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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-mcsimmod 1.2
Propagated dependencies: r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://CRAN.R-project.org/package=MCSimMod
Licenses: GPL 3
Build system: r
Synopsis: Working with 'MCSim' Models
Description:

This package provides tools that facilitate ordinary differential equation (ODE) modeling in R'. This package allows one to perform simulations for ODE models that are encoded in the GNU MCSim model specification language (Bois, 2009) <doi:10.1093/bioinformatics/btp162> using ODE solvers from the R package deSolve (Soetaert et al., 2010) <doi:10.18637/jss.v033.i09>.

r-mashr 0.2.79
Propagated dependencies: r-softimpute@1.4-3 r-rmeta@3.0 r-rcppgsl@0.3.13 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9 r-mvtnorm@1.3-3 r-assertthat@0.2.1 r-ashr@2.2-63 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/stephenslab/mashr
Licenses: Modified BSD
Build system: r
Synopsis: Multivariate Adaptive Shrinkage
Description:

This package implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.

r-mgdrive 1.6.2
Propagated dependencies: r-rdpack@2.6.4 r-rcpp@1.1.0 r-r6@2.6.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://marshalllab.github.io/MGDrivE/
Licenses: GPL 3
Build system: r
Synopsis: Mosquito Gene Drive Explorer
Description:

This package provides a model designed to be a reliable testbed where various gene drive interventions for mosquito-borne diseases control. It is being developed to accommodate the use of various mosquito-specific gene drive systems within a population dynamics framework that allows migration of individuals between patches in landscape. Previous work developing the population dynamics can be found in Deredec et al. (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) <doi:10.1038/s41598-017-02744-7>.

r-ml2pvae 1.0.0.1
Propagated dependencies: r-tfprobability@0.15.2 r-tensorflow@2.20.0 r-reticulate@1.44.1 r-keras@2.16.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://converseg.github.io
Licenses: Expat
Build system: r
Synopsis: Variational Autoencoder Models for IRT Parameter Estimation
Description:

Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of tensorflow or keras is required.

r-multideploy 0.1.0
Propagated dependencies: r-gh@1.5.0 r-cli@3.6.5 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://r-pkg.thecoatlessprofessor.com/multideploy/
Licenses: AGPL 3+
Build system: r
Synopsis: Deploy File Changes Across Multiple 'GitHub' Repositories
Description:

Deploy file changes across multiple GitHub repositories using the GitHub Web API <https://docs.github.com/en/rest>. Allows synchronizing common files, Continuous Integration ('CI') workflows, or configurations across many repositories with a single command.

r-markets 1.1.7
Dependencies: tbb@2021.6.0
Propagated dependencies: r-rlang@1.1.6 r-rcppparallel@5.1.11-1 r-rcppgsl@0.3.13 r-rcpp@1.1.0 r-mass@7.3-65 r-formula@1.2-5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/pi-kappa-devel/markets/
Licenses: Expat
Build system: r
Synopsis: Estimation Methods for Markets in Equilibrium and Disequilibrium
Description:

This package provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) <doi:10.18637/jss.v108.i02> for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics.

r-mattransmix 0.1.18
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MatTransMix
Licenses: GPL 2+
Build system: r
Synopsis: Clustering with Matrix Gaussian and Matrix Transformation Mixture Models
Description:

This package provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations. For more information, please check: Xuwen Zhu, Shuchismita Sarkar, and Volodymyr Melnykov (2021), "MatTransMix: an R package for matrix model-based clustering and parsimonious mixture modeling", <doi:10.1007/s00357-021-09401-9>.

r-mlr3summary 0.1.1
Propagated dependencies: r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-future-apply@1.20.0 r-data-table@1.17.8 r-cli@3.6.5 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://cran.r-project.org/package=mlr3summary
Licenses: LGPL 3
Build system: r
Synopsis: Model and Learner Summaries for 'mlr3'
Description:

Concise and interpretable summaries for machine learning models and learners of the mlr3 ecosystem. The package takes inspiration from the summary function for (generalized) linear models but extends it to non-parametric machine learning models, based on generalization performance, model complexity, feature importances and effects, and fairness metrics.

r-mcprogress 0.1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/myles-lewis/mcprogress
Licenses: GPL 3+
Build system: r
Synopsis: Progress Bars and Messages for Parallel Processes
Description:

This package provides tools for monitoring progress during parallel processing. Lightweight package which acts as a wrapper around mclapply() and adds a progress bar to it in RStudio or Linux environments. Simply replace your original call to mclapply() with pmclapply(). A progress bar can also be displayed during parallelisation via the foreach package. Also included are functions to safely print messages (including error messages) from within parallelised code, which can be useful for debugging parallelised R code.

r-mupetflow 0.1.1
Propagated dependencies: r-zoo@1.8-14 r-tidyr@1.3.1 r-shinythemes@1.2.0 r-shiny@1.11.1 r-markdown@2.0 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-dt@0.34.0 r-dplyr@1.1.4 r-biocmanager@1.30.27
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MuPETFlow
Licenses: GPL 3+
Build system: r
Synopsis: Multiple Ploidy Estimation Tool for all Species Compatible with Flow Cytometry
Description:

This package provides a graphical user interface tool to estimate ploidy from DNA cells stained with fluorescent dyes and analyzed by flow cytometry, following the methodology of Gómez-Muñoz and Fischer (2024) <doi:10.1101/2024.01.24.577056>. Features include multiple file uploading and configuration, peak fluorescence intensity detection, histogram visualizations, peak error curation, ploidy and genome size calculations, and easy results export.

r-mixedmem 1.1.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-gtools@3.9.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mixedMem
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Discrete Multivariate Mixed Membership Models
Description:

Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.

r-mvout 1.2
Propagated dependencies: r-robustbase@0.99-6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvout
Licenses: GPL 2+
Build system: r
Synopsis: Robust Multivariate Outlier Detection
Description:

Detection of multivariate outliers using robust estimates of location and scale. The Minimum Covariance Determinant (MCD) estimator is used to calculate robust estimates of the mean vector and covariance matrix. Outliers are determined based on robust Mahalanobis distances using either an unstructured covariance matrix, a principal components structured covariance matrix, or a factor analysis structured covariance matrix. Includes options for specifying the direction of interest for outlier detection for each variable.

r-modacdc 2.0.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-partition@0.2.2 r-ggplot2@4.0.1 r-genio@1.1.2 r-genieclust@1.2.0 r-foreach@1.5.2 r-doparallel@1.0.17 r-data-table@1.17.8 r-ccp@1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/USCbiostats/ACDC
Licenses: Expat
Build system: r
Synopsis: Association of Covariance for Detecting Differential Co-Expression
Description:

This package provides a series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) <doi:10.3389/fmed.2023.1118824>.

r-mixghd 2.3.7
Propagated dependencies: r-numderiv@2016.8-1.1 r-mvtnorm@1.3-3 r-mixture@2.2.0 r-mass@7.3-65 r-ghyp@1.6.5 r-e1071@1.7-16 r-cluster@2.1.8.1 r-bessel@0.7-0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MixGHD
Licenses: GPL 2+
Build system: r
Synopsis: Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions
Description:

Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model MGHD (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The MGHFA (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The MSGHD is the mixture of multiple scaled generalized hyperbolic distributions, the cMSGHD is a MSGHD with convex contour plots and the MCGHD', mixture of coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.

r-modelstudio 3.1.2
Propagated dependencies: r-r2d3@0.2.6 r-progress@1.2.3 r-jsonlite@2.0.0 r-ingredients@2.3.0 r-ibreakdown@2.1.2 r-digest@0.6.39 r-dalex@2.5.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://modelstudio.drwhy.ai
Licenses: GPL 3
Build system: r
Synopsis: Interactive Studio for Explanatory Model Analysis
Description:

Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. modelStudio facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) <doi:10.1007/s10618-023-00924-w>.

r-mpspline2 0.1.9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/obrl-soil/mpspline2
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Mass-Preserving Spline Functions for Soil Data
Description:

This package provides a low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>, which applies a mass-preserving spline to soil attributes. Splining soil data is a safe way to make continuous down-profile estimates of attributes measured over discrete, often discontinuous depth intervals.

r-mseapca 2.2.1
Propagated dependencies: r-xml@3.99-0.20 r-loadings@0.5.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mseapca
Licenses: LGPL 3
Build system: r
Synopsis: Metabolite Set Enrichment Analysis for Loadings
Description:

Computing metabolite set enrichment analysis (MSEA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), single sample enrichment analysis (SSEA) (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>) and over-representation analysis (ORA) that accounts for undetected metabolites (Yamamoto, H. (2024) <doi:10.51094/jxiv.954>).

r-metanet 0.2.7
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-pcutils@0.2.8 r-magrittr@2.0.4 r-igraph@2.2.1 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/Asa12138/MetaNet
Licenses: GPL 3
Build system: r
Synopsis: Network Analysis for Omics Data
Description:

Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', Cytoscape', ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.

r-michelrodange 1.0.0
Propagated dependencies: r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/b-rodrigues/michelRodange
Licenses: CC0
Build system: r
Synopsis: The Works (in Luxembourguish) of Michel Rodange
Description:

Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuà am Frack an a Ma'nsgrëà t"), but he also wrote many more works, including Rodange, Tockert (1928) <https://www.autorenlexikon.lu/page/document/361/3614/1/FRE/index.html> ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) <https://www.autorenlexikon.lu/page/document/361/3615/1/FRE/index.html> ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the plain text versions of his works available on <https://data.public.lu/fr/datasets/the-works-in-luxembourguish-of-michel-rodange/>.

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-mxmmod 1.1.0
Propagated dependencies: r-openmx@2.22.10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mxmmod
Licenses: ASL 2.0
Build system: r
Synopsis: Measurement Model of Derivatives in 'OpenMx'
Description:

This package provides a convenient interface in OpenMx for building Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of Derivatives (MMOD).

r-mlr3resampling 2025.11.19
Propagated dependencies: r-r6@2.6.1 r-pbdmpi@0.5-4 r-paradox@1.0.1 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-data-table@1.17.8 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/tdhock/mlr3resampling
Licenses: LGPL 3
Build system: r
Synopsis: Resampling Algorithms for 'mlr3' Framework
Description:

This package provides a supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, <doi:10.48550/arXiv.2410.08643> can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set.

r-mutationtypes 0.0.1
Propagated dependencies: r-data-table@1.17.8 r-cli@3.6.5 r-assertions@0.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/selkamand/mutationtypes
Licenses: LGPL 3+
Build system: r
Synopsis: Validate and Convert Mutational Impacts Using Standard Genomic Dictionaries
Description:

Check concordance of a vector of mutation impacts with standard dictionaries such as Sequence Ontology (SO) <http://www.sequenceontology.org/>, Mutation Annotation Format (MAF) <https://docs.gdc.cancer.gov/Encyclopedia/pages/Mutation_Annotation_Format_TCGAv2/> or Prediction and Annotation of Variant Effects (PAVE) <https://github.com/hartwigmedical/hmftools/tree/master/pave>. It enables conversion between SO/PAVE and MAF terms and selection of the most severe consequence where multiple ampersand (&) delimited impacts are given.

r-matrixdist 1.1.9
Propagated dependencies: r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-nnet@7.3-20
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/martinbladt/matrixdist_1.0
Licenses: GPL 3
Build system: r
Synopsis: Statistics for Matrix Distributions
Description:

This package provides tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>, Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>, Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>, Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

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