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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-multivariateanalysis 0.5.1
Propagated dependencies: r-rstudioapi@0.17.1 r-plotly@4.11.0 r-pcamixdata@3.1 r-nbclust@3.0.1 r-magrittr@2.0.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-ggdendro@0.2.0 r-factoextra@1.0.7 r-ecodist@2.1.3 r-crayon@1.5.3 r-corrplot@0.95 r-candisc@1.1.0 r-biotools@4.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultivariateAnalysis
Licenses: GPL 3
Build system: r
Synopsis: Pacote Para Analise Multivariada
Description:

Package with multivariate analysis methodologies for experiment evaluation. The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA, principal components, canonical variables, etc. (Pacote com metodologias de analise multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.).

r-maictools 0.1.1
Propagated dependencies: r-vim@6.2.6 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-survminer@0.5.1 r-survival@3.8-3 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-broom@1.0.10 r-boot@1.3-32 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MAICtools
Licenses: Expat
Build system: r
Synopsis: Performing Matched-Adjusted Indirect Comparisons (MAIC)
Description:

This package provides a generalised workflow for Matching-Adjusted Indirect Comparison (MAIC) analysis, which supports both anchored and non-anchored MAIC methods. In MAIC, unbiased trial outcome comparison is achieved by weighting the subject-level outcomes of the intervention trial so that the weighted aggregate measures of prognostic or effect-modifying variables match those of the comparator trial. Measurements supported include time-to-event (e.g., overall survival) and binary (e.g., objective tumor response). The method is described in Signorovitch et al. (2010) <doi:10.2165/11538370-000000000-00000> and Signorovitch et al. (2012) <doi:10.1016/j.jval.2012.05.004>.

r-morphemepiece 1.2.3
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-piecemaker@1.0.2 r-morphemepiece-data@1.2.0 r-memoise@2.0.1 r-magrittr@2.0.4 r-fastmatch@1.1-6 r-dlr@1.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/macmillancontentscience/morphemepiece
Licenses: FSDG-compatible
Build system: r
Synopsis: Morpheme Tokenization
Description:

Tokenize text into morphemes. The morphemepiece algorithm uses a lookup table to determine the morpheme breakdown of words, and falls back on a modified wordpiece tokenization algorithm for words not found in the lookup table.

r-medlea 1.0.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MedLEA
Licenses: GPL 3
Build system: r
Synopsis: Morphological and Structural Features of Medicinal Leaves
Description:

This package contains a dataset of morphological and structural features of Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (<http://www.instituteofayurveda.org/plants/>). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka.

r-multiway 1.0-7
Propagated dependencies: r-cmls@1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multiway
Licenses: GPL 2+
Build system: r
Synopsis: Component Models for Multi-Way Data
Description:

Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.

r-metahelper 1.0.0
Propagated dependencies: r-magrittr@2.0.4 r-confintr@1.0.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/RobertEmprechtinger/metaHelper
Licenses: Expat
Build system: r
Synopsis: Transforms Statistical Measures Commonly Used for Meta-Analysis
Description:

Helps calculate statistical values commonly used in meta-analysis. It provides several methods to compute different forms of standardized mean differences, as well as other values such as standard errors and standard deviations. The methods used in this package are described in the following references: Altman D G, Bland J M. (2011) <doi:10.1136/bmj.d2090> Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) <doi:10.1002/9780470743386.ch4> Chinn S. (2000) <doi:10.1002/1097-0258(20001130)19:22%3C3127::aid-sim784%3E3.0.co;2-m> Cochrane Handbook (2011) <https://handbook-5-1.cochrane.org/front_page.htm> Cooper, H., Hedges, L. V., & Valentine, J. C. (2009) <https://psycnet.apa.org/record/2009-05060-000> Cohen, J. (1977) <https://psycnet.apa.org/record/1987-98267-000> Ellis, P.D. (2009) <https://www.psychometrica.de/effect_size.html> Goulet-Pelletier, J.-C., & Cousineau, D. (2018) <doi:10.20982/tqmp.14.4.p242> Hedges, L. V. (1981) <doi:10.2307/1164588> Hedges L. V., Olkin I. (1985) <doi:10.1016/C2009-0-03396-0> Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et al. (2023) <doi:10.1136/bmj-2022-073141> Mayer M (2023) <https://search.r-project.org/CRAN/refmans/confintr/html/ci_proportion.html> Stackoverflow (2014) <https://stats.stackexchange.com/questions/82720/confidence-interval-around-binomial-estimate-of-0-or-1> Stackoverflow (2018) <https://stats.stackexchange.com/q/338043>.

r-mcpmod 1.0-10.1
Propagated dependencies: r-mvtnorm@1.3-3 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=MCPMod
Licenses: GPL 3
Build system: r
Synopsis: Design and Analysis of Dose-Finding Studies
Description:

This package implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCP-Mod methodology. Please note: The MCPMod package will not be further developed, all future development of the MCP-Mod methodology will be done in the DoseFinding R-package.

r-muimaterial 0.1.2
Propagated dependencies: r-shiny-react@0.4.0 r-shiny@1.11.1 r-htmltools@0.5.8.1 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://felixluginbuhl.com/muiMaterial/
Licenses: Expat
Build system: r
Synopsis: 'Material UI' for 'shiny' Apps and 'Quarto'
Description:

This package provides a set of user interface components for building shiny applications and quarto documents, including inputs, layouts, navigation, surfaces, and various utilities. All components Material UI from the company MUI <https://mui.com/> are available and all inputs have usage examples in R.

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
Build system: r
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-mupet 0.1.0
Propagated dependencies: r-yardstick@1.3.2 r-rlang@1.1.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/astamm/mupet
Licenses: Expat
Build system: r
Synopsis: Multiclass Performance Evaluation Toolkit
Description:

Implementation of custom tidymodels metrics for multi-class prediction models with a single negative class. Currently are implemented macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020) "Imbalance accuracy metric for model selection in multi-class imbalance classification problemsâ <doi:10.1016/j.knosys.2020.106490> and a generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng J.X and Chen P.Y. (2013) â Weighted Youden index and its two-independent-sample comparison based on weighted sensitivity and specificityâ <doi:10.3760/cma.j.issn.0366-6999.20123102>.

r-mapycusmaximus 1.0.7
Propagated dependencies: r-sf@1.0-23 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://alex-nguyen-vn.github.io/mapycusmaximus/
Licenses: Expat
Build system: r
Synopsis: Focus-Glue-Context Fisheye Transformations for Spatial Visualization
Description:

Focus-glue-context (FGC) fisheye transformations to two-dimensional coordinates and spatial vector geometries. Implements a smooth radial distortion that enlarges a focal region, transitions through a glue ring, and preserves outside context. Methods build on generalized fisheye views and focus+context mapping. For more details see Furnas (1986) <doi:10.1145/22339.22342>, Furnas (2006) <doi:10.1145/1124772.1124921> and Yamamoto et al. (2009) <doi:10.1145/1653771.1653788>.

r-mltest 1.0.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mltest
Licenses: GPL 2
Build system: r
Synopsis: Classification Evaluation Metrics
Description:

This package provides a fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.

r-multiwayregression 1.2
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=MultiwayRegression
Licenses: GPL 3
Build system: r
Synopsis: Perform Tensor-on-Tensor Regression
Description:

This package provides functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>]. Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.

r-mrtanalysis 0.4.1
Propagated dependencies: r-sandwich@3.1-1 r-rootsolve@1.8.2.4 r-ranger@0.17.0 r-randomforest@4.7-1.2 r-mgcv@1.9-4 r-geepack@1.3.13 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MRTAnalysis
Licenses: GPL 3
Build system: r
Synopsis: Assessing Proximal, Distal, and Mediated Causal Excursion Effects for Micro-Randomized Trials
Description:

This package provides methods to analyze micro-randomized trials (MRTs) with binary treatment options. Supports four types of analyses: (1) proximal causal excursion effects, including weighted and centered least squares (WCLS) for continuous proximal outcomes by Boruvka et al. (2018) <doi:10.1080/01621459.2017.1305274> and the estimator for marginal excursion effect (EMEE) for binary proximal outcomes by Qian et al. (2021) <doi:10.1093/biomet/asaa070>; (2) distal causal excursion effects (DCEE) for continuous distal outcomes using a two-stage estimator by Qian (2025) <doi:10.1093/biomtc/ujaf134>; (3) mediated causal excursion effects (MCEE) for continuous distal outcomes, estimating natural direct and indirect excursion effects in the presence of time-varying mediators by Qian (2025) <doi:10.48550/arXiv.2506.20027>; and (4) standardized proximal effect size estimation for continuous proximal outcomes, generalizing the approach in Luers et al. (2019) <doi:10.1007/s11121-017-0862-5> to allow adjustment for baseline and time-varying covariates for improved efficiency.

r-moder 0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/lhdjung/moder
Licenses: Expat
Build system: r
Synopsis: Mode Estimation
Description:

Determines single or multiple modes (most frequent values). Checks if missing values make this impossible, and returns NA in this case. Dependency-free source code. See Franzese and Iuliano (2019) <doi:10.1016/B978-0-12-809633-8.20354-3>.

r-mcboost 0.4.4
Propagated dependencies: r-rpart@4.1.24 r-rmarkdown@2.30 r-r6@2.6.1 r-mlr3pipelines@0.10.0 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-glmnet@4.1-10 r-data-table@1.17.8 r-checkmate@2.3.3 r-backports@1.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mlr-org/mcboost
Licenses: LGPL 3+
Build system: r
Synopsis: Multi-Calibration Boosting
Description:

This package implements Multi-Calibration Boosting (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and Multi-Accuracy Boosting (2019) <doi:10.48550/arXiv.1805.12317> for the multi-calibration of a machine learning model's prediction. MCBoost updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.

r-metasnf 2.1.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-snftool@2.3.1 r-rlang@1.1.6 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-progressr@0.18.0 r-mclust@6.1.2 r-mass@7.3-65 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-digest@0.6.39 r-data-table@1.17.8 r-cluster@2.1.8.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://branchlab.github.io/metasnf/
Licenses: GPL 3+
Build system: r
Synopsis: Meta Clustering with Similarity Network Fusion
Description:

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

r-mcunit 0.3.2
Propagated dependencies: r-testthat@3.3.0 r-simctest@2.6.1 r-rlang@1.1.6 r-rdpack@2.6.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://bitbucket.org/agandy/mcunit/
Licenses: GPL 3
Build system: r
Synopsis: Unit Tests for MC Methods
Description:

Unit testing for Monte Carlo methods, particularly Markov Chain Monte Carlo (MCMC) methods, are implemented as extensions of the testthat package. The MCMC methods check whether the MCMC chain has the correct invariant distribution. They do not check other properties of successful samplers such as whether the chain can reach all points, i.e. whether is recurrent. The tests require the ability to sample from the prior and to run steps of the MCMC chain. The methodology is described in Gandy and Scott (2020) <arXiv:2001.06465>.

r-mangrove 1.21
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=Mangrove
Licenses: GPL 2+
Build system: r
Synopsis: Risk Prediction on Trees
Description:

This package provides methods for performing genetic risk prediction from genotype data. You can use it to perform risk prediction for individuals, or for families with missing data.

r-mabacr 0.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/slabaverse/mabacR
Licenses: GPL 2+
Build system: r
Synopsis: Assisting Decision Makers
Description:

Easy implementation of the MABAC multi-criteria decision method, that was introduced by PamuÄ ar and Ä iroviÄ in the work entitled: "The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC)" - <doi:10.1016/j.eswa.2014.11.057> - which aimed to choose implements for logistics centers. This package receives data, preferably in a spreadsheet, reads it and applies the mathematical algorithms inherent to the MABAC method to generate a ranking with the optimal solution according to the established criteria, weights and type of criteria. The data will be normalized, weighted by the weights, the border area will be determined, the distances to this border area will be calculated and finally a ranking with the optimal option will be generated.

r-medscan 1.0.2
Propagated dependencies: r-qvalue@2.42.0 r-qqman@0.1.9 r-locfdr@1.1-8 r-hdmt@1.0.5 r-fdrtool@1.2.18
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/umich-cphds/medScan
Licenses: GPL 3
Build system: r
Synopsis: Large Scale Single Mediator Hypothesis Testing
Description:

This package provides a collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) <doi:10.1002/gepi.22510>.

r-msentropy 0.1.4
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/YuanyueLi/MSEntropy
Licenses: ASL 2.0
Build system: r
Synopsis: Spectral Entropy for Mass Spectrometry Data
Description:

Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy similarity, and entropy similarity for mass spectrometry data. The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" <doi:10.1038/s41592-021-01331-z>.

r-matching 4.10-15
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/JasjeetSekhon/Matching
Licenses: GPL 3
Build system: r
Synopsis: Multivariate and Propensity Score Matching with Balance Optimization
Description:

This package provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, <doi:10.18637/jss.v042.i07>).

r-multinet 4.3.2
Propagated dependencies: r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 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=multinet
Licenses: ASL 2.0
Build system: r
Synopsis: Analysis and Mining of Multilayer Social Networks
Description:

This package provides functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.

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