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      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-mwtensor 1.1.0
Propagated dependencies: r-rtensor@1.4.9 r-nntensor@1.3.0 r-mass@7.3-65 r-itensor@1.0.2 r-igraph@2.2.1 r-cctensor@1.0.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/rikenbit/mwTensor
Licenses: Expat
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 (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.

r-mammalcol 0.2.9
Propagated dependencies: r-sf@1.0-23 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-geodata@0.6-6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/dlizcano/mammalcol
Licenses: Expat
Synopsis: Access to the List of Mammal Species of Colombia
Description:

The goal of mammalcol is to provide easy access to a meticulously structured dataset of Colombian mammal species in R. The 2025 update includes comprehensive, detailed species accounts, and distribution information.

r-moveez 1.1.1
Propagated dependencies: r-gpabin@1.1.1 r-ggplot2@4.0.1 r-gganimate@1.0.11 r-dplyr@1.1.4 r-biplotez@2.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://muvisu.github.io/moveEZ/
Licenses: Expat
Synopsis: Animated Biplots
Description:

Create animated biplots that enables dynamic visualisation of temporal or sequential changes in multivariate data by animating a single biplot across the levels of a time variable. It builds on objects from the biplotEZ package, Lubbe S, le Roux N, Nienkemper-Swanepoel J, Ganey R, Buys R, Adams Z, Manefeldt P (2024) <doi:10.32614/CRAN.package.biplotEZ>, allowing users to create animated biplots that reveal how both samples and variables evolve over time.

r-midasr 0.9
Propagated dependencies: r-zoo@1.8-14 r-texreg@1.39.4 r-sandwich@3.1-1 r-quantreg@6.1 r-optimx@2025-4.9 r-numderiv@2016.8-1.1 r-matrix@1.7-4 r-mass@7.3-65 r-formula@1.2-5 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://mpiktas.github.io/midasr/
Licenses: GPL 2 FSDG-compatible
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.

r-mappp 1.0.0
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 r-progress@1.2.3 r-pbmcapply@1.5.1 r-parallelly@1.45.1 r-memoise@2.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/cole-brokamp/mappp
Licenses: Expat
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.

r-milorgwas 0.7.1
Dependencies: zlib@1.3.1
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-gaston@1.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=milorGWAS
Licenses: GPL 3
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>.

r-mlmts 1.1.2
Propagated dependencies: r-waveslim@1.8.5 r-tsfeatures@1.1.1 r-tserieschaos@0.1-13.1 r-tseries@0.10-58 r-tsdist@3.7.1 r-tsclust@1.3.2 r-tsa@1.3.1 r-rfast@2.1.5.2 r-rdpack@2.6.4 r-ranger@0.17.0 r-randomforest@4.7-1.2 r-quantspec@1.2-4 r-psych@2.5.6 r-pspline@1.0-21 r-pracma@2.4.6 r-multiwave@1.4 r-mts@1.2.1 r-matrix@1.7-4 r-mass@7.3-65 r-igraph@2.2.1 r-ggplot2@4.0.1 r-geigen@2.3 r-freqdom@2.0.5 r-forecast@8.24.0 r-fda-usc@2.2.0 r-e1071@1.7-16 r-dtw@1.23-1 r-desctools@0.99.60 r-complexplus@2.1 r-clusterr@1.3.5 r-caret@7.0-1 r-aid@3.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mlmts
Licenses: GPL 2
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 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'.

r-mmdai 2.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MMDai
Licenses: GPL 2+
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>.

r-mccmeiv 2.1
Propagated dependencies: r-survival@3.8-3 r-numderiv@2016.8-1.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mccmeiv
Licenses: GPL 2
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).

r-mini007 0.2.2
Propagated dependencies: r-uuid@1.2-1 r-r6@2.6.1 r-glue@1.8.0 r-ellmer@0.4.0 r-diagrammer@1.0.11 r-cli@3.6.5 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mini007
Licenses: Expat
Synopsis: Lightweight Framework for Orchestrating Multi-Agent Large Language Models
Description:

This package provides tools for creating agents with persistent state using R6 classes <https://cran.r-project.org/package=R6> and the ellmer package <https://cran.r-project.org/package=ellmer>. Tracks prompts, messages, and agent metadata for reproducible, multi-turn large language model sessions.

r-mrgsim-parallel 0.3.0
Propagated dependencies: r-mrgsolve@1.6.1 r-future-apply@1.20.0 r-future@1.68.0 r-fst@0.9.8 r-dplyr@1.1.4 r-callr@3.7.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/kylebaron/mrgsim.parallel
Licenses: GPL 2+
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>.

r-multiclassroc 0.1.0
Propagated dependencies: r-proc@1.19.0.1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiClassROC
Licenses: GPL 3
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).

r-multinomiallogitmix 1.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-mvtnorm@1.3-3 r-matrixstats@1.5.0 r-mass@7.3-65 r-label-switching@1.8 r-ggplot2@4.0.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multinomialLogitMix
Licenses: GPL 2
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>.

r-missalpha 0.2.0
Propagated dependencies: r-nloptr@2.2.1 r-ga@3.2.4 r-deoptim@2.2-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=missalpha
Licenses: Expat
Synopsis: Find Range of Cronbach Alpha with a Dataset Including Missing Data
Description:

This package provides functions to calculate the minimum and maximum possible values of Cronbach's alpha when item-level missing data are present. Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011 <doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 <doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature.

r-mvna 2.0.1
Propagated dependencies: r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvna
Licenses: Expat
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>.

r-mixgb 2.0.3
Propagated dependencies: r-xgboost@1.7.11.1 r-rfast@2.1.5.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mice@3.18.0 r-matrix@1.7-4 r-magrittr@2.0.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/agnesdeng/mixgb
Licenses: GPL 3+
Synopsis: Multiple Imputation Through 'XGBoost'
Description:

Multiple imputation using XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.

r-massivegst 1.2.4
Propagated dependencies: r-writexls@6.8.0 r-visnetwork@2.1.4 r-igraph@2.2.1 r-formattable@0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: <https://github.com/stefanoMP/massiveGST>
Licenses: GPL 3+
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.

r-muvr2 0.1.0
Propagated dependencies: r-ranger@0.17.0 r-randomforest@4.7-1.2 r-psych@2.5.6 r-proc@1.19.0.1 r-mgcv@1.9-4 r-magrittr@2.0.4 r-glmnet@4.1-10 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/MetaboComp/MUVR2
Licenses: GPL 3
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.

r-mlmi 1.1.3
Propagated dependencies: r-norm@1.0-11.1 r-nlme@3.1-168 r-mix@1.0-13 r-matrix@1.7-4 r-mass@7.3-65 r-gsl@2.1-9 r-cat@0.0-9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mlmi
Licenses: GPL 3
Synopsis: Maximum Likelihood Multiple Imputation
Description:

This package implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputation methods are available, by utilising the norm', cat and mix packages. Inferences can be performed either using Rubin's rules (for proper imputation), or a modified version for maximum likelihood imputation. For maximum likelihood imputations a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935> is also available.

r-mgwrsar 1.1
Propagated dependencies: r-stringr@1.6.0 r-sp@2.2-0 r-smut@1.1 r-sf@1.0-23 r-rlang@1.1.6 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-nabor@0.5.0 r-microbenchmark@1.5.0 r-mgcv@1.9-4 r-mboost@2.9-11 r-matrix@1.7-4 r-mapview@2.11.4 r-leaflet@2.2.3 r-knitr@1.50 r-htmltools@0.5.8.1 r-gridextra@2.3 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mgwrsar
Licenses: GPL 2+
Synopsis: GWR, Mixed GWR and Multiscale GWR with Spatial Autocorrelation
Description:

This package provides functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.

r-maxeff 0.2.0
Propagated dependencies: r-spatstat-geom@3.6-1 r-rpart@4.1.24 r-groupedhyperframe@0.3.2 r-foreach@1.5.2 r-doparallel@1.0.17 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/tingtingzhan/maxEff
Licenses: GPL 2
Synopsis: Additional Predictor with Maximum Effect Size
Description:

This package provides methods of selecting one from many numeric predictors for a regression model, to ensure that the additional predictor has the maximum effect size.

r-mlr3fda 0.3.0
Propagated dependencies: r-tf@0.3.4 r-r6@2.6.1 r-paradox@1.0.1 r-mlr3pipelines@0.10.0 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-lgr@0.5.0 r-data-table@1.17.8 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://mlr3fda.mlr-org.com
Licenses: LGPL 3
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 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'.

r-mixsal 1.0
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MixSAL
Licenses: GPL 2+
Synopsis: Mixtures of Multivariate Shifted Asymmetric Laplace (SAL) Distributions
Description:

The current version of the 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).

r-mat 2.3.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MAT
Licenses: FSDG-compatible
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>.

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