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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-mongolstats 0.1.1
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-stringi@1.8.7 r-stringdist@0.9.15 r-sf@1.0-23 r-rappdirs@0.3.3 r-purrr@1.2.0 r-memoise@2.0.1 r-jsonlite@2.0.0 r-httr2@1.2.1 r-dplyr@1.1.4 r-curl@7.0.0 r-cachem@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://temuulene.github.io/mongolstats/
Licenses: Expat
Build system: r
Synopsis: Mongolian 'NSO' 'PXWeb' Data and Boundaries (Tidy Client)
Description:

This package provides a tidyverse'-friendly client for the National Statistics Office of Mongolia PXWeb API <https://data.1212.mn/> with helpers to discover tables, variables, and fetch statistical data. Also includes utilities to retrieve Mongolia administrative boundaries (ADM0-ADM2) as sf objects from open sources for mapping and spatial analysis.

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-minimax 1.1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=minimax
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: The Minimax Distribution Family
Description:

The minimax family of distributions is a two-parameter family like the beta family, but computationally a lot more tractible.

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-mtanan 0.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mtanan
Licenses: GPL 3
Build system: r
Synopsis: Single Valued Neutrosophic Kruskal-Wallis and Mann Whitney Tests
Description:

Dealing with neutrosophic data in single valued form using score, accuracy and certainty functions to calculate ranks of Single Valued Neutrosophic Set (SVNS), also to calculate the Mann-Whitney test, and making a post-hoc test after rejecting the null hypothesis using the Neutrosophic Statistics Kruskal-Wallis test. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://digitalrepository.unm.edu/nss_journal/vol51/iss1/60/>.

r-mlmoi 0.1.2
Propagated dependencies: r-rmpfr@1.1-2 r-rdpack@2.6.4 r-openxlsx@4.2.8.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MLMOI
Licenses: GPL 3
Build system: r
Synopsis: Estimating Frequencies, Prevalence and Multiplicity of Infection
Description:

The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.

r-mandalar 0.1.0
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://lucianealcoforado.shinyapps.io/Mandala/
Licenses: GPL 3
Build system: r
Synopsis: Building Mandalas from Parametric Equations of Classical Curves
Description:

This package provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal.

r-mmb 0.13.3
Propagated dependencies: r-rdpack@2.6.4 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/MrShoenel/R-mmb
Licenses: GPL 3
Build system: r
Synopsis: Arbitrary Dependency Mixed Multivariate Bayesian Models
Description:

Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053> and by Scutari (2010) <doi:10.18637/jss.v035.i03>.

r-markowitz 0.1.0
Propagated dependencies: r-tidyverse@2.0.0 r-tidyr@1.3.1 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/luana1909/Markowitiz
Licenses: GPL 3
Build system: r
Synopsis: Markowitz Criterion
Description:

The Markowitz criterion is a multicriteria decision-making method that stands out in risk and uncertainty analysis in contexts where probabilities are known. This approach represents an evolution of Pascal's criterion by incorporating the dimension of variability. In this framework, the expected value reflects the anticipated return, while the standard deviation serves as a measure of risk. The markowitz package provides a practical and accessible tool for implementing this method, enabling researchers and professionals to perform analyses without complex calculations. Thus, the package facilitates the application of the Markowitz criterion. More details on the method can be found in Octave Jokung-Nguéna (2001, ISBN 2100055372).

r-motifr 1.0.0
Dependencies: python@3.11.14 python-pandas@2.2.3 python-numpy@1.26.4
Propagated dependencies: r-tidygraph@1.3.1 r-tibble@3.3.0 r-scales@1.4.0 r-rlang@1.1.6 r-reticulate@1.44.1 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-network@1.19.0 r-intergraph@2.0-4 r-igraph@2.2.1 r-ggraph@2.2.2 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://marioangst.github.io/motifr/
Licenses: Expat
Build system: r
Synopsis: Motif Analysis in Multi-Level Networks
Description:

This package provides tools for motif analysis in multi-level networks. Multi-level networks combine multiple networks in one, e.g. social-ecological networks. Motifs are small configurations of nodes and edges (subgraphs) occurring in networks. motifr can visualize multi-level networks, count multi-level network motifs and compare motif occurrences to baseline models. It also identifies contributions of existing or potential edges to motifs to find critical or missing edges. The package is in many parts an R wrapper for the excellent SESMotifAnalyser Python package written by Tim Seppelt.

r-multidiscreterng 0.1.0
Propagated dependencies: r-mvtnorm@1.3-3 r-multiord@2.4.4 r-matrixcalc@1.0-6 r-matrix@1.7-4 r-genord@2.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ckchengtommy/MultiDiscreteRNG
Licenses: GPL 3
Build system: r
Synopsis: Generate Multivariate Discrete Data
Description:

Generate multivariate discrete data with generalized Poisson, negative binomial and binomial marginal distributions using user-specified distribution parameters and a target correlation matrix. The method is described in Cheng and Demirtas (2026) <doi:10.48550/arXiv.2602.07707>.

r-multiplencc 1.2-5
Propagated dependencies: r-survival@3.8-3 r-mgcv@1.9-4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multipleNCC
Licenses: GPL 2
Build system: r
Synopsis: Weighted Cox-Regression for Nested Case-Control Data
Description:

Fit Cox proportional hazard models with a weighted partial likelihood. It handles one or multiple endpoints, additional matching and makes it possible to reuse controls for other endpoints Stoer NC and Samuelsen SO (2016) <doi:10.32614/rj-2016-030>.

r-molar 6.0
Propagated dependencies: r-rvcg@0.25 r-pracma@2.4.6 r-htmltools@0.5.8.1 r-alphahull@2.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=molaR
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Dental Surface Complexity Measurement Tools
Description:

Surface topography calculations of Dirichlet's normal energy, relief index, surface slope, and orientation patch count for teeth using scans of enamel caps. Importantly, for the relief index and orientation patch count calculations to work, the scanned tooth files must be oriented with the occlusal plane parallel to the x and y axes, and perpendicular to the z axis. The files should also be simplified, and smoothed in some other software prior to uploading into R.

r-mhctools 1.6.0
Propagated dependencies: r-openxlsx@4.2.8.1 r-mgcv@1.9-4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MHCtools
Licenses: Expat
Build system: r
Synopsis: Analysis of MHC Data in Non-Model Species
Description:

Sixteen tools for bioinformatics processing and analysis of major histocompatibility complex (MHC) data. The functions are tailored for amplicon data sets that have been filtered using the dada2 method (for more information on dada2, visit <https://benjjneb.github.io/dada2/> ), but even other types of data sets can be analyzed. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates individual fasta files for each sample in the data set. The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in a data set, and mean distances of all pairwise comparisons within each sample in a data set. The function additionally outputs five tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for each amino acid position in all sequences in the data set. These tables may be useful for further downstream analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper for the kmeans() function of the stats package, which allows for bootstrapping. Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values do not produce clear inflection points ("elbows"). BootKmeans() performs multiple runs of kmeans() and estimates optimal k-values based on a user-defined threshold of BIC reduction. The method is an automated and bootstrapped version of visually inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool for evaluating whether different k-means() clustering models identify similar clusters, and summarize bootstrap model stats as means for different estimated values of k. It is designed to take files produced by the BootKmeans() function as input, but other data can be analyzed if the descriptions of the required data formats are observed carefully. The SynDist() function analyses of synonymous variation among aligned protein-coding DNA sequences, that is, nucleotide substitutions that do not translate to changes in the amino acid sequences due to degeneracy of the genetic code. The SynDist() function calculates synonymous nucleotide changes per base and per codon in pairwise sequence comparisons, as well as mean synonymous variation among all pairwise comparisons of the sequences within each sample in a data set. The PapaDiv() function compares parent pairs in the data set and calculate their joint MHC diversity, taking into account sequence variants that occur in both parents. The HpltFind() function infers putative haplotypes from families in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of the haplotype inference. The CreateHpltOccTable() function creates a binary (logical) haplotype-sequence occurrence matrix from the output of HpltFind(), for easy overview of which sequences are present in which haplotypes. The HpltMatch() function compares haplotypes to help identify overlapping and potentially identical types. The NestTablesXL() function translates the output from HpltFind() to an Excel workbook, that provides a convenient overview for evaluation and curating of the inferred putative haplotypes.

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-multifear 0.1.5
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-purrr@1.2.0 r-plyr@1.8.9 r-nlme@3.1-168 r-maditr@0.8.7 r-ggplot2@4.0.1 r-forestplot@3.1.7 r-fastdummies@1.7.5 r-esc@0.5.1 r-effsize@0.8.1 r-effectsize@1.0.1 r-dplyr@1.1.4 r-car@3.1-3 r-broom@1.0.10 r-bootstrap@2019.6 r-bayestestr@0.17.0 r-bayesfactor@0.9.12-4.7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/AngelosPsy/multifear
Licenses: GPL 3
Build system: r
Synopsis: Multiverse Analyses for Conditioning Data
Description:

This package provides a suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos (2022) <doi:10.1016/j.brat.2022.104072>. Since November 2025, this package contains code from the ez R package (Copyright (c) 2016-11-01, Michael A. Lawrence <mike.lwrnc@gmail.com>), originally distributed under the GPL (equal and above 2) license.

r-mnt 1.3
Propagated dependencies: r-pracma@2.4.6 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=mnt
Licenses: FSDG-compatible
Build system: r
Synopsis: Affine Invariant Tests of Multivariate Normality
Description:

Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) <arXiv:2004.07332> titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the energy test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns).

r-marssvrhybrid 0.1.0
Propagated dependencies: r-earth@5.3.4 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=MARSSVRhybrid
Licenses: GPL 3
Build system: r
Synopsis: MARS SVR Hybrid
Description:

Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.

r-madsim 1.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=madsim
Licenses: GPL 2+
Build system: r
Synopsis: Flexible Microarray Data Simulation Model
Description:

This function allows to generate two biological conditions synthetic microarray dataset which has similar behavior to those currently observed with common platforms. User provides a subset of parameters. Available default parameters settings can be modified.

r-mdimnormn 0.8.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MDimNormn
Licenses: GPL 3
Build system: r
Synopsis: Multi-Dimensional MA Normalization for Plate Effect
Description:

Normalize data to minimize the difference between sample plates (batch effects). For given data in a matrix and grouping variable (or plate), the function normn_MA normalizes the data on MA coordinates. More details are in the citation. The primary method is Multi-MA'. Other fitting functions on MA coordinates can also be employed e.g. loess.

r-mfx 1.2-4
Propagated dependencies: r-sandwich@3.1-1 r-mass@7.3-65 r-lmtest@0.9-40 r-betareg@3.2-4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mfx
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
Description:

Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic.

r-maxaltall 0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-magrittr@2.0.4 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=maxaltall
Licenses: GPL 3+
Build system: r
Synopsis: 'FASTA' ML and ‘altall’ Sequences from IQ-TREE .state Files
Description:

Takes a .state file generated by IQ-TREE as an input and, for each ancestral node present in the file, generates a FASTA-formatted maximum likelihood (ML) sequence as well as an âAltAllâ sequence in which uncertain sites, determined by the two parameters thres_1 and thres_2, have the maximum likelihood state swapped with the next most likely state as described in Geeta N. Eick, Jamie T. Bridgham, Douglas P. Anderson, Michael J. Harms, and Joseph W. Thornton (2017), "Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty" <doi:10.1093/molbev/msw223>.

r-mcmcensemble 3.2.0
Propagated dependencies: r-progressr@0.18.0 r-future-apply@1.20.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://hugogruson.fr/mcmcensemble/
Licenses: GPL 2
Build system: r
Synopsis: Ensemble Sampler for Affine-Invariant MCMC
Description:

This package provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the differential.evolution sampler from ter Braak and Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the stretch sampler from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.

r-mkpower 1.1
Propagated dependencies: r-rlang@1.1.6 r-qqplotr@0.0.7 r-mvtnorm@1.3-3 r-mkinfer@1.3 r-mkdescr@0.9 r-matrixtests@0.2.3.1 r-ggplot2@4.0.1 r-coin@1.4-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/stamats/MKpower
Licenses: LGPL 3
Build system: r
Synopsis: Power Analysis and Sample Size Calculation
Description:

Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>), comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>), ANCOVA (Shieh (2020), <doi:10.1007/s11336-019-09692-3>), reference ranges (Jennen-Steinmetz and Wellek (2005), <doi:10.1002/sim.2177>), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), <doi:10.1148/radiology.143.1.7063747>).

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Total results: 21457