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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-modeltime-ensemble 1.1.0
Propagated dependencies: r-yardstick@1.3.2 r-workflows@1.3.0 r-tune@2.0.1 r-timetk@2.9.1 r-tidyr@1.3.1 r-tictoc@1.2.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rsample@1.3.1 r-rlang@1.1.6 r-recipes@1.3.1 r-purrr@1.2.0 r-modeltime-resample@0.3.0 r-modeltime@1.3.5 r-magrittr@2.0.4 r-glmnet@4.1-10 r-generics@0.1.4 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://business-science.github.io/modeltime.ensemble/
Licenses: Expat
Build system: r
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.

r-multipanelfigure 2.1.6
Propagated dependencies: r-stringi@1.8.7 r-magrittr@2.0.4 r-magick@2.9.0 r-gtable@0.3.6 r-gridgraphics@0.5-1 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=multipanelfigure
Licenses: GPL 3+
Build system: r
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 ComplexHeatmap plots, grobs, as well as content from all image formats supported by ImageMagick (accessed through magick').

r-moodef 1.2.0
Propagated dependencies: r-xml2@1.5.0 r-xlsx@0.6.5 r-tidyr@1.3.1 r-tibble@3.3.0 r-snakecase@0.11.1 r-readxl@1.4.5 r-readr@2.1.6 r-magick@2.9.0 r-glue@1.8.0 r-dplyr@1.1.4 r-blastula@0.3.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://josesamos.github.io/moodef/
Licenses: Expat
Build system: r
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 quizzes to be included in the question bank.

r-mcl 1.0
Propagated dependencies: r-expm@1.0-0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MCL
Licenses: GPL 2+
Build system: r
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.

r-mbend 1.3.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/nilforooshan/mbend
Licenses: GPL 3
Build system: r
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>.

r-mvrsquared 0.1.5
Propagated dependencies: r-rcppthread@2.2.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/TommyJones/mvrsquared
Licenses: Expat
Build system: r
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) <arXiv:1911.11061>.

r-multidoe 0.9.4
Propagated dependencies: r-pracma@2.4.6 r-plotly@4.11.0 r-magrittr@2.0.4 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/andreamelloncelli/multiDoE
Licenses: FSDG-compatible
Build system: r
Synopsis: Multi-Criteria Design of Experiments for Optimal Design
Description:

Multi-criteria design of experiments algorithm that simultaneously optimizes up to six different criteria ('I', Id', D', Ds', A and As'). The algorithm finds the optimal Pareto front and, if requested, selects a possible symmetrical design on it. The symmetrical design is selected based on two techniques: minimum distance with the Utopia point or the TOPSIS approach.

r-mscp 1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mscp
Licenses: GPL 3
Build system: r
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.

r-mmsample 0.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 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=mmsample
Licenses: GPL 3
Build system: r
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).

r-modalclust 0.7
Propagated dependencies: r-zoo@1.8-14 r-mvtnorm@1.3-3 r-class@7.3-23
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=Modalclust
Licenses: GPL 2
Build system: r
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.

r-metawho 0.2.0
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 r-metafor@4.8-0 r-magrittr@2.0.4 r-forestmodel@0.6.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ShixiangWang/metawho
Licenses: GPL 3
Build system: r
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.

r-mvgps 1.2.2
Propagated dependencies: r-weightit@1.5.1 r-sp@2.2-0 r-rdpack@2.6.4 r-matrixnormal@0.1.2 r-mass@7.3-65 r-geometry@0.5.2 r-gbm@2.2.2 r-cobalt@4.6.2 r-cbps@0.24
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/williazo/mvGPS
Licenses: Expat
Build system: r
Synopsis: Causal Inference using Multivariate Generalized Propensity Score
Description:

This package provides methods for estimating and utilizing the multivariate generalized propensity score (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.

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+
Build system: r
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-mildsvm 0.4.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-proc@1.19.0.1 r-pillar@1.11.1 r-mvtnorm@1.3-3 r-magrittr@2.0.4 r-kernlab@0.9-33 r-e1071@1.7-16 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/skent259/mildsvm
Licenses: Expat
Build system: r
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 (2024) <doi:10.1214/24-AOAS1876>; 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.

r-missinghe 1.5.1
Propagated dependencies: r-r2jags@0.8-9 r-mcmcr@0.6.2 r-loo@2.8.0 r-gridextra@2.3 r-ggthemes@5.1.0 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ggmcmc@1.5.1.2 r-coda@0.19-4.1 r-bcea@2.4.83 r-bayesplot@1.14.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=missingHE
Licenses: GPL 2
Build system: r
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 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, 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>.

r-miivsem 0.5.8
Propagated dependencies: r-numderiv@2016.8-1.1 r-matrix@1.7-4 r-lavaan@0.6-20 r-car@3.1-3 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/zackfisher/MIIVsem
Licenses: GPL 2
Build system: r
Synopsis: Model Implied Instrumental Variable (MIIV) Estimation of Structural Equation Models
Description:

This package provides functions for estimating structural equation models using instrumental variables.

r-muacz 2.1.0
Propagated dependencies: r-ggplot2@4.0.1 r-epidisplay@3.7.0.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MUACz
Licenses: GPL 3
Build system: r
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.

r-metaphonebr 0.0.5
Propagated dependencies: r-stringi@1.8.7 r-lifecycle@1.0.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ipeadata-lab/metaphonebr
Licenses: Expat
Build system: r
Synopsis: Custom 'MetaphoneBR' Phonetic Encoding for Brazilian Names
Description:

Simplifies Brazilian names phonetically using a custom metaphoneBR algorithm that preserves ending vowels. Useful for name matching processing preserving gender information carried generally by ending vowels in Portuguese. Mation (2025) <doi:10.6082/uchicago.15104>.

r-mmoc 0.1.1.0
Propagated dependencies: r-spectrum@1.1 r-mass@7.3-65 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MMOC
Licenses: Expat
Build system: r
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).

r-mswm 1.5
Propagated dependencies: r-nlme@3.1-168
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MSwM
Licenses: GPL 2+
Build system: r
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>).

r-mvngmod 0.1.0
Propagated dependencies: r-truncnorm@1.0-9 r-pracma@2.4.6 r-maxlik@1.5-2.1 r-matlib@1.0.1 r-distributionutils@0.6-2 r-clustergeneration@1.3.8 r-bessel@0.7-0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/soonsk-vcu/MVNGmod
Licenses: Expat
Build system: r
Synopsis: Matrix-Variate Non-Gaussian Linear Regression Models
Description:

An implementation of the expectation conditional maximization (ECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res', and covariates in gaad_cov'. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.

r-microbats 0.1-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/stathwang/microbats
Licenses: GPL 2+
Build system: r
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>).

r-matman 1.1.4
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-shinywidgets@0.9.0 r-shinydashboard@0.7.3 r-shiny@1.11.1 r-plotly@4.11.0 r-parsedate@1.3.2 r-lubridate@1.9.4 r-forecast@8.24.0 r-dt@0.34.0 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=matman
Licenses: GPL 3
Build system: r
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.

r-multileveloptimalbayes 0.0.4.0
Propagated dependencies: r-pracma@2.4.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiLevelOptimalBayes
Licenses: GPL 3
Build system: r
Synopsis: Regularized Bayesian Estimator for Two-Level Latent Variable Models
Description:

This package implements a regularized Bayesian estimator that optimizes the estimation of between-group coefficients for multilevel latent variable models by minimizing mean squared error (MSE) and balancing variance and bias. The package provides more reliable estimates in scenarios with limited data, offering a robust solution for accurate parameter estimation in two-level latent variable models. It is designed for researchers in psychology, education, and related fields who face challenges in estimating between-group effects under small sample sizes and low intraclass correlation coefficients. The package includes comprehensive S3 methods for result objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(), dim(), length(), names(), and update() for enhanced usability and integration with standard R workflows. Dashuk et al. (2025a) <doi:10.1017/psy.2025.10045> derived the optimal regularized Bayesian estimator; Dashuk et al. (2025b) <doi:10.1007/s41237-025-00264-7> extended it to the multivariate case; and Luedtke et al. (2008) <doi:10.1037/a0012869> formalized the two-level latent variable framework.

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