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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-migui 1.3
Propagated dependencies: r-mi@1.2 r-gwidgets2@1.0-10 r-arm@1.14-4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=migui
Licenses: GPL 2+
Build system: r
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.

r-muir 0.1.0
Propagated dependencies: r-stringr@1.6.0 r-dplyr@1.1.4 r-diagrammer@1.0.11
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/alforj/muir
Licenses: GPL 2+
Build system: r
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.

r-mizer 2.5.4
Propagated dependencies: r-rlang@1.1.6 r-reshape2@1.4.5 r-rcpp@1.1.0 r-progress@1.2.3 r-plyr@1.8.9 r-plotly@4.11.0 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-desolve@1.40 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://sizespectrum.org/mizer/
Licenses: GPL 3
Build system: r
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.

r-mmeta 3.0.2
Propagated dependencies: r-ggplot2@4.0.1 r-aod@1.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mmeta
Licenses: GPL 2+
Build system: r
Synopsis: Multivariate Meta-Analysis
Description:

Multiple 2 by 2 tables often arise in meta-analysis which combines statistical evidence from multiple studies. Two risks within the same study are possibly correlated because they share some common factors such as environment and population structure. This package implements a set of novel Bayesian approaches for multivariate meta analysis when the risks within the same study are independent or correlated. The exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 by 2 table or multiple 2 by 2 tables is provided. Luo, Chen, Su, Chu, (2014) <doi:10.18637/jss.v056.i11>, Chen, Luo, (2011) <doi:10.1002/sim.4248>, Chen, Chu, Luo, Nie, Chen, (2015) <doi:10.1177/0962280211430889>, Chen, Luo, Chu, Su, Nie, (2014) <doi:10.1080/03610926.2012.700379>, Chen, Luo, Chu, Wei, (2013) <doi:10.1080/19466315.2013.791483>.

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
Build system: r
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-mplustrees 0.2.3
Propagated dependencies: r-rpart-plot@3.1.4 r-rpart@4.1.24 r-nlme@3.1-168 r-mplusautomation@1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MplusTrees
Licenses: GPL 2+ GPL 3+
Build system: r
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.

r-mixedfact 0.1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mixedfact
Licenses: GPL 3
Build system: r
Synopsis: Generate and Analyze Mixed-Level Blocked Factorial Designs
Description:

Generates blocked designs for mixed-level factorial experiments for a given block size. Internally, it uses finite-field based, collapsed, and heuristic methods to construct block structures that minimize confounding between block effects and factorial effects. The package creates the full treatment combination table, partitions runs into blocks, and computes detailed confounding diagnostics for main effects and two-factor interactions. It also checks orthogonal factorial structure (OFS) and computes efficiencies of factorial effects using the methods of Nair and Rao (1948) <doi:10.1111/j.2517-6161.1948.tb00005.x>. When OFS is not satisfied but the design has equal treatment replications and equal block sizes, a general method based on the C-matrix and custom contrast vectors is used to compute efficiencies. The output includes the generated design, finite-field metadata, confounding summaries, OFS diagnostics, and efficiency results.

r-mqrcm 1.3
Propagated dependencies: r-pch@2.2 r-hmisc@5.2-4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=Mqrcm
Licenses: GPL 2
Build system: r
Synopsis: M-Quantile Regression Coefficients Modeling
Description:

Parametric modeling of M-quantile regression coefficient functions.

r-mseapca 2.2.1
Propagated dependencies: r-xml@3.99-0.20 r-loadings@0.5.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mseapca
Licenses: LGPL 3
Build system: r
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>), single sample enrichment analysis (SSEA) (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>) and over-representation analysis (ORA) that accounts for undetected metabolites (Yamamoto, H. (2024) <doi:10.51094/jxiv.954>).

r-mispr 1.0.0
Propagated dependencies: r-penalized@0.9-53 r-mass@7.3-65 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mispr
Licenses: GPL 2
Build system: r
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>.

r-modeldatatoo 0.3.0
Propagated dependencies: r-pins@1.4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/tidymodels/modeldatatoo
Licenses: Expat
Build system: r
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.

r-multisite-accuracy 1.3
Propagated dependencies: r-survival@3.8-3 r-proc@1.19.0.1 r-metafor@4.8-0 r-logistf@1.26.1 r-lmertest@3.1-3 r-lme4@1.1-37 r-coxme@2.2-22
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multisite.accuracy
Licenses: GPL 3
Build system: r
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.

r-my-stepwise 0.1.0
Propagated dependencies: r-survival@3.8-3 r-lmtest@0.9-40 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=My.stepwise
Licenses: GPL 3+
Build system: r
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.

r-mmgfm 1.2.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-multicoap@1.1 r-mass@7.3-65 r-irlba@2.3.5.1 r-gfm@1.2.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MMGFM
Licenses: GPL 3
Build system: r
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. (2025) <doi:10.48550/arXiv.2507.09889>.

r-multiwayvcov 1.2.3
Propagated dependencies: r-sandwich@3.1-1 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://sites.google.com/site/npgraham1/research/code
Licenses: FreeBSD
Build system: r
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.

r-mvvg 0.1.0
Propagated dependencies: r-psych@2.5.6 r-nlme@3.1-168 r-mixmatrix@0.2.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvvg
Licenses: Expat
Build system: r
Synopsis: Matrix-Variate Variance-Gamma Distribution
Description:

Rudimentary functions for sampling and calculating density from the matrix-variate variance-gamma distribution.

r-materialmodifier 1.2.0
Propagated dependencies: r-stringr@1.6.0 r-readbitmap@0.1.5 r-png@0.1-8 r-moments@0.14.1 r-magrittr@2.0.4 r-jpeg@0.1-11 r-imager@1.0.5 r-downloader@0.4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/tsuda16k/materialmodifier
Licenses: Expat
Build system: r
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>.

r-maaper 1.1.1
Propagated dependencies: r-rsamtools@2.26.0 r-mass@7.3-65 r-iranges@2.44.0 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-genomeinfodb@1.46.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/Vivianstats/MAAPER
Licenses: GPL 3
Build system: r
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 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>.

r-mmpp 0.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mmpp
Licenses: GPL 2
Build system: r
Synopsis: Various Similarity and Distance Metrics for Marked Point Processes
Description:

Compute similarities and distances between marked point processes.

r-marsgwr 0.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MARSGWR
Licenses: GPL 2+
Build system: r
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.

r-mdw 2024.8-1
Propagated dependencies: r-matrix@1.7-4 r-mass@7.3-65 r-kyotil@2024.11-01
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mdw
Licenses: GPL 2
Build system: r
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>.

r-multiphen 2.0.3
Propagated dependencies: r-rcolorbrewer@1.1-3 r-meta@8.2-1 r-mass@7.3-65 r-hardyweinberg@1.7.9 r-epitools@0.5-10.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiPhen
Licenses: GPL 2
Build system: r
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).

r-memshare 1.1.0
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://www.iap-gmbh.de
Licenses: GPL 3
Build system: r
Synopsis: Shared Memory Multithreading
Description:

This project extends R with a mechanism for efficient parallel data access by utilizing C++ shared memory. Large data objects can be accessed and manipulated directly from R without redundant copying, providing both speed and memory efficiency.

r-multirl 0.2.3
Propagated dependencies: r-scales@1.4.0 r-rcpp@1.1.0 r-progressr@0.18.0 r-ggplot2@4.0.1 r-future@1.68.0 r-foreach@1.5.2 r-dorng@1.8.6.2 r-dofuture@1.1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://yuki-961004.github.io/multiRL/
Licenses: GPL 3
Build system: r
Synopsis: Reinforcement Learning Tools for Multi-Armed Bandit
Description:

This package provides a flexible general-purpose toolbox for implementing Rescorla-Wagner models in multi-armed bandit tasks. As the successor and functional extension of the binaryRL package, multiRL modularizes the Markov Decision Process (MDP) into six core components. This framework enables users to construct custom models via intuitive if-else syntax and define latent learning rules for agents. For parameter estimation, it provides both likelihood-based inference (MLE and MAP) and simulation-based inference (ABC and RNN), with full support for parallel processing across subjects. The workflow is highly standardized, featuring four main functions that strictly follow the four-step protocol (and ten rules) proposed by Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Beyond the three built-in models (TD, RSTD, and Utility), users can easily derive new variants by declaring which variables are treated as free parameters.

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