha256
        (base32 "1f39y2xq9vn060v4myynwsrxx9f40vn4kq16jzn76lj99shvcvkg"))))
    (properties `((upstream-name . "ManlyMix")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ManlyMix")
    (synopsis "Manly Mixture Modeling and Model-Based Clustering")
    (description
     "The utility of this package includes finite mixture modeling and model-based
clustering through Manly mixture models by Zhu and Melnykov (2016)
<DOI:10.1016/j.csda.2016.01.015>.  It also provides capabilities for forward and
backward model selection procedures.")
    (license license:gpl2+)))

(define-public r-manipulate
  (package
    (name "r-manipulate")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manipulate" version))
       (sha256
        (base32 "1klknqdfppi5lf6zbda3r2aqzsghabcsaxmvd3vw3cy3aa984zky"))))
    (properties `((upstream-name . "manipulate")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=manipulate")
    (synopsis "Interactive Plots for RStudio")
    (description
     "Interactive plotting functions for use within RStudio.  The manipulate function
accepts a plotting expression and a set of controls (e.g. slider, picker,
checkbox, or button) which are used to dynamically change values within the
expression.  When a value is changed using its corresponding control the
expression is automatically re-executed and the plot is redrawn.")
    (license license:gpl2)))

(define-public r-manifoldoptim
  (package
    (name "r-manifoldoptim")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ManifoldOptim" version))
       (sha256
        (base32 "0vp064ayniwqc8wck9n5y7s5y22x2jq55n8l5q4vjb8q7idvy6xd"))))
    (properties `((upstream-name . "ManifoldOptim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=ManifoldOptim")
    (synopsis
     "An R Interface to the 'ROPTLIB' Library for Riemannian Manifold Optimization")
    (description
     "An R interface to version 0.3 of the ROPTLIB optimization library (see
<https://www.math.fsu.edu/~whuang2/> for more information).  Optimize real-
valued functions over manifolds such as Stiefel, Grassmann, and Symmetric
Positive Definite matrices.  For details see Martin et.  al. (2020)
<doi:10.18637/jss.v093.i01>.  Note that the optional ldr package used in some of
this package's examples can be obtained from either JSS
<https://www.jstatsoft.org/index.php/jss/article/view/v061i03/2886> or from the
CRAN archives
<https://cran.r-project.org/src/contrib/Archive/ldr/ldr_1.3.3.tar.gz>.")
    (license license:gpl2+)))

(define-public r-manifold
  (package
    (name "r-manifold")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manifold" version))
       (sha256
        (base32 "1amvx0f0q4s2qgn7q23gp967bcm0djbnb8bmf1bf3fm3iyn432hv"))))
    (properties `((upstream-name . "manifold")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=manifold")
    (synopsis "Operations for Riemannian Manifolds")
    (description
     "This package implements operations for Riemannian manifolds, e.g., geodesic
distance, Riemannian metric, exponential and logarithm maps, etc.  Also
incorporates random object generator on the manifolds.  See Dai, Lin, and
MÃ¼ller (2021) <doi:10.1111/biom.13385>.")
    (license license:gpl2+)))

(define-public r-manifestor
  (package
    (name "r-manifestor")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manifestoR" version))
       (sha256
        (base32 "1ghzhsd7apib775h2nmv33mvwg5yqwp5w9bn7alppgfa16yznf31"))))
    (properties `((upstream-name . "manifestoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tm
                             r-tidyselect
                             r-tibble
                             r-readr
                             r-purrr
                             r-psych
                             r-nlp
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-htmlwidgets
                             r-htmltools
                             r-functional
                             r-dt
                             r-dplyr
                             r-base64enc))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/ManifestoProject/manifestoR")
    (synopsis "Access and Process Data and Documents of the Manifesto Project")
    (description
     "This package provides access to coded election programmes from the Manifesto
Corpus and to the Manifesto Project's Main Dataset and routines to analyse this
data.  The Manifesto Project <https://manifesto-project.wzb.eu> collects and
analyses election programmes across time and space to measure the political
preferences of parties.  The Manifesto Corpus contains the collected and
annotated election programmes in the Corpus format of the package tm to enable
easy use of text processing and text mining functionality.  Specific functions
for scaling of coded political texts are included.")
    (license license:gpl3+)))

(define-public r-manhplot
  (package
    (name "r-manhplot")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manhplot" version))
       (sha256
        (base32 "17yw10gpfp6bqmilb09v61jd2amz6cv1z38si4nqnhbvmac065qd"))))
    (properties `((upstream-name . "manhplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-gridextra r-ggrepel r-ggplot2))
    (home-page "https://github.com/cgrace1978/manhplot/")
    (synopsis "The Manhattan++ Plot")
    (description
     "This plot integrates annotation into a manhattan plot.  The plot is implemented
as a heatmap, which is binned using -log10(p-value) and chromosome position.
Annotation currently supported is minor allele frequency and gene function high
impact variants.")
    (license license:gpl2+)))

(define-public r-manhattanly
  (package
    (name "r-manhattanly")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manhattanly" version))
       (sha256
        (base32 "007qvfzq8fyvnirywfpl6n1gqxxizz32z4xbgnnx7riyb7nxl1qh"))))
    (properties `((upstream-name . "manhattanly")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly r-magrittr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sahirbhatnagar/manhattanly/")
    (synopsis "Interactive Q-Q and Manhattan Plots Using 'plotly.js'")
    (description
     "Create interactive manhattan, Q-Q and volcano plots that are usable from the R
console, in Dash apps, in the RStudio viewer pane, in R Markdown documents, and
in Shiny apps.  Hover the mouse pointer over a point to show details or drag a
rectangle to zoom.  A manhattan plot is a popular graphical method for
visualizing results from high-dimensional data analysis such as a (epi)genome
wide association study (GWAS or EWAS), in which p-values, Z-scores, test
statistics are plotted on a scatter plot against their genomic position.
Manhattan plots are used for visualizing potential regions of interest in the
genome that are associated with a phenotype.  Interactive manhattan plots allow
the inspection of specific value (e.g. rs number or gene name) by hovering the
mouse over a cell, as well as zooming into a region of the genome (e.g. a
chromosome) by dragging a rectangle around the relevant area.  This work is
based on the qqman package and the plotly.js engine.  It produces similar
manhattan and Q-Q plots as the manhattan and qq functions in the qqman package,
with the advantage of including extra annotation information and interactive
web-based visualizations directly from R. Once uploaded to a plotly account,
plotly graphs (and the data behind them) can be viewed and modified in a web
browser.")
    (license license:expat)))

(define-public r-mangrove
  (package
    (name "r-mangrove")
    (version "1.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Mangrove" version))
       (sha256
        (base32 "1qf3fjzf0sb6fqbfdbxwbw6wah3gg2qran4mgg13hcmk4n3j0hxp"))))
    (properties `((upstream-name . "Mangrove")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Mangrove")
    (synopsis "Risk Prediction on Trees")
    (description
     "This package provides methods for performing genetic risk prediction from
genotype data.  You can use it to perform risk prediction for individuals, or
for families with missing data.")
    (license license:gpl2+)))

(define-public r-manet
  (package
    (name "r-manet")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "manet" version))
       (sha256
        (base32 "1dnacgkdjmn7yiqk71qx5jgfipbpvqfdnrdd8wm0b9p8kgw4x7n4"))))
    (properties `((upstream-name . "manet")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack r-mclust r-igraph r-combinat))
    (home-page "https://cran.r-project.org/package=manet")
    (synopsis "Multiple Allocation Model for Actor-Event Networks")
    (description
     "Mixture model with overlapping clusters for binary actor-event data.  Parameters
are estimated in a Bayesian framework.  Model and inference are described in
Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture
model under overlapping clusters.  Submitted.")
    (license license:gpl2)))

(define-public r-mandelbrot
  (package
    (name "r-mandelbrot")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mandelbrot" version))
       (sha256
        (base32 "04a288jj8h1a04r584a02wg9dm3c6zifhqwj964ybsvgb4m6g99s"))))
    (properties `((upstream-name . "mandelbrot")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2))
    (home-page "https://cran.r-project.org/package=mandelbrot")
    (synopsis "Generates Views on the Mandelbrot Set")
    (description "Estimates membership for the Mandelbrot set.")
    (license license:expat)))

(define-public r-mandalar
  (package
    (name "r-mandalar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MandalaR" version))
       (sha256
        (base32 "13shj6r416cz11qlwrha468gx1y76vx28xyhap3k0c0b2jxn699b"))))
    (properties `((upstream-name . "MandalaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://lucianealcoforado.shinyapps.io/Mandala/")
    (synopsis
     "Building Mandalas from Parametric Equations of Classical Curves")
    (description
     "This package provides an algorithm for creating mandalas.  From the perspective
of classic mathematical curves and rigid movements on the plane, the package
allows you to select curves and produce mandalas from the curve.  The algorithm
was developed based on the book by Alcoforado et.  al.  entitled \"Art, Geometry
and Mandalas with R\" (2022) in press by the USP Open Books Portal.")
    (license license:gpl3)))

(define-public r-mand
  (package
    (name "r-mand")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mand" version))
       (sha256
        (base32 "0058jghm1gby68cbf57lg5qrfzypamp1z2wjdygl2p06v2ihlgp0"))))
    (properties `((upstream-name . "mand")))
    (build-system r-build-system)
    (propagated-inputs (list r-oro-nifti r-oro-dicom r-msma r-imager r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mand")
    (synopsis "Multivariate Analysis for Neuroimaging Data")
    (description
     "Several functions can be used to analyze neuroimaging data using multivariate
methods based on the msma package.  The functions used in the book entitled
\"Multivariate Analysis for Neuroimaging Data\" (2021, ISBN-13: 978-0367255329)
are contained.")
    (license license:gpl2+)))

(define-public r-mancie
  (package
    (name "r-mancie")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MANCIE" version))
       (sha256
        (base32 "0940xl3z5bca6hcnj2bj341l79wajilxlxzmyz3dlgrz0b3bbdmm"))))
    (properties `((upstream-name . "MANCIE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MANCIE")
    (synopsis
     "Matrix Analysis and Normalization by Concordant Information Enhancement")
    (description
     "High-dimensional data integration is a critical but difficult problem in
genomics research because of potential biases from high-throughput experiments.
We present MANCIE, a computational method for integrating two genomic data sets
with homogenous dimensions from different sources based on a PCA procedure as an
approximation to a Bayesian approach.")
    (license license:gpl2)))

(define-public r-managelocalrepo
  (package
    (name "r-managelocalrepo")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "managelocalrepo" version))
       (sha256
        (base32 "180b7ikas1kb7phm4l2z1d8wi45wi0qyz2c8rl8ml3f71b4mlzgc"))))
    (properties `((upstream-name . "managelocalrepo")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-assertthat))
    (home-page "https://cran.r-project.org/package=managelocalrepo")
    (synopsis "Manage a CRAN-Style Local Repository")
    (description
     "This will allow easier management of a CRAN-style repository on local networks
(i.e.  not on CRAN).  This might be necessary where hosted packages contain
intellectual property owned by a corporation.")
    (license license:gpl2)))

(define-public r-managedcloudprovider
  (package
    (name "r-managedcloudprovider")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ManagedCloudProvider" version))
       (sha256
        (base32 "0glhx9ylpx2s8mi711gblapw6qnv80nd4wahph65g6ddngvmgv73"))))
    (properties `((upstream-name . "ManagedCloudProvider")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-dockerparallel r-adagio))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Jiefei-Wang/ManagedCloudProvider")
    (synopsis
     "Providing the Kubernetes-Like Functions for the Non-Kubernetes Cloud Service")
    (description
     "Providing the kubernetes-like class @code{ManagedCloudProvider} as a child class
of the @code{CloudProvider} class in the @code{DockerParallel} package.  The
class is able to manage the cloud instance made by the non-kubernetes cloud
service.  For creating a provider for the non-kubernetes cloud service, the
developer needs to define a reference class inherited from
@code{ManagedCloudProvider} and define the method for the generics
@code{runDockerWorkerContainers}(), @code{getDockerWorkerStatus}() and
@code{killDockerWorkerContainers}().  For more information, please see the
vignette in this package and
<https://CRAN.R-project.org/package=@code{DockerParallel>}.")
    (license license:gpl3)))

(define-public r-mams
  (package
    (name "r-mams")
    (version "3.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAMS" version))
       (sha256
        (base32 "1w55vysw5n1wapaz0x3zyyzlyn7l1764d32b09p45yzfv2wdwr6v"))))
    (properties `((upstream-name . "MAMS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-future-apply r-future r-cli))
    (home-page "https://tjaki.github.io/MAMS/")
    (synopsis "Designing Multi-Arm Multi-Stage Studies")
    (description
     "Designing multi-arm multi-stage studies with (asymptotically) normal endpoints
and known variance.")
    (license license:gpl2)))

(define-public r-malvinas
  (package
    (name "r-malvinas")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "malvinas" version))
       (sha256
        (base32 "1afgyrn7cslrd4qxd9k5l3kyhdi99vx06360yhb9n005xfmd65kf"))))
    (properties `((upstream-name . "malvinas")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=malvinas")
    (synopsis "Islas Malvinas, Georgias Del Sur y SÃ¡ndwich Del Sur")
    (description
     "Data sets related to the Islas Malvinas /// Sets de datos relacionados a las
Islas Malvinas - La NaciÃ³n Argentina ratifica su legÃ­tima e imprescriptible
soberanÃ­a sobre las islas Malvinas, Georgias del Sur y SÃ¡ndwich del Sur y los
espacios marÃ­timos e insulares correspondientes, por ser parte integrante del
territorio nacional.  La recuperaciÃ³n de dichos territorios y el ejercicio
pleno de la soberanÃ­a, respetando el modo de vida de sus habitantes y conforme
a los principios del Derecho Internacional, constituyen un objetivo permanente e
irrenunciable del pueblo argentino.")
    (license license:gpl3)))

(define-public r-mallet
  (package
    (name "r-mallet")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mallet" version))
       (sha256
        (base32 "1g54nq01qgmi25lgdpsw044nyschcj7wnaaqhj72hax8yl4s8vqi"))))
    (properties `((upstream-name . "mallet")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava r-checkmate))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/mimno/RMallet")
    (synopsis "An R Wrapper for the Java Mallet Topic Modeling Toolkit")
    (description
     "An R interface for the Java Machine Learning for Language Toolkit (mallet)
<http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such as
Latent Dirichlet Allocation.  We can use the R package to read textual data into
mallet from R objects, run the Java implementation of mallet directly in R, and
extract results as R objects.  The Mallet toolkit has many functions, this
wrapper focuses on the topic modeling sub-package written by David Mimno.  The
package uses the @code{rJava} package to connect to a JVM.")
    (license license:expat)))

(define-public r-maldirppa
  (package
    (name "r-maldirppa")
    (version "1.1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MALDIrppa" version))
       (sha256
        (base32 "1afwkc8dyq51z610z7wnhm95f5j04yj90pxxa33mhdnm2dwsc963"))))
    (properties `((upstream-name . "MALDIrppa")))
    (build-system r-build-system)
    (propagated-inputs (list r-waveslim r-signal r-robustbase r-maldiquant
                             r-lattice))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Japal/MALDIrppa")
    (synopsis
     "MALDI Mass Spectrometry Data Robust Pre-Processing and Analysis")
    (description
     "This package provides methods for quality control and robust pre-processing and
analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018)
<doi:10.1093/bioinformatics/btx628>).")
    (license license:gpl2+)))

(define-public r-maldiquantforeign
  (package
    (name "r-maldiquantforeign")
    (version "0.14.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MALDIquantForeign" version))
       (sha256
        (base32 "0504a755mccz174g5zcldblwbyg3y11hpxxwpj9bhw937h55zxng"))))
    (properties `((upstream-name . "MALDIquantForeign")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-readmzxmldata
                             r-readbrukerflexdata
                             r-maldiquant
                             r-digest
                             r-base64enc))
    (native-inputs (list r-knitr))
    (home-page "https://strimmerlab.github.io/software/maldiquant/")
    (synopsis "Import/Export Routines for 'MALDIquant'")
    (description
     "This package provides functions for reading (tab, csv, Bruker fid, Ciphergen
XML, @code{mzXML}, @code{mzML}, @code{imzML}, Analyze 7.5, CDF, @code{mMass}
MSD) and writing (tab, csv, @code{mMass} MSD, @code{mzML}, @code{imzML})
different file formats of mass spectrometry data into/from MALDIquant objects.")
    (license license:gpl3+)))

(define-public r-maldipickr
  (package
    (name "r-maldipickr")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maldipickr" version))
       (sha256
        (base32 "1324dgmkigdynkph52afwyn5iycw37ndl6jfsqjgarm9hyr72dy2"))))
    (properties `((upstream-name . "maldipickr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-readbrukerflexdata
                             r-maldiquant
                             r-magrittr
                             r-lifecycle
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ClavelLab/maldipickr")
    (synopsis "Dereplicate and Cherry-Pick Mass Spectrometry Spectra")
    (description
     "Convenient wrapper functions for the analysis of matrix-assisted laser
desorption/ionization-time-of-flight (MALDI-TOF) spectra data in order to select
only representative spectra (also called cherry-pick).  The package covers the
preprocessing and dereplication steps (based on Strejcek, Smrhova, Junkova and
Uhlik (2018) <doi:10.3389/fmicb.2018.01294>) needed to cluster MALDI-TOF spectra
before the final cherry-picking step.  It enables the easy exclusion of spectra
and/or clusters to accommodate complex cherry-picking strategies.
Alternatively, cherry-picking using taxonomic identification MALDI-TOF data is
made easy with functions to import inconsistently formatted reports.")
    (license license:gpl3+)))

(define-public r-maldicellassay
  (package
    (name "r-maldicellassay")
    (version "0.4.47")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MALDIcellassay" version))
       (sha256
        (base32 "0bxspf3i57qz5hybjh3hfasn95m8k3ijfi2js1rsnz8pn9x6isqb"))))
    (properties `((upstream-name . "MALDIcellassay")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-svmisc
                             r-scales
                             r-purrr
                             r-nplr
                             r-maldiquantforeign
                             r-maldiquant
                             r-ggplot2
                             r-forcats
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CeMOS-Mannheim/MALDIcellassay")
    (synopsis "Automated MALDI Cell Assays Using Dose-Response Curve Fitting")
    (description
     "Conduct automated cell-based assays using Matrix-Assisted Laser
Desorption/Ionization (MALDI) methods for high-throughput screening of signals
responsive to treatments.  The package efficiently identifies high variance
signals and fits dose-response curves to them.  Quality metrics such as Z', V',
log2FC, and CRS are provided for evaluating the potential of signals as
biomarkers.  The methodologies were introduced by Weigt et al. (2018)
<doi:10.1038/s41598-018-29677-z> and refined by Unger et al. (2021)
<doi:10.1038/s41596-021-00624-z>.")
    (license license:expat)))

(define-public r-malaytextr
  (package
    (name "r-malaytextr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "malaytextr" version))
       (sha256
        (base32 "0z5q0hl07yh528xgvzc820rdaaihgilm6ql934jh0ciswil022sm"))))
    (properties `((upstream-name . "malaytextr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rlang r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zahiernasrudin/malaytextr")
    (synopsis "Text Mining for Bahasa Malaysia")
    (description
     "It is designed to work with text written in Bahasa Malaysia.  We provide
functions and data sets that will make working with Bahasa Malaysia text much
easier.  For word stemming in particular, we will look up the Malay words in a
dictionary and then proceed to remove \"extra suffix\" as explained in Khan,
Rehman Ullah, Fitri Suraya Mohamad, Muh Inam @code{UlHaq}, Shahren Ahmad Zadi
Adruce, Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh
Al-Hababi (2017) <https://ijrest.net/vol-4-issue-12.html> .  This package
includes a dictionary of Malay words that may be used to perform word stemming,
a dataset of Malay stop words, a dataset of sentiment words and a dataset of
normalized words.")
    (license license:expat)))

(define-public r-malariaatlas
  (package
    (name "r-malariaatlas")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "malariaAtlas" version))
       (sha256
        (base32 "1ipjhds73pcw30y3ks9glqlxng97fnavvpi1v9s01jr7m0l587xs"))))
    (properties `((upstream-name . "malariaAtlas")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyterra
                             r-tidyr
                             r-terra
                             r-stringr
                             r-sf
                             r-rlang
                             r-ows4r
                             r-lubridate
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-gridextra
                             r-ggplot2
                             r-ggnewscale
                             r-future-apply
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/malaria-atlas-project/malariaAtlas")
    (synopsis
     "An R Interface to Open-Access Malaria Data, Hosted by the 'Malaria Atlas Project'")
    (description
     "This package provides a suite of tools to allow you to download all publicly
available parasite rate survey points, mosquito occurrence points and raster
surfaces from the Malaria Atlas Project <https://malariaatlas.org/> servers as
well as utility functions for plotting the downloaded data.")
    (license license:expat)))

(define-public r-malani
  (package
    (name "r-malani")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "malani" version))
       (sha256
        (base32 "1jps28gfkiysr356ackxf1my5xz6rpx3a2bv68pziy2614hrqbs9"))))
    (properties `((upstream-name . "malani")))
    (build-system r-build-system)
    (propagated-inputs (list r-e1071))
    (home-page "https://cran.r-project.org/package=malani")
    (synopsis "Machine Learning Assisted Network Inference")
    (description
     "Find dark genes.  These genes are often disregarded due to no detected mutation
or differential expression, but are important in coordinating the functionality
in cancer networks.")
    (license license:gpl3)))

(define-public r-malan
  (package
    (name "r-malan")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "malan" version))
       (sha256
        (base32 "1m11rxzmvkdvpkcnhjwwgrhkmbylwjpqnn47zyy5wna66c3zzvr6"))))
    (properties `((upstream-name . "malan")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidygraph
                             r-tibble
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-magrittr
                             r-igraph
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=malan")
    (synopsis "MAle Lineage ANalysis")
    (description
     "MAle Lineage ANalysis by simulating genealogies backwards and imposing short
tandem repeats (STR) mutations forwards.  Intended for forensic Y chromosomal
STR (Y-STR) haplotype analyses.  Numerous analyses are possible, e.g. number of
matches and meiotic distance to matches.  Refer to papers mentioned in
citation(\"malan\") (DOI's: <doi:10.1371/journal.pgen.1007028>,
<doi:10.21105/joss.00684> and <doi:10.1016/j.fsigen.2018.10.004>).")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-makl
  (package
    (name "r-makl")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAKL" version))
       (sha256
        (base32 "0xljmnwl8v3y68xmfm5vbycsa5y5yi0p0n207hg7zjvfaf6gbfhj"))))
    (properties `((upstream-name . "MAKL")))
    (build-system r-build-system)
    (propagated-inputs (list r-grplasso r-auc))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MAKL")
    (synopsis "Multiple Approximate Kernel Learning (MAKL)")
    (description
     "R package associated with the Multiple Approximate Kernel Learning (MAKL)
algorithm proposed in <doi:10.1093/bioinformatics/btac241>.  The algorithm fits
multiple approximate kernel learning (MAKL) models that are fast, scalable and
interpretable.")
    (license license:gpl3+)))

(define-public r-makeunique
  (package
    (name "r-makeunique")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makeunique" version))
       (sha256
        (base32 "0pf8cl6p3q9fzj20s4gm8amw29291xf6711v0pmp0rcnwva1zav0"))))
    (properties `((upstream-name . "makeunique")))
    (build-system r-build-system)
    (home-page "https://github.com/selkamand/makeunique")
    (synopsis "Make Character Strings Unique")
    (description
     "Make all elements of a character vector unique.  Differs from make.unique by
starting at 1 and allowing users to customise suffix format.")
    (license license:expat)))

(define-public r-maketools
  (package
    (name "r-maketools")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maketools" version))
       (sha256
        (base32 "03ng6lfs1phi41zvpdc2mp336c4smb7882hh76nis5qn8njmpf3b"))))
    (properties `((upstream-name . "maketools")))
    (build-system r-build-system)
    (propagated-inputs (list r-sys))
    (native-inputs (list r-knitr))
    (home-page "https://jeroen.r-universe.dev/maketools")
    (synopsis "Exploring and Testing the Toolchain and System Libraries")
    (description
     "Helper functions that interface with the system utilities to learn about the
local build environment.  Lets you explore make rules to test the local
configuration, or query pkg-config to find compiler flags and libs needed for
building packages with external dependencies.  Also contains tools to analyze
which libraries that a installed R package linked to by inspecting output from
ldd in combination with information from your distribution package manager, e.g.
rpm or dpkg'.")
    (license license:expat)))

(define-public r-makeproject
  (package
    (name "r-makeproject")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makeProject" version))
       (sha256
        (base32 "09q8xa5j4s5spgzzr3y06l3xis93lqxlx0q66s2nczrhd8nrz3ca"))))
    (properties `((upstream-name . "makeProject")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=makeProject")
    (synopsis "Creates an empty package framework for the LCFD format")
    (description
     "This package creates an empty framework of files and directories for the \"Load,
Clean, Func, Do\" structure described by Josh Reich.")
    (license license:gpl3)))

(define-public r-makepipe
  (package
    (name "r-makepipe")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makepipe" version))
       (sha256
        (base32 "0mhabnmpvjamgam63nrjdimn94azn8crafr9jcwsr08lzlbv9kmv"))))
    (properties `((upstream-name . "makepipe")))
    (build-system r-build-system)
    (propagated-inputs (list r-roxygen2 r-r6 r-nomnoml r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://kinto-b.github.io/makepipe/")
    (synopsis "Pipeline Tools Inspired by 'GNU Make'")
    (description
     "This package provides a suite of tools for transforming an existing workflow
into a self-documenting pipeline with very minimal upfront costs.  Segments of
the pipeline are specified in much the same way a Make rule is, by declaring an
executable recipe (which might be an R script), along with the corresponding
targets and dependencies.  When the entire pipeline is run through, only those
recipes that need to be executed will be.  Meanwhile, execution metadata is
captured behind the scenes for later inspection.")
    (license license:gpl3+)))

(define-public r-makepalette
  (package
    (name "r-makepalette")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makePalette" version))
       (sha256
        (base32 "0ma8dm7x07wwm94ginsah778lkwqb3nlqpydmn7g22zvbariail7"))))
    (properties `((upstream-name . "makePalette")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-prismatic r-cluster))
    (home-page "https://github.com/musajajorge/makePalette")
    (synopsis "Make Palette")
    (description
     "This package provides functions that allow you to create your own color palette
from an image, using mathematical algorithms.")
    (license license:gpl3)))

(define-public r-makemyprior
  (package
    (name "r-makemyprior")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makemyprior" version))
       (sha256
        (base32 "03njj91hwmfpxwv3srjfylcnv02h17f5dnbawnrqxrgakpwsxib4"))))
    (properties `((upstream-name . "makemyprior")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-shinyjs
                             r-shinybs
                             r-shiny
                             r-rlang
                             r-matrix
                             r-mass
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ingebogh/makemyprior")
    (synopsis "Intuitive Construction of Joint Priors for Variance Parameters")
    (description
     "Tool for easy prior construction and visualization.  It helps to formulates
joint prior distributions for variance parameters in latent Gaussian models.
The resulting prior is robust and can be created in an intuitive way.  A
graphical user interface (GUI) can be used to choose the joint prior, where the
user can click through the model and select priors.  An extensive guide is
available in the GUI. The package allows for direct inference with the specified
model and prior.  Using a hierarchical variance decomposition, we formulate a
joint variance prior that takes the whole model structure into account.  In this
way, existing knowledge can intuitively be incorporated at the level it applies
to.  Alternatively, one can use independent variance priors for each model
components in the latent Gaussian model.  Details can be found in the
accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of
Statistical Software, <doi:10.18637/jss.v110.i03>).")
    (license license:gpl2+)))

(define-public r-makeit
  (package
    (name "r-makeit")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makeit" version))
       (sha256
        (base32 "1x0dwffzz1i88h9mqcigihm52fi47439jmd1v9712sf3h7hf5iiy"))))
    (properties `((upstream-name . "makeit")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/arni-magnusson/makeit")
    (synopsis "Run R Scripts if Needed")
    (description
     "Automation tool to run R scripts if needed, based on last modified time.
Implemented in base R with no additional software requirements, organizational
overhead, or structural requirements.  In short: run an R script if underlying
files have changed, otherwise do nothing.")
    (license license:gpl3)))

(define-public r-makeflow
  (package
    (name "r-makeflow")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makeFlow" version))
       (sha256
        (base32 "0r7a0klgx144rnks0fhjflnf8vfyyx2544n86nnxkp6cdvw0b4pw"))))
    (properties `((upstream-name . "makeFlow")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-dplyr))
    (home-page "https://cran.r-project.org/package=makeFlow")
    (synopsis "Visualizing Sequential Classifications")
    (description
     "This package provides a user-friendly tool for visualizing categorical or group
movement.")
    (license license:gpl2+)))

(define-public r-makefiler
  (package
    (name "r-makefiler")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MakefileR" version))
       (sha256
        (base32 "1pfjic2lsar8ghbb6byr4rqrs30qrgfih092z4rxdpsiwkk3y7l1"))))
    (properties `((upstream-name . "MakefileR")))
    (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-magrittr))
    (native-inputs (list r-knitr esbuild))
    (home-page "http://krlmlr.github.io/MakefileR")
    (synopsis "Create 'Makefiles' Using R")
    (description
     "This package provides a user-friendly interface for the construction of
Makefiles'.")
    (license license:gpl3)))

(define-public r-makedummies
  (package
    (name "r-makedummies")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "makedummies" version))
       (sha256
        (base32 "062rvijhy2dybpgxzp44cg46bfyb8lp9yasv3mhskhf3yg717sqp"))))
    (properties `((upstream-name . "makedummies")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/toshi-ara/makedummies")
    (synopsis "Create Dummy Variables from Categorical Data")
    (description
     "Create dummy variables from categorical data.  This package can convert
categorical data (factor and ordered) into dummy variables and handle multiple
columns simultaneously.  This package enables to select whether a dummy variable
for base group is included (for principal component analysis/factor analysis) or
excluded (for regression analysis) by an option.  makedummies function accepts
data.frame', matrix', and tbl (tibble) class (by tibble package).  matrix class
data is automatically converted to data.frame class.")
    (license license:gpl2)))

(define-public r-majminkmeans
  (package
    (name "r-majminkmeans")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MajMinKmeans" version))
       (sha256
        (base32 "0lq2qjlha1bd76wlgxbwr9j55yjhwpj380nfi7kx91j50qhdsr9x"))))
    (properties `((upstream-name . "MajMinKmeans")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MajMinKmeans")
    (synopsis "k-Means Algorithm with a Majorization-Minimization Method")
    (description
     "This package provides a hybrid of the K-means algorithm and a
Majorization-Minimization method to introduce a robust clustering.  The
reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>.  The two most
important functions in package @code{MajMinKmeans} are cluster_km() and
cluster_@code{MajKm}().  Cluster_km() clusters data without
Majorization-Minimization and cluster_@code{MajKm}() clusters data with
Majorization-Minimization method.  Both of these functions calculate the sum of
squares (SS) of clustering.  Another useful function is @code{MajMinOptim}(),
which helps to find the optimum values of the Majorization-Minimization
estimator.")
    (license license:gpl3)))

(define-public r-majkmeans
  (package
    (name "r-majkmeans")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MajKMeans" version))
       (sha256
        (base32 "1zphrxyx3jd6rnh1y1qnc6bip34kfqk1a5ms4ps39gfngznh482m"))))
    (properties `((upstream-name . "MajKMeans")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=MajKMeans")
    (synopsis "k-Means Algorithm with a Majorization-Minimization Method")
    (description
     "This package provides a hybrid of the K-means algorithm and a
Majorization-Minimization method to introduce a robust clustering.  The
reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>.  The two most
important functions in package @code{MajKMeans} are cluster_km() and
cluster_@code{MajKm}().  cluster_km() clusters data without
Majorization-Minimization and cluster_@code{MajKm}() clusters data with
Majorization-Minimization method.  Both of these functions calculate the sum of
squares (SS) of clustering.")
    (license license:gpl3)))

(define-public r-maint-data
  (package
    (name "r-maint-data")
    (version "2.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAINT.Data" version))
       (sha256
        (base32 "0hjrz25jg4a6kcap01rjjgfvvmg1padd57wgjxihaxpsm7pb0fq9"))))
    (properties `((upstream-name . "MAINT.Data")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-sn
                             r-rrcov
                             r-robustbase
                             r-rcpparmadillo
                             r-rcpp
                             r-pcapp
                             r-misctools
                             r-mclust
                             r-mass
                             r-ggplot2
                             r-ggally))
    (home-page "https://cran.r-project.org/package=MAINT.Data")
    (synopsis "Model and Analyse Interval Data")
    (description
     "This package implements methodologies for modelling interval data by Normal and
Skew-Normal distributions, considering appropriate parameterizations of the
variance-covariance matrix that takes into account the intrinsic nature of
interval data, and lead to four different possible configuration structures.
The Skew-Normal parameters can be estimated by maximum likelihood, while Normal
parameters may be estimated by maximum likelihood or robust trimmed maximum
likelihood methods.")
    (license license:gpl2)))

(define-public r-mainexistingdatasets
  (package
    (name "r-mainexistingdatasets")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MainExistingDatasets" version))
       (sha256
        (base32 "16g741hzckla6amhwa54bhg3l3700sp43p6mijbb5065qnviw5y9"))))
    (properties `((upstream-name . "MainExistingDatasets")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmaptools
                             r-tmap
                             r-tidyr
                             r-spdata
                             r-shiny
                             r-sf
                             r-rlang
                             r-processx
                             r-pkgload
                             r-openxlsx
                             r-magrittr
                             r-htmlwidgets
                             r-htmltools
                             r-golem
                             r-glue
                             r-dt
                             r-dplyr
                             r-config))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MainExistingDatasets")
    (synopsis "Main Existing Human Datasets")
    (description
     "Shiny for Open Science to visualize, share, and inventory the main existing
human datasets for researchers.")
    (license license:gpl3)))

(define-public r-mailtor
  (package
    (name "r-mailtor")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mailtoR" version))
       (sha256
        (base32 "0mnzblhy2vbxkf0k49bw1g08jw84s59zxbjrva2j2q347wzf4rgk"))))
    (properties `((upstream-name . "mailtoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmltools r-glue))
    (home-page "https://github.com/feddelegrand7/mailtoR")
    (synopsis
     "Creates a Friendly User Interface for Emails Sending in 'shiny'")
    (description
     "Allows the user to generate a friendly user interface for emails sending.  The
user can choose from the most popular free email services ('Gmail', Outlook',
Yahoo') and his default email application.  The package is a wrapper for the
Mailtoui @code{JavaScript} library.  See <https://mailtoui.com/#menu> for more
information.")
    (license license:expat)))

(define-public r-mailr
  (package
    (name "r-mailr")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mailR" version))
       (sha256
        (base32 "0p6vxzpyms1pxdb0j07izzxinw8xfmmbsj35nkypxyjv92qi802k"))))
    (properties `((upstream-name . "mailR")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-stringr r-rjava r-r-utils r-assertthat))
    (home-page "https://github.com/rpremrajGit/mailR")
    (synopsis "Utility to Send Emails from R")
    (description "Interface to Apache Commons Email to send emails from R.")
    (license license:gpl3)))

(define-public r-mailmerge
  (package
    (name "r-mailmerge")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mailmerge" version))
       (sha256
        (base32 "0i11kaxv57imyms96j8dbry8nkll3smb19ba8agcl4zxp5xxqj7a"))))
    (properties `((upstream-name . "mailmerge")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rstudioapi
                             r-rmarkdown
                             r-purrr
                             r-miniui
                             r-magrittr
                             r-lifecycle
                             r-googledrive
                             r-gmailr
                             r-glue
                             r-fs
                             r-commonmark))
    (native-inputs (list r-knitr))
    (home-page "https://andrie.github.io/mailmerge/")
    (synopsis "Mail Merge Using R Markdown Documents and 'gmailr'")
    (description
     "Perform a mail merge (mass email) using the message defined in markdown, the
recipients in a csv file, and gmail as the mailing engine.  With this package
you can parse markdown documents as the body of email, and the yaml header to
specify the subject line of the email.  Any {} braces in the email will be
encoded with glue::glue()'.  You can preview the email in the RStudio viewer
pane, and send (draft) email using gmailr'.")
    (license license:expat)))

(define-public r-mailchimpr
  (package
    (name "r-mailchimpr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mailchimpR" version))
       (sha256
        (base32 "1f7zjclgcky1qb88638lqhr71gi5myk3d3vxkjl1xnikhcr5l9mp"))))
    (properties `((upstream-name . "mailchimpR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://windsor.ai/")
    (synopsis "Get Mailchimp Data via the 'Windsor.ai' API")
    (description
     "Collect your data on digital marketing campaigns from Mailchimp using the
Windsor.ai API <https://windsor.ai/api-fields/>.")
    (license license:gpl3)))

(define-public r-maicchecks
  (package
    (name "r-maicchecks")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maicChecks" version))
       (sha256
        (base32 "1c2n8avrhx0lvw9hi5slymm49lzf37hbkqzkqy230aikgmpv0cpa"))))
    (properties `((upstream-name . "maicChecks")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-quadprog r-lpsolve r-ggplot2
                             r-data-table))
    (home-page "https://cran.r-project.org/package=maicChecks")
    (synopsis
     "Assessing the Numerical Feasibility for Conducting a Matching-Adjusted Indirect Comparison (MAIC)")
    (description
     "This package provides a collection of easy-to-implement tools for checking
whether a MAIC can be conducted.  An alternative way of calculating weights is
also included.  These methods are introduced in Glimm & Yau (2021)
<@code{arXiv:2108.01896>}.")
    (license license:gpl3+)))

(define-public r-maic
  (package
    (name "r-maic")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maic" version))
       (sha256
        (base32 "0ba0kg5kgnn2g33mmhj9x8ichckyybbpn5xxc56qxvk2w2xv2wpj"))))
    (properties `((upstream-name . "maic")))
    (build-system r-build-system)
    (propagated-inputs (list r-weights r-matrixstats r-hmisc))
    (home-page "https://github.com/heorltd/maic")
    (synopsis "Matching-Adjusted Indirect Comparison")
    (description
     "This package provides a generalised workflow for generation of subject weights
to be used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et
al. (2012) <doi:10.1016/j.jval.2012.05.004>, Signorovitch et al (2010)
<doi:10.2165/11538370-000000000-00000>.  In MAIC, unbiased comparison between
outcomes of two trials is facilitated by weighting the subject-level outcomes of
one trial with weights derived such that the weighted aggregate measures of the
prognostic or effect modifying variables are equal to those of the sample in the
comparator trial.  The functions and classes included in this package wrap and
abstract the process demonstrated in the UK National Institute for Health and
Care Excellence Decision Support Unit (NICE DSU)'s example (Phillippo et al,
(2016) [see URL]), providing a repeatable and easily specifiable workflow for
producing multiple comparison variable sets against a variety of target studies,
with preprocessing for a number of aggregate target forms (e.g. mean, median,
domain limits).")
    (license license:gpl3)))

(define-public r-magui
  (package
    (name "r-magui")
    (version "4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maGUI" version))
       (sha256
        (base32 "0r54r9p1j8mnqsm0r0c3glz9p9vz7mcdyqlc6lpi7rdy9n61b7r5"))))
    (properties `((upstream-name . "maGUI")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-biobase))
    (home-page "https://cran.r-project.org/package=maGUI")
    (synopsis
     "Graphical User Interface for Microarray Data Analysis and Annotation")
    (description
     "This package provides a comprehensive graphical user interface for analysis of
Affymetrix, Agilent, Illumina, Nimblegen and other microarray data.  It can
perform miscellaneous tasks such as gene set enrichment and test analyses,
identifying gene symbols and building co-expression network.  It can also
estimate sample size for atleast two-fold expression change.  The current
version is its slenderized form for compatable and flexible implementation.")
    (license license:gpl2)))

(define-public r-magree
  (package
    (name "r-magree")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "magree" version))
       (sha256
        (base32 "01jnl2lq6s9l25yz4c66h7icyiwa4lggiwwa3383z1x4ac1fgw2q"))))
    (properties `((upstream-name . "magree")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=magree")
    (synopsis
     "Implements the O'Connell-Dobson-Schouten Estimators of Agreement for Multiple Observers")
    (description
     "This package implements an interface to the legacy Fortran code from O'Connell
and Dobson (1984) <DOI:10.2307/2531148>.  Implements Fortran 77 code for the
methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>.
Includes estimates of average agreement for each observer and average agreement
for each subject.")
    (license (list license:gpl3 license:gpl2))))

(define-public r-magnamwar
  (package
    (name "r-magnamwar")
    (version "2.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAGNAMWAR" version))
       (sha256
        (base32 "1asr18byrgy6bkpxpmnkczgr3dam6ymf30lw6izq112krc891bg4"))))
    (properties `((upstream-name . "MAGNAMWAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-seqinr
                             r-qqman
                             r-plyr
                             r-multcomp
                             r-lme4
                             r-iterators
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-coxme
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MAGNAMWAR")
    (synopsis "Pipeline for Meta-Genome Wide Association")
    (description
     "Correlates variation within the meta-genome to target species phenotype
variations in meta-genome with association studies.  Follows the pipeline
described in Chaston, J.M. et al. (2014) <doi:10.1128/@code{mBio.01631-14>}.")
    (license license:expat)))

(define-public r-magmar
  (package
    (name "r-magmar")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "magmaR" version))
       (sha256
        (base32 "04c1wvz8r6msc6jq6xv5w75sqza8xcfnawzayniyzdg6k2lr7hr5"))))
    (properties `((upstream-name . "magmaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-crul))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=magmaR")
    (synopsis "R-Client for Interacting with the 'UCSF Data Library'")
    (description
     "This package provides a client for interacting with magma', the data warehouse
of the UCSF Data Library'. @code{magmaR} includes functions for querying and
downloading data from magma', in order to enable working with such data in R, as
well as for uploading local data to magma'.")
    (license license:gpl2)))

(define-public r-magmaclustr
  (package
    (name "r-magmaclustr")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MagmaClustR" version))
       (sha256
        (base32 "0gwfb5p5garycnvqsd4l80yqna7y09xs9ffgr2ah6150s9w3r0q0"))))
    (properties `((upstream-name . "MagmaClustR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-rcpp
                             r-purrr
                             r-plyr
                             r-mvtnorm
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-broom))
    (home-page "https://github.com/ArthurLeroy/MagmaClustR")
    (synopsis
     "Clustering and Prediction using Multi-Task Gaussian Processes with Common Mean")
    (description
     "An implementation for the multi-task Gaussian processes with common mean
framework.  Two main algorithms, called Magma and @code{MagmaClust}', are
available to perform predictions for supervised learning problems, in particular
for time series or any functional/continuous data applications.  The
corresponding articles has been respectively proposed by Arthur Leroy, Pierre
Latouche, Benjamin Guedj and Servane Gey (2022)
<doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche, Benjamin
Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>.  Theses
approaches leverage the learning of cluster-specific mean processes, which are
common across similar tasks, to provide enhanced prediction performances (even
far from data) at a linear computational cost (in the number of tasks).
@code{MagmaClust} is a generalisation of Magma where the tasks are
simultaneously clustered into groups, each being associated to a specific mean
process.  User-oriented functions in the package are decomposed into training,
prediction and plotting functions.  Some basic features (classic kernels,
training, prediction) of standard Gaussian processes are also implemented.")
    (license license:expat)))

(define-public r-magma-r
  (package
    (name "r-magma-r")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAGMA.R" version))
       (sha256
        (base32 "07jicr9axlb8cnjjprzrkad57r2wrnap52n45sia5qfjp53ys12g"))))
    (properties `((upstream-name . "MAGMA.R")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tidyselect
                             r-tibble
                             r-stddiff
                             r-robumeta
                             r-rlang
                             r-purrr
                             r-psych
                             r-overlapping
                             r-metafor
                             r-janitor
                             r-ggplot2
                             r-foreach
                             r-flextable
                             r-dplyr
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=MAGMA.R")
    (synopsis "MAny-Group MAtching")
    (description
     "Balancing quasi-experimental field research for effects of covariates is
fundamental for drawing causal inference.  Propensity Score Matching deals with
this issue but current techniques are restricted to binary treatment variables.
Moreover, they provide several solutions without providing a comprehensive
framework on choosing the best model.  The MAGMA R-package addresses these
restrictions by offering nearest neighbor matching for two to four groups.  It
also includes the option to match data of a 2x2 design.  In addition, MAGMA
includes a framework for evaluating the post-matching balance.  The package
includes functions for the matching process and matching reporting.  We provide
a tutorial on MAGMA as vignette.  More information on MAGMA can be found in
Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022)
<https://osf.io/p47nc/>.")
    (license license:gpl3)))

(define-public r-magiclamp
  (package
    (name "r-magiclamp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "magicLamp" version))
       (sha256
        (base32 "1nfcnbqd6vawz8l3vgkbhmm462xdvaa4pn6axzmz8hl05xv2k3cv"))))
    (properties `((upstream-name . "magicLamp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-httr))
    (home-page "https://github.com/swarm-lab/magicLamp")
    (synopsis "'WeMo Switch' Smart Plug Utilities")
    (description
     "Set of utility functions to interact with @code{WeMo} Switch', a smart plug that
can be remotely controlled via wifi.  The provided functions make it possible to
turn one or more @code{WeMo} Switch plugs on and off in a scriptable fashion.
More information about @code{WeMo} Switch can be found at
<http://www.belkin.com/us/p/P-F7C027/>.")
    (license license:gpl3)))

(define-public r-magickgui
  (package
    (name "r-magickgui")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "magickGUI" version))
       (sha256
        (base32 "14jgf4bkpk8lsv3v5843mnkd241f5gpqyg5cqw7xls30d2vs2r8p"))))
    (properties `((upstream-name . "magickGUI")))
    (build-system r-build-system)
    (inputs (list imagemagick))
    (propagated-inputs (list r-magick))
    (home-page "https://github.com/ShotaOchi/magickGUI")
    (synopsis "GUI Tools for Interactive Image Processing with 'magick'")
    (description
     "Enables us to use the functions of the package magick interactively.")
    (license license:gpl3)))

(define-public r-magicfor
  (package
    (name "r-magicfor")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "magicfor" version))
       (sha256
        (base32 "07l33fczr1x42sfxrsqq4q6r65sdflin6l8f5cgclnvx1858jdip"))))
    (properties `((upstream-name . "magicfor")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hoxo-m/magicfor")
    (synopsis "Magic Functions to Obtain Results from for Loops")
    (description "Magic functions to obtain results from for loops.")
    (license license:expat)))

(define-public r-magi
  (package
    (name "r-magi")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "magi" version))
       (sha256
        (base32 "0xri39mjq9x8gdj1a69h1sby2i6vcx4vx86zv483128wb0qwgfww"))))
    (properties `((upstream-name . "magi")))
    (build-system r-build-system)
    (propagated-inputs (list r-roptim
                             r-rcpparmadillo
                             r-rcpp
                             r-gridextra
                             r-gridbase
                             r-desolve
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://doi.org/10.18637/jss.v109.i04")
    (synopsis "MAnifold-Constrained Gaussian Process Inference")
    (description
     "This package provides fast and accurate inference for the parameter estimation
problem in Ordinary Differential Equations, including the case when there are
unobserved system components.  Implements the MAGI method (MAnifold-constrained
Gaussian process Inference) of Yang, Wong, and Kou (2021)
<doi:10.1073/pnas.2020397118>.  A user guide is provided by the accompanying
software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.")
    (license license:expat)))

(define-public r-magee
  (package
    (name "r-magee")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAGEE" version))
       (sha256
        (base32 "1gwm82iwji0wviy2d3brws9d3iqwgxlkcf4b24b9hcvfl1kgglb6"))))
    (properties `((upstream-name . "MAGEE")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-gmmat
                             r-foreach
                             r-data-table
                             r-compquadform))
    (home-page "https://cran.r-project.org/package=MAGEE")
    (synopsis "Mixed Model Association Test for GEne-Environment Interaction")
    (description
     "Use a glmmkin class object (GMMAT package) from the null model to perform
generalized linear mixed model-based single-variant and variant set main effect
tests, gene-environment interaction tests, and joint tests for association, as
proposed in Wang et al. (2020) <DOI:10.1002/gepi.22351>.")
    (license license:gpl3+)))

(define-public r-magclass
  (package
    (name "r-magclass")
    (version "6.13.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "magclass" version))
       (sha256
        (base32 "1nv500vlmrl4d8rkmrk0fkklwigablb7ybgwr42zx50vjd9wz850"))))
    (properties `((upstream-name . "magclass")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pik-piam/magclass")
    (synopsis "Data Class and Tools for Handling Spatial-Temporal Data")
    (description
     "Data class for increased interoperability working with spatial-temporal data
together with corresponding functions and methods (conversions, basic
calculations and basic data manipulation).  The class distinguishes between
spatial, temporal and other dimensions to facilitate the development and
interoperability of tools build for it.  Additional features are name-based
addressing of data and internal consistency checks (e.g. checking for the right
data order in calculations).")
    (license (list license:lgpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-mafr
  (package
    (name "r-mafr")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mafR" version))
       (sha256
        (base32 "1cqqrgn9br1rpn6zqngammdwrzx32nds7f2nn151nwn1hwg9xin7"))))
    (properties `((upstream-name . "mafR")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate))
    (home-page "https://github.com/f-rousset/mafR")
    (synopsis "Interface for Masked Autoregressive Flows")
    (description
     "Interfaces the Python library zuko implementing Masked Autoregressive Flows.
See Rozet, Divo and Schnake (2023) <doi:10.5281/zenodo.7625672> and
Papamakarios, Pavlakou and Murray (2017) <doi:10.48550/@code{arXiv.1705.07057>}.")
    (license license:gpl2+)))

(define-public r-maeswrap
  (package
    (name "r-maeswrap")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Maeswrap" version))
       (sha256
        (base32 "0cnnr5zq7ax1j7dx7ira7iccqppc6qpdjghjarvdb2zj0lf69yyb"))))
    (properties `((upstream-name . "Maeswrap")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rgl r-lattice r-geometry))
    (home-page "https://cran.r-project.org/package=Maeswrap")
    (synopsis "Wrapper Functions for MAESTRA/MAESPA")
    (description
     "This package provides a bundle of functions for modifying MAESTRA/MAESPA input
files,reading output files, and visualizing the stand in 3D. Handy for running
sensitivity analyses, scenario analyses, etc.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-maestro
  (package
    (name "r-maestro")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maestro" version))
       (sha256
        (base32 "1z5x5gnkynl51116na8lzqqqwniskqpsdds5dy0mwh1qndi5j3c2"))))
    (properties `((upstream-name . "maestro")))
    (build-system r-build-system)
    (propagated-inputs (list r-timechange
                             r-tictoc
                             r-roxygen2
                             r-rlang
                             r-r6
                             r-r-utils
                             r-purrr
                             r-lubridate
                             r-logger
                             r-lifecycle
                             r-glue
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/whipson/maestro")
    (synopsis "Orchestration of Data Pipelines")
    (description
     "Framework for creating and orchestrating data pipelines.  Organize, orchestrate,
and monitor multiple pipelines in a single project.  Use tags to decorate
functions with scheduling parameters and configuration.")
    (license license:expat)))

(define-public r-madsim
  (package
    (name "r-madsim")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "madsim" version))
       (sha256
        (base32 "11gfyimbz363p5xy1x6rsz5fcgzcr55myc5aaz5lazjkcnfn17aq"))))
    (properties `((upstream-name . "madsim")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=madsim")
    (synopsis "Flexible Microarray Data Simulation Model")
    (description
     "This function allows to generate two biological conditions synthetic microarray
dataset which has similar behavior to those currently observed with common
platforms.  User provides a subset of parameters.  Available default parameters
settings can be modified.")
    (license license:gpl2+)))

(define-public r-madshapr
  (package
    (name "r-madshapr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "madshapR" version))
       (sha256
        (base32 "035hxkkhxi68id1f3q9qyl82kzphq9als60x7wv03gmzi6fn85zy"))))
    (properties `((upstream-name . "madshapR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidytext
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-readr
                             r-lubridate
                             r-lifecycle
                             r-knitr
                             r-janitor
                             r-haven
                             r-ggplot2
                             r-fs
                             r-forcats
                             r-fabr
                             r-dt
                             r-dplyr
                             r-crayon
                             r-bookdown))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/maelstrom-research/madshapR")
    (synopsis
     "Support Technical Processes Following 'Maelstrom Research' Standards")
    (description
     "This package provides functions to support rigorous processes in data cleaning,
evaluation, and documentation across datasets from different studies based on
Maelstrom Research guidelines.  The package includes the core functions to
evaluate and format the main inputs that define the process, diagnose errors,
and summarize and evaluate datasets and their associated data dictionaries.  The
main outputs are clean datasets and associated metadata, and tabular and visual
summary reports.  As described in Maelstrom Research guidelines for rigorous
retrospective data harmonization (Fortier I and al. (2017)
<doi:10.1093/ije/dyw075>).")
    (license license:gpl3)))

(define-public r-madrat
  (package
    (name "r-madrat")
    (version "3.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "madrat" version))
       (sha256
        (base32 "03wkzpr0nx50li0g69fn2fznfh6nbg4sdpymsilx3387v50vc9vs"))))
    (properties `((upstream-name . "madrat")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-withr
                             r-stringi
                             r-renv
                             r-pkgload
                             r-matrix
                             r-magclass
                             r-igraph
                             r-digest
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pik-piam/madrat")
    (synopsis "May All Data be Reproducible and Transparent (MADRaT) *")
    (description
     "This package provides a framework which should improve reproducibility and
transparency in data processing.  It provides functionality such as automatic
meta data creation and management, rudimentary quality management, data caching,
work-flow management and data aggregation. * The title is a wish not a promise.
By no means we expect this package to deliver everything what is needed to
achieve full reproducibility and transparency, but we believe that it supports
efforts in this direction.")
    (license license:bsd-2)))

(define-public r-madr
  (package
    (name "r-madr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "madr" version))
       (sha256
        (base32 "0lzg75kphz4a0w6n0jbsq87g72jvrbkyas4j813mryq2nv4lmsl4"))))
    (properties `((upstream-name . "madr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=madr")
    (synopsis "Model Averaged Double Robust Estimation")
    (description
     "Estimates average treatment effects using model average double robust (MA-DR)
estimation.  The MA-DR estimator is defined as weighted average of double robust
estimators, where each double robust estimator corresponds to a specific choice
of the outcome model and the propensity score model.  The MA-DR estimator extend
the desirable double robustness property by achieving consistency under the much
weaker assumption that either the true propensity score model or the true
outcome model be within a specified, possibly large, class of models.")
    (license license:gpl3)))

(define-public r-madpop
  (package
    (name "r-madpop")
    (version "1.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MADPop" version))
       (sha256
        (base32 "0qmn6spp6vfn60h32s58yxngv8gmp1hpl8xni55v6ari0kczw90x"))))
    (properties `((upstream-name . "MADPop")))
    (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://github.com/mlysy/MADPop")
    (synopsis "MHC Allele-Based Differencing Between Populations")
    (description
     "This package provides tools for the analysis of population differences using the
Major Histocompatibility Complex (MHC) genotypes of samples having a variable
number of alleles (1-4) recorded for each individual.  A hierarchical
Dirichlet-Multinomial model on the genotype counts is used to pool small samples
from multiple populations for pairwise tests of equality.  Bayesian inference is
implemented via the rstan package.  Bootstrapped and posterior p-values are
provided for chi-squared and likelihood ratio tests of equal genotype
probabilities.")
    (license license:gpl3)))

(define-public r-madness
  (package
    (name "r-madness")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "madness" version))
       (sha256
        (base32 "1a0jcvy7xq654hcjv0nsjfhwh9fgxzhk7grjy209mgzmkivdinpq"))))
    (properties `((upstream-name . "madness")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-matrix r-expm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/shabbychef/madness")
    (synopsis "Automatic Differentiation of Multivariate Operations")
    (description
     "An object that supports automatic differentiation of matrix- and
multidimensional-valued functions with respect to multidimensional independent
variables.  Automatic differentiation is via forward accumulation'.")
    (license license:lgpl3)))

(define-public r-maditr
  (package
    (name "r-maditr")
    (version "0.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maditr" version))
       (sha256
        (base32 "15jhchxa5ckir6n2gz4vlsvlz64mxqlry11zqpma32wj901d18r4"))))
    (properties `((upstream-name . "maditr")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gdemin/maditr")
    (synopsis
     "Fast Data Aggregation, Modification, and Filtering with Pipes and 'data.table'")
    (description
     "This package provides pipe-style interface for data.table'.  Package preserves
all data.table features without significant impact on performance.  let and take
functions are simplified interfaces for most common data manipulation tasks.
For example, you can write take(mtcars, mean(mpg), by = am) for aggregation or
let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg) for modification.  Use
take_if/let_if for conditional aggregation/modification.  Additionally there are
some conveniences such as automatic data.frame conversion to data.table'.")
    (license license:gpl2)))

(define-public r-madgrad
  (package
    (name "r-madgrad")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "madgrad" version))
       (sha256
        (base32 "0cxf1b57cl7qwax60wv4jvcac762ara2i18nb1fcjp954mwgwnlb"))))
    (properties `((upstream-name . "madgrad")))
    (build-system r-build-system)
    (propagated-inputs (list r-torch r-rlang))
    (home-page "https://cran.r-project.org/package=madgrad")
    (synopsis "'MADGRAD' Method for Stochastic Optimization")
    (description
     "This package provides a Momentumized, Adaptive, Dual Averaged Gradient Method
for Stochastic Optimization algorithm.  MADGRAD is a best-of-both-worlds
optimizer with the generalization performance of stochastic gradient descent and
at least as fast convergence as that of Adam, often faster.  A drop-in
optim_madgrad() implementation is provided based on Defazio et al (2020)
<arxiv:2101.11075>.")
    (license license:expat)))

(define-public r-maddison
  (package
    (name "r-maddison")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maddison" version))
       (sha256
        (base32 "0v6s28kzghpxrj6sw94c3ys7k4z1h89kdx8ns1991m8q2v51bx1f"))))
    (properties `((upstream-name . "maddison")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=maddison")
    (synopsis "The Maddison Project Database")
    (description
     "This package contains the Maddison Project 2018 database, which provides
estimates of GDP per capita for all countries in the world between AD 1 and
2016.  See <https://www.rug.nl/ggdc/historicaldevelopment/maddison/> for more
information.")
    (license license:cc0)))

(define-public r-madantextnetwork
  (package
    (name "r-madantextnetwork")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MadanTextNetwork" version))
       (sha256
        (base32 "0691pwsgbmy2fmvcf6adqgasrdf70n5g0cgy6663wfavkdx5nni2"))))
    (properties `((upstream-name . "MadanTextNetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsx
                             r-visnetwork
                             r-udpipe
                             r-topicmodels
                             r-tm
                             r-tidytext
                             r-tidyr
                             r-textminer
                             r-stringr
                             r-stringi
                             r-stopwords
                             r-shinythemes
                             r-shiny
                             r-persianstemmer
                             r-ngram
                             r-lattice
                             r-igraph
                             r-hwordcloud
                             r-glue
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=MadanTextNetwork")
    (synopsis "Persian Text Mining Tool for Co-Occurrence Network")
    (description
     "This package provides an extension to @code{MadanText} for creating and
analyzing co-occurrence networks in Persian text data.  This package mainly
makes use of the @code{PersianStemmer} (Safshekan, R., et al. (2019).
<https://CRAN.R-project.org/package=@code{PersianStemmer>}), udpipe (Wijffels,
J., et al. (2023). <https://CRAN.R-project.org/package=udpipe>), and shiny
(Chang, W., et al. (2023). <https://CRAN.R-project.org/package=shiny>) packages.")
    (license license:gpl3)))

(define-public r-madantext
  (package
    (name "r-madantext")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MadanText" version))
       (sha256
        (base32 "1sz3mwb74yvj5ijmpm7zdwsgyvhs6rs8fpy1mpw538r5jx4xbzg8"))))
    (properties `((upstream-name . "MadanText")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsx
                             r-udpipe
                             r-topicmodels
                             r-tm
                             r-tidytext
                             r-tidyr
                             r-textminer
                             r-stringr
                             r-stringi
                             r-stopwords
                             r-shinythemes
                             r-shiny
                             r-persianstemmer
                             r-lattice
                             r-hwordcloud
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=MadanText")
    (synopsis
     "Persian Text Mining Tool for Frequency Analysis, Statistical Analysis, and Word Clouds")
    (description
     "This is an open-source software designed specifically for text mining in the
Persian language.  It allows users to examine word frequencies, download data
for analysis, and generate word clouds.  This tool is particularly useful for
researchers and analysts working with Persian language data.  This package
mainly makes use of the @code{PersianStemmer} (Safshekan, R., et al. (2019).
<https://CRAN.R-project.org/package=@code{PersianStemmer>}), udpipe (Wijffels,
J., et al. (2023). <https://CRAN.R-project.org/package=udpipe>), and shiny
(Chang, W., et al. (2023). <https://CRAN.R-project.org/package=shiny>) packages.")
    (license license:gpl3)))

(define-public r-mada
  (package
    (name "r-mada")
    (version "0.5.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mada" version))
       (sha256
        (base32 "1cqjkk8rr8in12hms6qvm2yrgmnr9xwg0z6cc0gnzazz0rijj8n1"))))
    (properties `((upstream-name . "mada")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mvmeta r-metafor r-ellipse))
    (home-page "r-forge.r-project.org/projects/mada/")
    (synopsis "Meta-Analysis of Diagnostic Accuracy")
    (description
     "This package provides functions for diagnostic meta-analysis.  Next to basic
analysis and visualization the bivariate Model of Reitsma et al. (2005) that is
equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted.  A new
approach based to diagnostic meta-analysis of Holling et al. (2012) is also
available.  Standard methods like summary, plot and so on are provided.")
    (license license:gpl2)))

(define-public r-mad
  (package
    (name "r-mad")
    (version "0.8-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAd" version))
       (sha256
        (base32 "0br099qzmzvdcx5pzb772dipyb279kgzhx6p2b7g0fp4r4bk7gbd"))))
    (properties `((upstream-name . "MAd")))
    (build-system r-build-system)
    (home-page "https://www.acdelre.com")
    (synopsis "Meta-Analysis with Mean Differences")
    (description
     "This package provides a collection of functions for conducting a meta-analysis
with mean differences data.  It uses recommended procedures as	described in The
	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine,
2009).")
    (license license:gpl2+)))

(define-public r-maczic
  (package
    (name "r-maczic")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maczic" version))
       (sha256
        (base32 "02papbs1pw470cxzwcgvn8px5hvvg1p8zpmcrk5zqmlpzgjsdx2l"))))
    (properties `((upstream-name . "maczic")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sandwich
                             r-pscl
                             r-mediation
                             r-mathjaxr
                             r-mass
                             r-emplik
                             r-bb))
    (home-page "https://cran.r-project.org/package=maczic")
    (synopsis "Mediation Analysis for Count and Zero-Inflated Count Data")
    (description
     "This package performs causal mediation analysis for count and zero-inflated
count data without or with a post-treatment confounder; calculates power to
detect prespecified causal mediation effects, direct effects, and total effects;
performs sensitivity analysis when there is a treatment- induced
mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z.,
Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) <doi:10.1177/0962280216686131>.
 Implements Instrumental Variable (IV) method to estimate the controlled
(natural) direct and mediation effects, and compute the bootstrap Confidence
Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018)
<doi:10.1111/rssc.12233>.  This software was made possible by Grant R03DE028410
from the National Institute of Dental and Craniofacial Research, a component of
the National Institutes of Health.")
    (license license:gpl2+)))

(define-public r-mactivate
  (package
    (name "r-mactivate")
    (version "0.6.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mactivate" version))
       (sha256
        (base32 "0qqhniq0wdhazhfiiw7g2b0v8jkc8jl2m117k1c6gnajg8gs1ymn"))))
    (properties `((upstream-name . "mactivate")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=mactivate")
    (synopsis "Multiplicative Activation")
    (description
     "This package provides methods and classes for adding m-activation
(\"multiplicative activation\") layers to MLR or multivariate logistic regression
models.  M-activation layers created in this library detect and add input
interaction (polynomial) effects into a predictive model.  M-activation can
detect high-order interactions -- a traditionally non-trivial challenge.
Details concerning application, methodology, and relevant survey literature can
be found in this library's vignette, \"About.\".")
    (license license:gpl3+)))

(define-public r-macrozoobenthoswatera
  (package
    (name "r-macrozoobenthoswatera")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MacroZooBenthosWaterA" version))
       (sha256
        (base32 "15b90g7nw82dmpfarx5qm159jmrpp915qz26vv3ya6ky4g2kl6ic"))))
    (properties `((upstream-name . "MacroZooBenthosWaterA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=MacroZooBenthosWaterA")
    (synopsis "Fresh Water Quality Analysis Based on Macrozoobenthos")
    (description
     "Includes functions for calculating basic indices of macrozoobenthos for water
quality and is designed to provide researchers and environmental professionals
with a comprehensive tool for evaluating the ecological health of aquatic
ecosystems.The package is based on the following references: Paisley, M. F.,
Trigg, D. J. and Walley, W. J. (2014)<doi:10.1002/rra.2686>.  Arslan, N., Salur,
A., Kalyoncu, H. et al.(2016) <doi:10.1515/biolog-2016-0005>.  Hilsenhoff W.L.
(1987).  Hilsenhoff.  W.L. (1988) Barbour, M.T., Gerritsen, J., Snyder, B.D.,
and Stribling, J.B. (1999).")
    (license license:gpl3+)))

(define-public r-macrosyntr
  (package
    (name "r-macrosyntr")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "macrosyntR" version))
       (sha256
        (base32 "1q9fbm36059r007cd80hlg662gwqfy17hqdakgbv6lgv9gy6ymif"))))
    (properties `((upstream-name . "macrosyntR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-reshape2
                             r-igraph
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SamiLhll/macrosyntR")
    (synopsis "Draw Ordered Oxford Grids and Chord Diagrams")
    (description
     "Use standard genomics file format (BED) and a table of orthologs to illustrate
synteny conservation at the genome-wide scale.  Significantly conserved linkage
groups are identified as described in Simakov et al. (2020)
<doi:10.1038/s41559-020-1156-z> and displayed on an Oxford Grid (Edwards (1991)
<doi:10.1111/j.1469-1809.1991.tb00394.x>) or a chord diagram as in Simakov et
al. (2022) <doi:10.1126/sciadv.abi5884>.  The package provides a function that
uses a network-based greedy algorithm to find communities (Clauset et al. (2004)
<doi:10.1103/@code{PhysRevE.70.066111>}) and so automatically order the
chromosomes on the plot to improve interpretability.")
    (license license:gpl3)))

(define-public r-macrocol
  (package
    (name "r-macrocol")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "macrocol" version))
       (sha256
        (base32 "1qz5hsxg4ga4blbrakn4bia2l4lh6h5kh4ix7vnm7rl2f7yk4r5a"))))
    (properties `((upstream-name . "macrocol")))
    (build-system r-build-system)
    (propagated-inputs (list r-readxl r-r-utils r-openxlsx r-lubridate r-httr))
    (home-page
     "<https://github.com/pedroCabraAcela/Scrapping-Colombian-Macrodata>")
    (synopsis "Colombian Macro-Financial Time Series Generator")
    (description
     "This repository aims to contribute to the econometric models production with
Colombian data, by providing a set of web-scrapping functions of some of the
main macro-financial indicators.  All the sources are public and free, but the
advantage of these functions is that they directly download and harmonize the
information in R's environment.  No need to import or download additional files.
 You only need an internet connection!")
    (license license:asl2.0)))

(define-public r-macrobiome
  (package
    (name "r-macrobiome")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "macroBiome" version))
       (sha256
        (base32 "01yb1369g6ya1nkfpgkdzy9hy2vy4qxsz8lm1jkjqlgah1jx1d30"))))
    (properties `((upstream-name . "macroBiome")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-strex
                             r-sf
                             r-rnaturalearthdata
                             r-raster
                             r-palinsol
                             r-devtools))
    (home-page "https://github.com/szelepcsenyi/macroBiome")
    (synopsis "Tool for Mapping the Distribution of the Biomes and Bioclimate")
    (description
     "Procedures for simulating biomes by equilibrium vegetation models, with a
special focus on paleoenvironmental applications.  Three widely used equilibrium
biome models are currently implemented in the package: the Holdridge Life Zone
(HLZ) system (Holdridge 1947, <doi:10.1126/science.105.2727.367>), the
KÃ¶ppen-Geiger classification (KGC) system (KÃ¶ppen 1936,
<https://koeppen-geiger.vu-wien.ac.at/pdf/Koppen_1936.pdf>) and the BIOME model
(Prentice et al.  1992, <doi:10.2307/2845499>).  Three climatic forest-steppe
models are also implemented.  An approach for estimating monthly time series of
relative sunshine duration from temperature and precipitation data (Yin 1999,
<doi:10.1007/s007040050111>) is also adapted, allowing process-based biome
models to be combined with high-resolution paleoclimate simulation datasets
(e.g., CHELSA-@code{TraCE21k} v1.0 dataset:
<https://chelsa-climate.org/chelsa-trace21k/>).")
    (license license:gpl3+)))

(define-public r-maclogp
  (package
    (name "r-maclogp")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maclogp" version))
       (sha256
        (base32 "1fbwv4j4mb7zkyc5vspq7k17pwr49dbvqkssda54ggcf1nbqykmb"))))
    (properties `((upstream-name . "maclogp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist r-plot-matrix r-bma))
    (home-page "https://github.com/YuanyuanLi96/maclogp")
    (synopsis "Measures of Uncertainty for Model Selection")
    (description
     "Following the common types of measures of uncertainty for parameter estimation,
two measures of uncertainty were proposed for model selection, see Liu, Li and
Jiang (2020) <doi:10.1007/s11749-020-00737-9>.  The first measure is a kind of
model confidence set that relates to the variation of model selection, called
Mac.  The second measure focuses on error of model selection, called
@code{LogP}.  They are all computed via bootstrapping.  This package provides
functions to compute these two measures.  Furthermore, a similar model
confidence set adapted from Bayesian Model Averaging can also be computed using
this package.")
    (license license:gpl3+)))

(define-public r-macleish
  (package
    (name "r-macleish")
    (version "0.3.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "macleish" version))
       (sha256
        (base32 "06r97fsasjj7qdm4p8yhvi2hfs1qy35xs0ay9i3bv5k09z5zfia7"))))
    (properties `((upstream-name . "macleish")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stringr
                             r-sf
                             r-rvest
                             r-readr
                             r-phenocamr
                             r-lubridate
                             r-etl
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/beanumber/macleish")
    (synopsis "Retrieve Data from MacLeish Field Station")
    (description
     "Download data from the Ada and Archibald @code{MacLeish} Field Station in
Whately, MA. The Ada and Archibald @code{MacLeish} Field Station is a 260-acre
patchwork of forest and farmland located in West Whately, MA that provides
opportunities for faculty and students to pursue environmental research, outdoor
education, and low-impact recreation (see
<https://www.smith.edu/about-smith/sustainable-smith/macleish> for more
information).  This package contains weather data over several years, and
spatial data on various man-made and natural structures.")
    (license license:cc0)))

(define-public r-machineshop
  (package
    (name "r-machineshop")
    (version "3.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MachineShop" version))
       (sha256
        (base32 "0kpk3fvp7w6qx71556jplpl0wsm5q4fhxzcyz9np2gq3idxl7q37"))))
    (properties `((upstream-name . "MachineShop")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-survival
                             r-rsolnp
                             r-rsample
                             r-rlang
                             r-recipes
                             r-progress
                             r-polspline
                             r-party
                             r-nnet
                             r-matrix
                             r-magrittr
                             r-kernlab
                             r-ggplot2
                             r-foreach
                             r-dials
                             r-cli
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://brian-j-smith.github.io/MachineShop/")
    (synopsis "Machine Learning Models and Tools")
    (description
     "Meta-package for statistical and machine learning with a unified interface for
model fitting, prediction, performance assessment, and presentation of results.
Approaches for model fitting and prediction of numerical, categorical, or
censored time-to-event outcomes include traditional regression models,
regularization methods, tree-based methods, support vector machines, neural
networks, ensembles, data preprocessing, filtering, and model tuning and
selection.  Performance metrics are provided for model assessment and can be
estimated with independent test sets, split sampling, cross-validation, or
bootstrap resampling.  Resample estimation can be executed in parallel for
faster processing and nested in cases of model tuning and selection.  Modeling
results can be summarized with descriptive statistics; calibration curves;
variable importance; partial dependence plots; confusion matrices; and ROC,
lift, and other performance curves.")
    (license license:gpl3)))

(define-public r-macer
  (package
    (name "r-macer")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MACER" version))
       (sha256
        (base32 "02qqcaccw671773lp1zva340fd6wqbsx9v0yxy62cbjnyh3k260r"))))
    (properties `((upstream-name . "MACER")))
    (build-system r-build-system)
    (propagated-inputs (list r-rentrez
                             r-png
                             r-pbapply
                             r-httr
                             r-ggplot2
                             r-ape))
    (home-page "<https://github.com/rgyoung6/MACER>")
    (synopsis "Molecular Acquisition, Cleaning, and Evaluation in R 'MACER'")
    (description
     "To assist biological researchers in assembling taxonomically and marker focused
molecular sequence data sets.  MACER accepts a list of genera as a user input
and uses NCBI-@code{GenBank} and BOLD as resources to download and assemble
molecular sequence datasets.  These datasets are then assembled by marker,
aligned, trimmed, and cleaned.  The use of this package allows the publication
of specific parameters to ensure reproducibility.  The MACER package has four
core functions and an example run through using all of these functions can be
found in the associated repository <https://github.com/rgyoung6/MACER_example>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-macc
  (package
    (name "r-macc")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "macc" version))
       (sha256
        (base32 "1qj4mlikbqrxa6m46527xmxdbk7b3l95z6jdgpmi0ifywjiv52a4"))))
    (properties `((upstream-name . "macc")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimx r-nlme r-mass r-lme4 r-car))
    (home-page "https://cran.r-project.org/package=macc")
    (synopsis "Mediation Analysis of Causality under Confounding")
    (description
     "This package performs causal mediation analysis under confounding or correlated
errors.  This package includes a single level mediation model, a two-level
mediation model, and a three-level mediation model for data with hierarchical
structures.  Under the two/three-level mediation model, the correlation
parameter is identifiable and is estimated based on a hierarchical-likelihood, a
marginal-likelihood or a two-stage method.  See Zhao, Y., & Luo, X. (2014),
Estimating Mediation Effects under Correlated Errors with an Application to
@code{fMRI}, <@code{arXiv:1410.7217>} for details.")
    (license license:gpl2+)))

(define-public r-macbehaviour
  (package
    (name "r-macbehaviour")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MacBehaviour" version))
       (sha256
        (base32 "0i11vvc66zw2r1gnphp0j65q9ar92m5f890bd6c0b31mm49ca6pk"))))
    (properties `((upstream-name . "MacBehaviour")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjson r-openxlsx r-httr r-dplyr))
    (home-page "https://cran.r-project.org/package=MacBehaviour")
    (synopsis "Behavioural Studies of Large Language Models")
    (description
     "Efficient way to design and conduct psychological experiments for testing the
performance of large language models.  It simplifies the process of setting up
experiments and data collection via language modelsâ API, facilitating a
smooth workflow for researchers in the field of machine behaviour.")
    (license license:lgpl3)))

(define-public r-maboust
  (package
    (name "r-maboust")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MABOUST" version))
       (sha256
        (base32 "044k29nibl5zc32zz2696kbhrz5dafgaikgmjqznsq25p48si453"))))
    (properties `((upstream-name . "MABOUST")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=MABOUST")
    (synopsis "Multi-Armed Bayesian Ordinal Utility-Based Sequential Trial")
    (description
     "Conducts and simulates the MABOUST design, including making interim decisions to
stop a treatment for inferiority or stop the trial early for superiority or
equivalency.")
    (license license:gpl2)))

(define-public r-mable
  (package
    (name "r-mable")
    (version "4.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mable" version))
       (sha256
        (base32 "1sq0i4zdd0r55qzlsmx9p88hxyvbzhaga2bx2akxmz7x7crqbz2j"))))
    (properties `((upstream-name . "mable")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rlang
                             r-quadprog
                             r-mnormt
                             r-lowrankqp
                             r-iterators
                             r-icenreg
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=mable")
    (synopsis "Maximum Approximate Bernstein/Beta Likelihood Estimation")
    (description
     "Fit data from a continuous population with a smooth density on finite interval
by an approximate Bernstein polynomial model which is a mixture of certain beta
distributions and find maximum approximate Bernstein likelihood estimator of the
unknown coefficients.  Consequently, maximum likelihood estimates of the unknown
density, distribution functions, and more can be obtained.  If the support of
the density is not the unit interval then transformation can be applied.  This
is an implementation of the methods proposed by the author of this package
published in the Journal of Nonparametric Statistics: Guan (2016)
<doi:10.1080/10485252.2016.1163349> and Guan (2017)
<doi:10.1080/10485252.2017.1374384>.  For data with covariates, under some
semiparametric regression models such as Cox proportional hazards model and the
accelerated failure time model, the baseline survival function can be estimated
smoothly based on general interval censored data.")
    (license (license:fsdg-compatible "LGPL (>= 2.0, < 3)"))))

(define-public r-mabacr
  (package
    (name "r-mabacr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "mabacR" version))
       (sha256
        (base32 "1km8yi69xc5n2ksyps2ggw21b7zy972bw3hyazj026619j71dlqg"))))
    (properties `((upstream-name . "mabacR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/slabaverse/mabacR")
    (synopsis "Assisting Decision Makers")
    (description
     "Easy implementation of the MABAC multi-criteria decision method, that was
introduced by PamuÄar and ÄiroviÄ in the work entitled: \"The selection of
transport and handling resources in logistics centers using Multi-Attributive
Border Approximation area Comparison (MABAC)\" - <doi:10.1016/j.eswa.2014.11.057>
- which aimed to choose implements for logistics centers.  This package receives
data, preferably in a spreadsheet, reads it and applies the mathematical
algorithms inherent to the MABAC method to generate a ranking with the optimal
solution according to the established criteria, weights and type of criteria.
The data will be normalized, weighted by the weights, the border area will be
determined, the distances to this border area will be calculated and finally a
ranking with the optimal option will be generated.")
    (license license:gpl2+)))

(define-public r-maat
  (package
    (name "r-maat")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "maat" version))
       (sha256
        (base32 "0slyigq90bn2dn09s2ipc13glsywwzs7y5d80zyazwwr51i23xsq"))))
    (properties `((upstream-name . "maat")))
    (build-system r-build-system)
    (propagated-inputs (list r-testdesign r-readxl r-mass r-diagram))
    (native-inputs (list r-knitr))
    (home-page "https://choi-phd.github.io/maat/")
    (synopsis "Multiple Administrations Adaptive Testing")
    (description
     "This package provides an extension of the shadow-test approach to computerized
adaptive testing (CAT) implemented in the @code{TestDesign} package for the
assessment framework involving multiple tests administered periodically
throughout the year.  This framework is referred to as the Multiple
Administrations Adaptive Testing (MAAT) and supports multiple item pools
vertically scaled and multiple phases (stages) of CAT within each test.  Between
phases and tests, transitioning from one item pool (and associated constraints)
to another is allowed as deemed necessary to enhance the quality of measurement.")
    (license license:gpl2+)))

(define-public r-maaper
  (package
    (name "r-maaper")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "MAAPER" version))
       (sha256
        (base32 "05jjfnp1hwg8pk06np6fbdfxi6zn60wzhrkzlh8jkqgwxjhgnvai"))))
    (properties `((upstream-name . "MAAPER")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsamtools
                             r-mass
                             r-iranges
                             r-genomicranges
                             r-genomicfeatures
                             r-genomicalignments
                             r-genomeinfodb))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Vivianstats/MAAPER")
    (synopsis
     "Analysis of Alternative Polyadenylation Using 3' End-Linked Reads")
    (description
     "This package provides a computational method developed for model-based analysis
of alternative polyadenylation (APA) using 3 end-linked reads.  It accurately
assigns 3 RNA-seq reads to @code{polyA} sites through statistical modeling, and
generates multiple statistics for APA analysis.  Please also see Li WV, Zheng D,
Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.")
    (license license:gpl3)))

(define-public r-m61r
  (package
    (name "r-m61r")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "m61r" version))
       (sha256
        (base32 "0mhi70rvqgzrgmp261shlq5jdxxhjawdxfsfr0cyfyyils7xs7ln"))))
    (properties `((upstream-name . "m61r")))
    (build-system r-build-system)
    (home-page "https://github.com/pv71u98h1/m61r/")
    (synopsis "Package About Data Manipulation in Pure Base R")
    (description
     "Data manipulation in one package and in base R. Minimal.  No dependencies.
dplyr and tidyr'-like in one place.  Nothing else than base R to build the
package.")
    (license license:expat)))

(define-public r-m5
  (package
    (name "r-m5")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "m5" version))
       (sha256
        (base32 "0cw9b31j564qc5s1b37r1p425p4gndlzgwy34cn0m2c65jwiyf6h"))))
    (properties `((upstream-name . "m5")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi r-lubridate r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/krzjoa/m5")
    (synopsis "'M5 Forecasting' Challenges Data")
    (description
     "This package contains functions, which facilitate downloading, loading and
preparing data from M5 Forecasting challenges (by University of Nicosia', hosted
on Kaggle').  The data itself is set of time series of different product sales
in Walmart'.  The package also includes a ready-to-use built-in M5 subset named
tiny_m5'.  For detailed information about the challenges, see: Makridakis, S. &
Spiliotis, E. & Assimakopoulos, V. (2020).
<doi:10.1016/j.ijforecast.2021.10.009>.")
    (license license:expat)))

(define-public r-m3jf
  (package
    (name "r-m3jf")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "M3JF" version))
       (sha256
        (base32 "0sz02cis8xh11fyng44j6al2fkz6ybcabdgg25bbwb3vidf1y197"))))
    (properties `((upstream-name . "M3JF")))
    (build-system r-build-system)
    (propagated-inputs (list r-snftool r-mass r-intersim r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=M3JF")
    (synopsis
     "Multi-Modal Matrix Joint Factorization for Integrative Multi-Omics Data Analysis")
    (description
     "Multi modality data matrices are factorized conjointly into the multiplication
of a shared sub-matrix and multiple modality specific sub-matrices, group sparse
constraint is applied to the shared sub-matrix to capture the homogeneous and
heterogeneous information, respectively.  Then the samples are classified by
clustering the shared sub-matrix with kmeanspp(), a new version of kmeans()
developed here to obtain concordant results.  The package also provides the
cluster number estimation by rotation cost.  Moreover, cluster specific features
could be retrieved using hypergeometric tests.")
    (license license:gpl3)))

(define-public r-m3
  (package
    (name "r-m3")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "M3" version))
       (sha256
        (base32 "18473ifcwvavic29nh9vnbrxfxfckydik3yl3hmv2djwkclx976q"))))
    (properties `((upstream-name . "M3")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-ncdf4 r-maps r-mapdata))
    (home-page "https://cran.r-project.org/package=M3")
    (synopsis "Reading M3 Files")
    (description
     "This package provides functions to read in and manipulate air quality model
output from Models3-formatted files.  This format is used by the Community
Multiscale Air Quality (CMAQ) model.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-m2smjf
  (package
    (name "r-m2smjf")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "M2SMJF" version))
       (sha256
        (base32 "0v4sg5h3h2pjp996jspv206c8cymjpn1fg38ivaw12s7bmx8j409"))))
    (properties `((upstream-name . "M2SMJF")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=M2SMJF")
    (synopsis "Multi-Modal Similarity Matrix Joint Factorization")
    (description
     "This package provides a new method to implement clustering from multiple
modality data of certain samples, the function M2S@code{MjF}() jointly
factorizes multiple similarity matrices into a shared sub-matrix and several
modality private sub-matrices, which is further used for clustering.  Along with
this method, we also provide function to calculate the similarity matrix and
function to evaluate the best cluster number from the original data.")
    (license license:gpl2+)))

(define-public r-m2smf
  (package
    (name "r-m2smf")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "M2SMF" version))
       (sha256
        (base32 "160qjrb3mxybvr4z6zra6rzf6scyrb9mplw36gqlfxj418ndnmm1"))))
    (properties `((upstream-name . "M2SMF")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=M2SMF")
    (synopsis
     "Multi-Modal Similarity Matrix Factorization for Integrative Multi-Omics Data Analysis")
    (description
     "This package provides a new method to implement clustering from multiple
modality data of certain samples, the function M2SMF() jointly factorizes
multiple similarity matrices into a shared sub-matrix and several modality
private sub-matrices, which is further used for clustering.  Along with this
method, we also provide function to calculate the similarity matrix and function
to evaluate the best cluster number from the original data.")
    (license license:gpl2+)))

(define-public r-m2r
  (package
    (name "r-m2r")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "m2r" version))
       (sha256
        (base32 "0vwvpalap4z5x2004c92n0ka7i4l64ry5n8pmxs4vjpd8fyx91gc"))))
    (properties `((upstream-name . "m2r")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-stringr
                             r-rcpp
                             r-mpoly
                             r-memoise
                             r-gmp))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/coneill-math/m2r")
    (synopsis "Interface to 'Macaulay2'")
    (description
     "Persistent interface to Macaulay2 <http://www.math.uiuc.edu/Macaulay2/> and
front-end tools facilitating its use in the R ecosystem.  For details see Kahle
et.  al. (2020) <doi:10.18637/jss.v093.i09>.")
    (license license:gpl2)))

(define-public r-m2b
  (package
    (name "r-m2b")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "m2b" version))
       (sha256
        (base32 "0agzw67mmwrw1f61yn24z5w1pgjssdapg3li0a53i3ylnij45mzr"))))
    (properties `((upstream-name . "m2b")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest
                             r-ggplot2
                             r-geosphere
                             r-e1071
                             r-catools
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ldbk/m2b")
    (synopsis "Movement to Behaviour Inference using Random Forest")
    (description
     "Prediction of behaviour from movement characteristics using observation and
random forest for the analyses of movement data in ecology.  From movement
information (speed, bearing...) the model predicts the observed behaviour
(movement, foraging...) using random forest.  The model can then extrapolate
behavioural information to movement data without direct observation of
behaviours.  The specificity of this method relies on the derivation of multiple
predictor variables from the movement data over a range of temporal windows.
This procedure allows to capture as much information as possible on the changes
and variations of movement and ensures the use of the random forest algorithm to
its best capacity.  The method is very generic, applicable to any set of data
providing movement data together with observation of behaviour.")
    (license license:gpl3)))

