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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-mailr 0.8
Dependencies: openjdk@25
Propagated dependencies: r-stringr@1.6.0 r-rjava@1.0-11 r-r-utils@2.13.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/rpremrajGit/mailR
Licenses: GPL 3
Build system: r
Synopsis: Utility to Send Emails from R
Description:

Interface to Apache Commons Email to send emails from R.

r-mts 1.2.1
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-fgarch@4052.93 r-fbasics@4041.97
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MTS
Licenses: FSDG-compatible
Build system: r
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.

r-marg 1.2-4
Propagated dependencies: r-survival@3.8-3 r-statmod@1.5.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://www.r-project.org
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Approximate Marginal Inference for Regression-Scale Models
Description:

This package implements likelihood inference based on higher order approximations for linear nonnormal regression models.

r-mully 2.1.38
Propagated dependencies: r-rgl@1.3.31 r-randomcolor@1.1.0.1 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/frankkramer-lab/mully
Licenses: GPL 2+
Build system: r
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>.

r-misssbm 1.0.5
Propagated dependencies: r-sbm@0.4.7 r-rspectra@0.16-2 r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r6@2.6.1 r-nloptr@2.2.1 r-matrix@1.7-4 r-magrittr@2.0.4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-future-apply@1.20.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://grosssbm.github.io/missSBM/
Licenses: GPL 3
Build system: r
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. 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>.

r-maint-data 2.7.4
Propagated dependencies: r-withr@3.0.2 r-sn@2.1.1 r-rrcov@1.7-7 r-robustbase@0.99-6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pcapp@2.0-5 r-misctools@0.6-28 r-mclust@6.1.2 r-mass@7.3-65 r-ggplot2@4.0.1 r-ggally@2.4.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MAINT.Data
Licenses: GPL 2
Build system: r
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.

r-mniw 1.0.2
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mlysy/mniw/
Licenses: GPL 3
Build system: r
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.

r-mmdcopula 0.2.1
Propagated dependencies: r-wdm@0.2.6 r-vinecopula@2.6.1 r-randtoolbox@2.0.5 r-pbapply@1.7-4 r-cubature@2.1.4-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MMDCopula
Licenses: GPL 3
Build system: r
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, Chérief-Abdellatif, Derumigny and Fermanian (2022) <doi:10.1080/01621459.2021.2024836>.

r-microhaplot 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-shinywidgets@0.9.0 r-shinybs@0.61.1 r-shiny@1.11.1 r-scales@1.4.0 r-magrittr@2.0.4 r-gtools@3.9.5 r-ggplot2@4.0.1 r-ggiraph@0.9.2 r-dt@0.34.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ngthomas/microhaplot
Licenses: GPL 3
Build system: r
Synopsis: Microhaplotype Constructor and Visualizer
Description:

This package provides a downstream bioinformatics tool to construct and assist curation of microhaplotypes from short read sequences.

r-mar1s 2.1.1
Propagated dependencies: r-zoo@1.8-14 r-fda@6.3.0 r-cmrutils@1.3.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/aparamon/mar1s
Licenses: GPL 3+
Build system: r
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.

r-manydist 0.4.9
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rsample@1.3.1 r-rfast@2.1.5.2 r-recipes@1.3.1 r-readr@2.1.6 r-purrr@1.2.0 r-philentropy@0.10.0 r-matrix@1.7-4 r-magrittr@2.0.4 r-fpc@2.2-13 r-forcats@1.0.1 r-fastdummies@1.7.5 r-entropy@1.3.2 r-dplyr@1.1.4 r-distances@0.1.13 r-data-table@1.17.8 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=manydist
Licenses: GPL 3
Build system: r
Synopsis: Unbiased Distances for Mixed-Type Data
Description:

This package provides a comprehensive framework for calculating unbiased distances in datasets containing mixed-type variables (numerical and categorical). The package implements a general formulation that ensures multivariate additivity and commensurability, meaning that variables contribute equally to the overall distance regardless of their type, scale, or distribution. Supports multiple distance measures including Gower's distance, Euclidean distance, Manhattan distance, and various categorical variable distances such as simple matching, Eskin, occurrence frequency, and association-based distances. Provides tools for variable scaling (standard deviation, range, robust range, and principal component scaling), and handles both independent and association-based category dissimilarities. Implements methods to correct for biases that typically arise from different variable types, distributions, and number of categories. Particularly useful for cluster analysis, data visualization, and other distance-based methods when working with mixed data. Methods based on van de Velden et al. (2024) <doi:10.48550/arXiv.2411.00429> "Unbiased mixed variables distance".

r-mombf 3.5.4
Propagated dependencies: r-survival@3.8-3 r-sparsematrixstats@1.22.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pracma@2.4.6 r-ncvreg@3.16.0 r-mvtnorm@1.3-3 r-mgcv@1.9-4 r-mclust@6.1.2 r-matrix@1.7-4 r-intervals@0.15.5 r-glmnet@4.1-10 r-glasso@1.11 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/davidrusi/mombf
Licenses: GPL 2+ FSDG-compatible
Build system: r
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).

r-measr 2.0.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stanheaders@2.32.10 r-s7@0.2.1 r-rstantools@2.5.0 r-rstan@2.32.7 r-rlang@1.1.6 r-rdcmchecks@0.1.0 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-psych@2.5.6 r-posterior@1.6.1 r-loo@2.8.0 r-lifecycle@1.0.4 r-glue@1.8.0 r-fs@1.6.6 r-dtplyr@1.3.2 r-dplyr@1.1.4 r-dcmstan@0.1.0 r-dcm2@1.0.2 r-cli@3.6.5 r-bridgesampling@1.2-1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://measr.r-dcm.org
Licenses: GPL 3+
Build system: r
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.

r-macleish 0.3.10
Propagated dependencies: r-xml2@1.5.0 r-stringr@1.6.0 r-sf@1.0-23 r-rvest@1.0.5 r-readr@2.1.6 r-phenocamr@1.1.5 r-lubridate@1.9.4 r-etl@0.4.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/beanumber/macleish
Licenses: CC0
Build system: r
Synopsis: Retrieve Data from MacLeish Field Station
Description:

Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald 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/discover-smith/smith-action/sustainable-smith/macleish-field-station> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures.

r-messy 0.1.0
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://nrennie.rbind.io/messy/
Licenses: FSDG-compatible
Build system: r
Synopsis: Create Messy Data from Clean Data Frames
Description:

For the purposes of teaching, it is often desirable to show examples of working with messy data and how to clean it. This R package creates messy data from clean, tidy data frames so that students have a clean example to work towards.

r-memapp 2.16
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-stringi@1.8.7 r-shinywidgets@0.9.0 r-shinyjs@2.1.0 r-shinydashboard@0.7.3 r-shinybs@0.61.1 r-shiny@1.11.1 r-rcolorbrewer@1.1-3 r-plotly@4.11.0 r-mem@2.19 r-ggplot2@4.0.1 r-formattable@0.2.1 r-dt@0.34.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/lozalojo/memapp
Licenses: GPL 2+
Build system: r
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.

r-mvmesh 1.6
Propagated dependencies: r-simplicialcubature@1.3 r-rgl@1.3.31 r-rcdd@1.6 r-geometry@0.5.2 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=mvmesh
Licenses: GPL 3+
Build system: r
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.

r-metadigitise 1.0.2
Propagated dependencies: r-purrr@1.2.0 r-magick@2.9.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=metaDigitise
Licenses: GPL 2+
Build system: r
Synopsis: Extract and Summarise Data from Published Figures
Description:

High-throughput, flexible and reproducible extraction of data from figures in primary research papers. 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.

r-mscombine 1.4
Propagated dependencies: r-plyr@1.8.9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MScombine
Licenses: GPL 2
Build system: r
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.

r-msmu 0.1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MSMU
Licenses: Expat
Build system: r
Synopsis: Descriptive Statistics Functions for Numeric Data
Description:

This package provides fundamental functions for descriptive statistics, including MODE(), estimate_mode(), center_stats(), position_stats(), pct(), spread_stats(), kurt(), skew(), and shape_stats(), which assist in summarizing the center, spread, and shape of numeric data. For more details, see McCurdy (2025), "Introduction to Data Science with R" <https://jonmccurdy.github.io/Introduction-to-Data-Science/>.

r-mstest 0.1.8
Propagated dependencies: r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pso@1.0.4 r-pracma@2.4.6 r-numderiv@2016.8-1.1 r-nloptr@2.2.1 r-gensa@1.1.15 r-ga@3.2.4 r-foreach@1.5.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/roga11/MSTest
Licenses: GPL 2+
Build system: r
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 (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2025_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20251014.pdf> that can be used to identify the number of regimes in Markov switching models.

r-motif 0.6.5
Propagated dependencies: r-tibble@3.3.0 r-stars@0.6-8 r-sf@1.0-23 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-philentropy@0.10.0 r-comat@0.9.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://jakubnowosad.com/motif/
Licenses: Expat
Build system: r
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>).

r-manytests 1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=ManyTests
Licenses: GPL 2
Build system: r
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>.

r-metasem 1.5.0
Propagated dependencies: r-openmx@2.22.10 r-numderiv@2016.8-1.1 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-mass@7.3-65 r-lavaan@0.6-20 r-ellipse@0.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mikewlcheung/metasem
Licenses: GPL 2+
Build system: r
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 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>.

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