_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-markdowninput 0.1.2
Propagated dependencies: r-shinyace@0.4.4 r-shiny@1.11.1 r-markdown@2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/juliendiot42/markdownInput
Licenses: GPL 3
Build system: r
Synopsis: Shiny Module for a Markdown Input with Result Preview
Description:

An R-Shiny module containing a "markdownInput". This input allows the user to write some markdown code and to preview the result. This input has been inspired by the "comment" window of <https://github.com/>.

r-mlmhelpr 0.1.1
Propagated dependencies: r-rdpack@2.6.4 r-mathjaxr@1.8-0 r-lme4@1.1-37
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/lrocconi/mlmhelpr
Licenses: Expat
Build system: r
Synopsis: Multilevel/Mixed Model Helper Functions
Description:

This package provides a collection of miscellaneous helper function for running multilevel/mixed models in lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015).

r-mulvariaterandomforestvarimp 0.0.2
Propagated dependencies: r-multivariaterandomforest@1.1.5 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/Megatvini/VIM/
Licenses: GPL 3+
Build system: r
Synopsis: Variable Importance Measures for Multivariate Random Forests
Description:

Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.

r-mcmiso 0.1.2
Propagated dependencies: r-future@1.68.0 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=McMiso
Licenses: GPL 3
Build system: r
Synopsis: Multicore Multivariable Isotonic Regression
Description:

The goal of McMiso is to provide functions for isotonic regression when there are multiple independent variables. The functions solve the optimization problem using recursion and leverage parallel computing to improve speed, and are useful for situations with relatively large number of covariates. The estimation method follows the projective Bayes solution described in Cheung and Diaz (2023) <doi:10.1093/jrsssb/qkad014>.

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-mverse 0.2.3
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-rdpack@2.6.4 r-multiverse@0.6.2 r-magrittr@2.0.4 r-igraph@2.2.1 r-ggupset@0.4.1 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mverseanalysis/mverse/
Licenses: GPL 3+
Build system: r
Synopsis: Tidy Multiverse Analysis Made Simple
Description:

Extends multiverse package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>, which allows users perform to create explorable multiverse analysis in R. This extension provides an additional level of abstraction to the multiverse package with the aim of creating user friendly syntax to researchers, educators, and students in statistics. The mverse syntax is designed to allow piping and takes hints from the tidyverse grammar. The package allows users to define and inspect multiverse analysis using familiar syntax in R.

r-mammalcol 0.2.9
Propagated dependencies: r-sf@1.0-23 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-geodata@0.6-6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/dlizcano/mammalcol
Licenses: Expat
Build system: r
Synopsis: Access to the List of Mammal Species of Colombia
Description:

The goal of mammalcol is to provide easy access to a meticulously structured dataset of Colombian mammal species in R. The 2025 update includes comprehensive, detailed species accounts, and distribution information.

r-metrica 2.1.0
Propagated dependencies: r-tidyr@1.3.1 r-rsqlite@2.4.4 r-rlang@1.1.6 r-minerva@1.5.10 r-ggpp@0.5.9 r-ggplot2@4.0.1 r-energy@1.7-12 r-dplyr@1.1.4 r-dbi@1.2.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://adriancorrendo.github.io/metrica/
Licenses: Expat
Build system: r
Synopsis: Prediction Performance Metrics
Description:

This package provides a compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.

r-mcount 1.0.0
Propagated dependencies: r-bbmle@1.0.25.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mcount
Licenses: GPL 3
Build system: r
Synopsis: Marginalized Count Regression Models
Description:

Implementation of marginalized models for zero-inflated count data. This package provides a tool to implement an estimation algorithm for the marginalized count models, which directly makes inference on the effect of each covariate on the marginal mean of the outcome. The method involves the marginalized zero-inflated Poisson model described in Long et al. (2014) <doi:10.1002/sim.6293>.

r-metaplot 0.8.4
Propagated dependencies: r-tidyr@1.3.1 r-scales@1.4.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-lattice@0.22-7 r-gtable@0.3.6 r-gridextra@2.3 r-ggplot2@4.0.1 r-encode@0.3.6 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=metaplot
Licenses: GPL 3
Build system: r
Synopsis: Data-Driven Plot Design
Description:

Designs plots in terms of core structure. See example(metaplot)'. Primary arguments are (unquoted) column names; order and type (numeric or not) dictate the resulting plot. Specify any y variables, x variable, any groups variable, and any conditioning variables to metaplot() to generate density plots, boxplots, mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() to arrange plots in grids. Wherever present, scalar column attributes label and guide are honored, producing fully annotated plots with minimal effort. Attribute guide is typically units, but may be encoded() to provide interpretations of categorical values (see ?encode'). Utility unpack() transforms scalar column attributes to row values and pack() does the reverse, supporting tool-neutral storage of metadata along with primary data. The package supports customizable aesthetics such as such as reference lines, unity lines, smooths, log transformation, and linear fits. The user may choose between trellis and ggplot output. Compact syntax and integrated metadata promote workflow scalability.

r-mixopt 0.1.3
Propagated dependencies: r-splitfngr@0.1.2 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://github.com/CollinErickson/mixopt
Licenses: LGPL 3+
Build system: r
Synopsis: Mixed Variable Optimization
Description:

Mixed variable optimization for non-linear functions. Can optimize function whose inputs are a combination of continuous, ordered, and unordered variables.

r-mycobacrvr 1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://mycobacteriarv.igib.res.in/download.html
Licenses: GPL 2
Build system: r
Synopsis: Integrative Immunoinformatics for Mycobacterial Diseases in R Platform
Description:

The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.

r-modturpoint 0.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=modTurPoint
Licenses: GPL 3
Build system: r
Synopsis: Estimate ED50 Based on Modified Turning Point Method
Description:

Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453> to estimate 50 percent effective dose (ED50) in the study of drug sensitivity. The method has its own advantages for that it can provide robust ED50 estimation. This package contains the modified function of Choi's turning point method.

r-monaco 0.2.2
Propagated dependencies: r-shiny@1.11.1 r-rstudioapi@0.17.1 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/stla/monaco
Licenses: GPL 3
Build system: r
Synopsis: The 'Monaco' Editor as a HTML Widget
Description:

This package provides a HTML widget rendering the Monaco editor. The Monaco editor is the code editor which powers VS Code'. It is particularly well developed for JavaScript'. In addition to the built-in features of the Monaco editor, the widget allows to prettify multiple languages, to view the HTML rendering of Markdown code, and to view and resize SVG images.

r-multilevelcoda 1.3.3
Propagated dependencies: r-shinystan@2.6.0 r-shiny@1.11.1 r-plotly@4.11.0 r-loo@2.8.0 r-htmltools@0.5.8.1 r-ggplot2@4.0.1 r-future@1.68.0 r-fs@1.6.6 r-foreach@1.5.2 r-extraoperators@0.3.0 r-emmeans@2.0.0 r-dt@0.34.0 r-dofuture@1.1.2 r-data-table@1.17.8 r-compositions@2.0-9 r-bslib@0.9.0 r-brms@2.23.0 r-bayesplot@1.14.0 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://florale.github.io/multilevelcoda/
Licenses: GPL 3+
Build system: r
Synopsis: Estimate Bayesian Multilevel Models for Compositional Data
Description:

Implement Bayesian multilevel modelling for compositional data. Compute multilevel compositional data and perform log-ratio transforms at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2025) <doi:10.1037/met0000750>, Le, Dumuid, Stanford, and Wiley (2025) <doi:10.1080/00273171.2025.2565598>.

r-microhaplot 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-shinywidgets@0.9.0 r-shinybs@0.61.1 r-shiny@1.11.1 r-scales@1.4.0 r-magrittr@2.0.4 r-gtools@3.9.5 r-ggplot2@4.0.1 r-ggiraph@0.9.2 r-dt@0.34.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ngthomas/microhaplot
Licenses: GPL 3
Build system: r
Synopsis: Microhaplotype Constructor and Visualizer
Description:

This package provides a downstream bioinformatics tool to construct and assist curation of microhaplotypes from short read sequences.

r-moqa 2.0.0
Propagated dependencies: r-readr@2.1.6 r-psych@2.5.6 r-gplots@3.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MOQA
Licenses: AGPL 3
Build system: r
Synopsis: Basic Quality Data Assurance for Epidemiological Research
Description:

With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The MOQA package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.

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-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-movedesign 0.3.2
Propagated dependencies: r-viridis@0.6.5 r-tidyr@1.3.1 r-terra@1.8-86 r-stringr@1.6.0 r-shinywidgets@0.9.0 r-shinyjs@2.1.0 r-shinyfeedback@0.4.0 r-shinydashboardplus@2.0.6 r-shinydashboard@0.7.3 r-shinybusy@0.3.3 r-shinyalert@3.1.0 r-shiny@1.11.1 r-scales@1.4.0 r-rlang@1.1.6 r-rintrojs@0.3.4 r-reactable@0.4.5 r-quarto@1.5.1 r-parsedate@1.3.2 r-lubridate@1.9.4 r-gsl@2.1-9 r-golem@0.5.1 r-ggtext@0.1.2 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ggiraph@0.9.2 r-gdtools@0.4.4 r-fontawesome@0.5.3 r-dplyr@1.1.4 r-data-table@1.17.8 r-ctmm@1.3.0 r-crayon@1.5.3 r-config@0.3.2 r-combinat@0.0-8 r-bsplus@0.1.5 r-bayestestr@0.17.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://ecoisilva.github.io/movedesign/
Licenses: GPL 3+
Build system: r
Synopsis: Study Design Toolbox for Movement Ecology Studies
Description:

Toolbox and shiny application to help researchers design movement ecology studies, focusing on two key objectives: estimating home range areas, and estimating fine-scale movement behavior, specifically speed and distance traveled. It provides interactive simulations and methodological guidance to support study planning and decision-making. The application is described in Silva et al. (2023) <doi:10.1111/2041-210X.14153>.

r-maxskew 1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MaxSkew
Licenses: GPL 2
Build system: r
Synopsis: Orthogonal Data Projections with Maximal Skewness
Description:

It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.

r-multipol 1.0-9
Propagated dependencies: r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multipol
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Multivariate Polynomials
Description:

Various utilities to manipulate multivariate polynomials. The package is almost completely superceded by the spray and mvp packages, which are much more efficient.

r-multilcirt 2.12
Propagated dependencies: r-mass@7.3-65 r-limsolve@2.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiLCIRT
Licenses: GPL 2+
Build system: r
Synopsis: Multidimensional Latent Class Item Response Theory Models
Description:

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).

r-mbmethpred 0.1.4.4
Propagated dependencies: r-xgboost@1.7.11.1 r-tensorflow@2.20.0 r-stringr@1.6.0 r-snftool@2.3.1 r-rtsne@0.17 r-rgl@1.3.31 r-reticulate@1.44.1 r-reshape2@1.4.5 r-readr@2.1.6 r-randomforest@4.7-1.2 r-proc@1.19.0.1 r-mass@7.3-65 r-keras@2.16.0 r-ggplot2@4.0.1 r-e1071@1.7-16 r-dplyr@1.1.4 r-class@7.3-23 r-catools@1.18.3 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/sharifrahmanie/MBMethPred
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Medulloblastoma Subgroups Prediction
Description:

Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, MBMethPred can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.

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Total results: 68658