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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-mcpan 1.1-21
Propagated dependencies: r-plyr@1.8.9 r-mvtnorm@1.3-3 r-multcomp@1.4-29 r-mcmcpack@1.7-1 r-magic@1.6-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MCPAN
Licenses: GPL 2
Build system: r
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.

r-maths-genealogy 0.1.4
Propagated dependencies: r-websocket@1.4.4 r-rvest@1.0.5 r-rlang@1.1.6 r-later@1.4.4 r-jsonlite@2.0.0 r-httr2@1.2.1 r-curl@7.0.0 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://genealogy.louisaslett.com/
Licenses: GPL 2+
Build system: r
Synopsis: Mathematics PhD Genealogy Data and Plotting
Description:

Query, extract, and plot genealogical data from The Mathematics Genealogy Project <https://mathgenealogy.org/>. Data is gathered from the WebSocket server run by the geneagrapher-core project <https://github.com/davidalber/geneagrapher-core>.

r-mrf2d 1.0
Propagated dependencies: r-tidyr@1.3.1 r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/Freguglia/mrf2d
Licenses: GPL 3
Build system: r
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>.

r-mriml 2.2.0
Propagated dependencies: r-yardstick@1.3.2 r-workflows@1.3.0 r-tune@2.0.1 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rsample@1.3.1 r-rlang@1.1.6 r-recipes@1.3.1 r-purrr@1.2.0 r-patchwork@1.3.2 r-metricsweighted@1.0.4 r-magrittr@2.0.4 r-hstats@1.2.2 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-flashlight@1.0.0 r-finetune@1.2.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/nickfountainjones/mrIML
Licenses: Expat
Build system: r
Synopsis: Multi-Response (Multivariate) Interpretable Machine Learning
Description:

Builds and interprets multi-response machine learning models using tidymodels syntax. Users can supply a tidy model, and mrIML automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) <doi:10.1111/1755-0998.13495> and Fountain-Jones et al. (2024) <doi:10.22541/au.172676147.77148600/v1>.

r-mcmodule 1.1.1
Propagated dependencies: r-mc2d@0.2.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://nataliaciria.github.io/mcmodule/
Licenses: GPL 3+
Build system: r
Synopsis: Modular Monte Carlo Risk Analysis
Description:

Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of mc2d to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, perform multivariate Monte Carlo node operations, automate the creation of Monte Carlo nodes and visualize risk analysis models. For more details see Ciria (2025) <https://nataliaciria.github.io/mcmodule/articles/mcmodule>.

r-mediationsens 0.0.3
Propagated dependencies: r-mediation@4.5.1 r-distr@2.9.7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mediationsens
Licenses: GPL 2
Build system: r
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.

r-mixdist 0.5-5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://www.r-project.org/
Licenses: GPL 2+
Build system: r
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.

r-mvbutils 2.8.232
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvbutils
Licenses: GPL 2+
Build system: r
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.

r-multimediate 0.1.4
Propagated dependencies: r-timereg@2.0.7 r-rmutil@1.1.10 r-mvtnorm@1.3-3 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://samarafk.github.io/multimediate/
Licenses: GPL 3
Build system: r
Synopsis: Causal Mediation Analysis in Presence of Multiple Mediators Uncausally Related
Description:

Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jérolon et al., (2021) <doi:10.1515/ijb-2019-0088> and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) <doi:10.1101/2024.02.16.24302923>.

r-metaensembler 0.1.0
Propagated dependencies: r-randomforest@4.7-1.2 r-gridextra@2.3 r-ggplot2@4.0.1 r-gbm@2.2.2 r-e1071@1.7-16 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=metaEnsembleR
Licenses: GPL 2+
Build system: r
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.

r-milineage 2.1
Propagated dependencies: r-mass@7.3-65 r-geepack@1.3.13 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=miLineage
Licenses: GPL 2+
Build system: r
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).

r-mufimeshgp 0.0.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-lhs@1.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MuFiMeshGP
Licenses: LGPL 2.0+
Build system: r
Synopsis: Multi-Fidelity Emulator for Computer Experiments with Tunable Fidelity Levels
Description:

Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, <doi:10.48550/arXiv.2503.23158>).

r-mlsurvlrnrs 0.0.8
Propagated dependencies: r-r6@2.6.1 r-mllrnrs@0.0.8 r-mlexperiments@1.0.0 r-kdry@0.0.3 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/kapsner/mlsurvlrnrs
Licenses: GPL 3+
Build system: r
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.

r-multitraits 0.6.0
Propagated dependencies: r-vegan@2.7-2 r-scatterplot3d@0.3-44 r-rpart@4.1.24 r-rlang@1.1.6 r-magrittr@2.0.4 r-igraph@2.2.1 r-hmisc@5.2-4 r-ggsci@4.1.0 r-ggrepel@0.9.6 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-corrplot@0.95 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiTraits
Licenses: GPL 3
Build system: r
Synopsis: Analyzing and Visualizing Multidimensional Plant Traits
Description:

This package implements analytical methods for multidimensional plant traits, including Competitors-Stress tolerators-Ruderals strategy analysis using leaf traits, Leaf-Height-Seed strategy analysis, Niche Periodicity Table analysis, and Trait Network analysis. Provides functions for data analysis, visualization, and network metrics calculation. Methods are based on Grime (1974) <doi:10.1038/250026a0>, Pierce et al. (2017) <doi:10.1111/1365-2435.12882>, Westoby (1998) <doi:10.1023/A:1004327224729>, Winemiller et al. (2015) <doi:10.1111/ele.12462>, He et al. (2020) <doi:10.1016/j.tree.2020.06.003>.

r-mpluslgm 1.0.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-purrr@1.2.0 r-mplusautomation@1.2 r-magrittr@2.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/OlivierPDS/MplusLGM
Licenses: GPL 3+
Build system: r
Synopsis: Automate Latent Growth Mixture Modelling in 'Mplus'
Description:

Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the MplusAutomation package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. âMplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.â Structural Equation Modeling, 25(4), 621â 638. <doi:10.1080/10705511.2017.1402334> The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). â An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.â Advances in Life Course Research, 43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.

r-masstimate 2.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MASSTIMATE
Licenses: GPL 2+
Build system: r
Synopsis: Body Mass Estimation Equations for Vertebrates
Description:

Estimation equations are from a variety of sources and associated error estimation.

r-mapsfinland 0.2.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mapsFinland
Licenses: GPL 2
Build system: r
Synopsis: Maps of Finland
Description:

Maps and other related data of Finland.

r-medseq 1.4.2
Propagated dependencies: r-weightedcluster@2.0 r-traminer@2.2-13 r-stringdist@0.9.15 r-seriation@1.5.8 r-nnet@7.3-20 r-matrixstats@1.5.0 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=MEDseq
Licenses: GPL 3+
Build system: r
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.

r-metapro 1.5.11
Propagated dependencies: r-metap@1.12
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=metapro
Licenses: GPL 2+
Build system: r
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 Fisherâ s method we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisherâ s 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>.

r-mcstats 0.1.3
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-magrittr@2.0.4 r-gridextra@2.3 r-ggthemes@5.1.0 r-ggplot2@4.0.1 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=mcStats
Licenses: GPL 3
Build system: r
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.

r-mvord 1.2.6
Propagated dependencies: r-ucminf@1.2.2 r-pbivnorm@0.6.0 r-optimx@2025-4.9 r-numderiv@2016.8-1.1 r-mvtnorm@1.3-3 r-mnormt@2.1.1 r-minqa@1.2.8 r-matrix@1.7-4 r-mass@7.3-65 r-dfoptim@2023.1.0 r-bb@2019.10-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/lauravana/mvord
Licenses: GPL 3
Build system: r
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>.

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-manorm2 1.2.2
Propagated dependencies: r-statmod@1.5.1 r-scales@1.4.0 r-locfit@1.5-9.12
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/tushiqi/MAnorm2
Licenses: GPL 3
Build system: r
Synopsis: Tools for Normalizing and Comparing ChIP-seq Samples
Description:

Chromatin immunoprecipitation followed by high-throughput sequencing (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 ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable 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.

r-modules 0.13.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/wahani/modules
Licenses: Expat
Build system: r
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.

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