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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-mixrf 1.0
Propagated dependencies: r-randomforest@4.7-1.2 r-lme4@1.1-37 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/randel/MixRF
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Random-Forest-Based Approach for Imputing Clustered Incomplete Data
Description:

It offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues.

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-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-misuvi 0.1.1
Propagated dependencies: r-tigris@2.2.1 r-sf@1.0-23 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/brendensm/misuvi
Licenses: CC0
Build system: r
Synopsis: Access the Michigan Substance Use Vulnerability Index (MI-SUVI)
Description:

Easily import the MI-SUVI data sets. The user can import data sets with full metrics, percentiles, Z-scores, or rankings. Data is available at both the County and Zip Code Tabulation Area (ZCTA) levels. This package also includes a function to import shape files for easy mapping and a function to access the full technical documentation. All data is sourced from the Michigan Department of Health and Human Services.

r-mixedcca 1.6.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pcapp@2.0-5 r-mnormt@2.1.1 r-matrix@1.7-4 r-mass@7.3-65 r-latentcor@2.0.2 r-irlba@2.3.5.1 r-fmultivar@4031.84
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mixedCCA
Licenses: GPL 3
Build system: r
Synopsis: Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data
Description:

Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

r-microsoft365r 2.4.1
Propagated dependencies: r-vctrs@0.6.5 r-r6@2.6.1 r-mime@0.13 r-jsonlite@2.0.0 r-httr@1.4.7 r-curl@7.0.0 r-azuregraph@1.3.5 r-azureauth@1.3.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=Microsoft365R
Licenses: Expat
Build system: r
Synopsis: Interface to the 'Microsoft 365' Suite of Cloud Services
Description:

An interface to the Microsoft 365 (formerly known as Office 365') suite of cloud services, building on the framework supplied by the AzureGraph package. Enables access from R to data stored in Teams', SharePoint Online and OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured Outlook email client, with the ability to send emails and manage emails and mail folders.

r-mave 1.3.12
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mda@0.5-5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MAVE
Licenses: GPL 2+
Build system: r
Synopsis: Methods for Dimension Reduction
Description:

This package provides functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). Methods for selecting the best dimension are also included. Xia (2002) <doi:10.1111/1467-9868.03411>; Xia (2007) <doi:10.1214/009053607000000352>; Wang (2008) <doi:10.1198/016214508000000418>.

r-mantaid 1.0.4
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-scutr@0.2.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-paradox@1.0.1 r-mlr3tuning@1.5.0 r-mlr3@1.2.0 r-magrittr@2.0.4 r-keras@2.16.1 r-ggplot2@4.0.1 r-ggcorrplot@0.1.4.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-caret@7.0-1 r-biomart@2.66.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://molaison.github.io/MantaID/
Licenses: GPL 3+
Build system: r
Synopsis: Machine-Learning Based Tool to Automate the Identification of Biological Database IDs
Description:

The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The MantaID model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. MantaID supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for MantaID to improve applicability. To our knowledge, MantaID is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.

r-morphoregions 0.1.0
Propagated dependencies: r-scales@1.4.0 r-rcolorbrewer@1.1-3 r-pbapply@1.7-4 r-ggplot2@4.0.1 r-cluster@2.1.8.1 r-chk@0.10.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://aagillet.github.io/MorphoRegions/
Licenses: GPL 2+
Build system: r
Synopsis: Analysis of Regionalization Patterns in Serially Homologous Structures
Description:

Computes the optimal number of regions (or subdivisions) and their position in serial structures without a priori assumptions and to visualize the results. After reducing data dimensionality with the built-in function for data ordination, regions are fitted as segmented linear regressions along the serial structure. Every region boundary position and increasing number of regions are iteratively fitted and the best model (number of regions and boundary positions) is selected with an information criterion. This package expands on the previous regions package (Jones et al., Science 2018) with improved computation and more fitting and plotting options.

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-mergedblocks 1.1.1
Propagated dependencies: r-randomizer@3.0.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mergedblocks
Licenses: GPL 3
Build system: r
Synopsis: Merged Block Randomization
Description:

Package to carry out merged block randomization (Van der Pas (2019), <doi:10.1177/1740774519827957>), a restricted randomization method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicentre trials. It can be used for more than two groups or unequal randomization ratios.

r-munfold 0.3.5
Propagated dependencies: r-memisc@0.99.31.8.3 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://www.elff.eu/software/munfold/
Licenses: GPL 2
Build system: r
Synopsis: Metric Unfolding
Description:

Multidimensional unfolding using Schoenemann's algorithm for metric and Procrustes rotation of unfolding results.

r-maskr 0.1.0
Propagated dependencies: r-vctrs@0.6.5 r-rlang@1.1.6 r-pillar@1.11.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/inpowell/maskr
Licenses: Expat
Build system: r
Synopsis: Visual Class for Vectors with Non-Publishing Requirements
Description:

Create vectors with sticky flags for elements that should not be displayed. Numeric vectors have basic subset and arithmetic methods implemented.

r-migest 2.0.5
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-purrr@1.2.0 r-mipfp@3.2.1 r-migration-indices@0.3.1 r-matrixstats@1.5.0 r-magrittr@2.0.4 r-lpsolve@5.6.23 r-forcats@1.0.1 r-dplyr@1.1.4 r-cvxr@1.0-15 r-circlize@0.4.16
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://guyabel.github.io/migest/
Licenses: GPL 3
Build system: r
Synopsis: Tools for Estimating, Measuring and Working with Migration Data
Description:

This package provides tools for estimating, measuring, and analyzing migration data. Designed to assist researchers and analysts in working effectively with migration data.

r-mapmisc 2.1.3
Propagated dependencies: r-terra@1.8-86 r-geosphere@1.5-20
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mapmisc
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Utilities for Producing Maps
Description:

This package provides a minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections. See Brown (2016) <doi:10.32614/RJ-2016-005>.

r-metapost 1.0-6
Propagated dependencies: r-gridbezier@1.1-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/pmur002/metapost
Licenses: GPL 2+
Build system: r
Synopsis: Interface to 'MetaPost'
Description:

This package provides an interface to MetaPost (Hobby, 1998) <http://www.tug.org/docs/metapost/mpman.pdf>. There are functions to generate an R description of a MetaPost curve, functions to generate MetaPost code from an R description, functions to process MetaPost code, and functions to read solved MetaPost paths back into R.

r-metalite-sl 0.1.1
Propagated dependencies: r-uuid@1.2-1 r-stringr@1.6.0 r-rlang@1.1.6 r-reactable@0.4.5 r-r2rtf@1.3.0 r-plotly@4.11.0 r-metalite-ae@0.1.3 r-metalite@0.1.4 r-htmltools@0.5.8.1 r-glue@1.8.0 r-brew@1.0-10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=metalite.sl
Licenses: GPL 3+
Build system: r
Synopsis: Subject-Level Analysis Using 'metalite'
Description:

Analyzes subject-level data in clinical trials using the metalite data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the subject-level analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

r-mytai 2.3.5
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-s7@0.2.1 r-readr@2.1.6 r-rcppthread@2.2.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-pheatmap@1.0.13 r-patchwork@1.3.2 r-memoise@2.0.1 r-matrix@1.7-4 r-ggtext@0.1.2 r-ggridges@0.5.7 r-ggrepel@0.9.6 r-ggplotify@0.1.3 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-fitdistrplus@1.2-4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://drostlab.github.io/myTAI/
Licenses: GPL 2
Build system: r
Synopsis: Evolutionary Transcriptomics
Description:

Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2018) <doi:10.1093/bioinformatics/btx835>). This package aims to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest.

r-mixpoissonreg 1.0.0
Propagated dependencies: r-tibble@3.3.0 r-statmod@1.5.1 r-rlang@1.1.6 r-rfast@2.1.5.2 r-pbapply@1.7-4 r-magrittr@2.0.4 r-lmtest@0.9-40 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-generics@0.1.4 r-gamlss-dist@6.1-1 r-gamlss@5.5-0 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/vpnsctl/mixpoissonreg/
Licenses: GPL 2
Build system: r
Synopsis: Mixed Poisson Regression for Overdispersed Count Data
Description:

Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.

r-mmirestriktor 0.3.1
Propagated dependencies: r-shinythemes@1.2.0 r-shiny@1.11.1 r-rpostgres@1.4.8 r-restriktor@0.6-30 r-pool@1.0.4 r-mmcards@0.1.1 r-mass@7.3-65 r-dt@0.34.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mightymetrika/mmirestriktor
Licenses: Expat
Build system: r
Synopsis: Informative Hypothesis Testing Web Applications
Description:

Offering enhanced statistical power compared to traditional hypothesis testing methods, informative hypothesis testing allows researchers to explicitly model their expectations regarding the relationships among parameters. An important software tool for this framework is restriktor'. The mmirestriktor package provides shiny web applications to implement some of the basic functionality of restriktor'. The mmirestriktor() function launches a shiny application for fitting and analyzing models with constraints. The FbarCards() function launches a card game application which can help build intuition about informative hypothesis testing. The iht_interpreter() helps interpret informative hypothesis testing results based on guidelines in Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>.

r-mgdrive2 2.1.1
Propagated dependencies: r-statmod@1.5.1 r-matrix@1.7-4 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://marshalllab.github.io/MGDrivE/docs_v2/index.html
Licenses: GPL 3
Build system: r
Synopsis: Mosquito Gene Drive Explorer 2
Description:

This package provides a simulation modeling framework which significantly extends capabilities from the MGDrivE simulation package via a new mathematical and computational framework based on stochastic Petri nets. For more information about MGDrivE', see our publication: Sánchez et al. (2019) <doi:10.1111/2041-210X.13318> Some of the notable capabilities of MGDrivE2 include: incorporation of human populations, epidemiological dynamics, time-varying parameters, and a continuous-time simulation framework with various sampling algorithms for both deterministic and stochastic interpretations. MGDrivE2 relies on the genetic inheritance structures provided in package MGDrivE', so we suggest installing that package initially.

r-msbox 1.4.8
Propagated dependencies: r-xml2@1.5.0 r-stringr@1.6.0 r-crayon@1.5.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/YonghuiDong/MSbox
Licenses: GPL 2
Build system: r
Synopsis: Mass Spectrometry Tools
Description:

Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks, examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) ion sources.

r-metaquant 0.1.3
Propagated dependencies: r-sld@1.0.1 r-plotly@4.11.0 r-metafor@4.8-0 r-magrittr@2.0.4 r-gld@2.6.8 r-ggplot2@4.0.1 r-estmeansd@1.0.1 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=metaquant
Licenses: GPL 3
Build system: r
Synopsis: Meta-Analysis of Quantiles and Functions of Quantiles
Description:

This package implements a novel density-based approach for estimating unknown parameters, distribution visualisations and meta-analyses of quantiles and ther functions. A detailed vignettes with example datasets and code to prepare data and analyses is available at <https://bookdown.org/a2delivera/metaquant/>. The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, <doi:10.48550/arXiv.2411.10971>).

r-mofat 1.0
Propagated dependencies: r-slhd@2.1-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MOFAT
Licenses: GPL 2+
Build system: r
Synopsis: Maximum One-Factor-at-a-Time Designs
Description:

Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Xiao, Joseph, and Ray (2022) <doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT design can be viewed as an improvement to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991) <doi:10.1080/00401706.1991.10484804>. The improvement is achieved by exploiting the connection between Morris screening designs and Monte Carlo-based Sobol designs, and optimizing the design using a space-filling criterion. This work is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.

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