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r-multimedia 0.2.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tidygraph@1.3.1 r-summarizedexperiment@1.38.1 r-s4vectors@0.46.0 r-rlang@1.1.6 r-ranger@0.17.0 r-purrr@1.0.4 r-progress@1.2.3 r-phyloseq@1.52.0 r-patchwork@1.3.0 r-minilnm@0.1.0 r-mass@7.3-65 r-glue@1.8.0 r-glmnetutils@1.1.9 r-ggplot2@3.5.2 r-formula-tools@1.7.1 r-fansi@1.0.6 r-dplyr@1.1.4 r-cli@3.6.5 r-brms@2.22.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://krisrs1128.github.io/multimedia/
Licenses: CC0
Synopsis: Multimodal Mediation Analysis
Description:

Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.

r-multistatm 2.0.0
Propagated dependencies: r-mvtnorm@1.3-3 r-matrix@1.7-3 r-mass@7.3-65 r-arrangements@1.1.9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiStatM
Licenses: GPL 3
Synopsis: Multivariate Statistical Methods
Description:

Algorithms to build set partitions and commutator matrices and their use in the construction of multivariate d-Hermite polynomials; estimation and derivation of theoretical vector moments and vector cumulants of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".

r-multilandr 1.0.0
Propagated dependencies: r-tidyterra@0.7.2 r-terra@1.8-50 r-sf@1.0-21 r-landscapemetrics@2.2.1 r-gridextra@2.3 r-ggplot2@3.5.2 r-ggally@2.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/phuais/multilandr
Licenses: GPL 3+
Synopsis: Landscape Analysis at Multiple Spatial Scales
Description:

This package provides a tidy workflow for landscape-scale analysis. multilandr offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the landscapemetrics package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about multilandr is provided in an introductory vignette included in this package and in the paper by Huais (2024) <doi:10.1007/s10980-024-01930-z>; see citation("multilandr") for details.

r-multiverse 0.6.2
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.2.1 r-styler@1.10.3 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-readr@2.1.5 r-r6@2.6.1 r-purrr@1.0.4 r-magrittr@2.0.3 r-knitr@1.50 r-jsonlite@2.0.0 r-furrr@0.3.1 r-formatr@1.14 r-evaluate@1.0.3 r-dplyr@1.1.4 r-distributional@0.5.0 r-collections@0.3.8 r-berryfunctions@1.22.13
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://mucollective.github.io/multiverse/
Licenses: GPL 3+
Synopsis: Create 'multiverse analysis' in R
Description:

Implement multiverse style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016) <doi:10.1177/1745691616658637> to show the robustness of statistical inference. Multiverse analysis is a philosophy of statistical reporting where paper authors report the outcomes of many different statistical analyses in order to show how fragile or robust their findings are. The multiverse package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm> allows users to concisely and flexibly implement multiverse-style analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.

r-multideggs 1.1.0
Propagated dependencies: r-visnetwork@2.1.2 r-shinydashboard@0.7.3 r-shiny@1.10.0 r-sfsmisc@1.1-20 r-rmarkdown@2.29 r-pbmcapply@1.5.1 r-pbapply@1.7-2 r-mass@7.3-65 r-magrittr@2.0.3 r-knitr@1.50 r-dt@0.33
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/elisabettasciacca/multiDEGGs/
Licenses: GPL 3
Synopsis: Multi-Omic Differentially Expressed Gene-Gene Pairs
Description:

This package performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) <doi:10.1093/bioinformatics/btad192>.

r-multfisher 1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multfisher
Licenses: GPL 3
Synopsis: Optimal Exact Tests for Multiple Binary Endpoints
Description:

Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints. The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures.

r-multiclust 1.38.0
Propagated dependencies: r-survival@3.8-3 r-mclust@6.1.1 r-dendextend@1.19.0 r-ctc@1.82.0 r-cluster@2.1.8.1 r-amap@0.8-20
Channel: guix-bioc
Location: guix-bioc/packages/m.scm (guix-bioc packages m)
Home page: https://bioconductor.org/packages/multiClust
Licenses: GPL 2+
Synopsis: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles
Description:

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

r-multiapply 2.1.5
Propagated dependencies: r-plyr@1.8.9 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://earth.bsc.es/gitlab/ces/multiApply
Licenses: GPL 3
Synopsis: Apply Functions to Multiple Multidimensional Arrays or Vectors
Description:

The base apply function and its variants, as well as the related functions in the plyr package, typically apply user-defined functions to a single argument (or a list of vectorized arguments in the case of mapply). The multiApply package extends this paradigm with its only function, Apply, which efficiently applies functions taking one or a list of multiple unidimensional or multidimensional arrays (or combinations thereof) as input. The input arrays can have different numbers of dimensions as well as different dimension lengths, and the applied function can return one or a list of unidimensional or multidimensional arrays as output. This saves development time by preventing the R user from writing often error-prone and memory-inefficient loops dealing with multiple complex arrays. Also, a remarkable feature of Apply is the transparent use of multi-core through its parameter ncores'. In contrast to the base apply function, this package suggests the use of target dimensions as opposite to the margins for specifying the dimensions relevant to the function to be applied.

r-mullerplot 0.1.3
Propagated dependencies: r-rcolorbrewer@1.1-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MullerPlot
Licenses: GPL 3
Synopsis: Generates Muller Plot from Population/Abundance/Frequency Dynamics Data
Description:

Generates Muller plot from parental/genealogy/phylogeny information and population/abundance/frequency dynamics data. Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist). A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process. To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time. MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs.

emacs-mu4easy 20250621.1238
Propagated dependencies: emacs-mu4e-column-faces@20250205.2118 emacs-mu4e-alert@20240911.1952 emacs-org-msg@20240902.447
Channel: emacs
Location: emacs/packages/melpa.scm (emacs packages melpa)
Home page: https://github.com/danielfleischer/mu4easy
Licenses:
Synopsis: Packages + configs for using mu4e with multiple accounts
Description:

Documentation at https://melpa.org/#/mu4easy

node-mustache 2.3.2
Channel: guix-science
Location: guix-science/packages/rstudio-node.scm (guix-science packages rstudio-node)
Home page: https://github.com/janl/mustache.js
Licenses: Expat
Synopsis: Logic-less {{mustache}} templates with JavaScript
Description:

Logic-less mustache templates with JavaScript

emacs-mu-cite 20190803.439
Propagated dependencies: emacs-flim@20250621.1317
Channel: emacs
Location: emacs/packages/melpa.scm (emacs packages melpa)
Home page: https://github.com/ksato9700/mu-cite
Licenses:
Synopsis: A library to provide MIME features
Description:

Documentation at https://melpa.org/#/mu-cite

emacs-helm-mu 20240910.854
Propagated dependencies: emacs-helm@4.0.2
Channel: emacs
Location: emacs/packages/melpa.scm (emacs packages melpa)
Home page: https://github.com/emacs-helm/helm-mu
Licenses:
Synopsis: Helm search for e-mails and contacts in mu4e
Description:

Documentation at https://melpa.org/#/helm-mu

ghc-multipart 0.2.1
Dependencies: ghc-stringsearch@0.3.6.6
Channel: guix
Location: gnu/packages/haskell-web.scm (gnu packages haskell-web)
Home page: http://www.github.com/silkapp/multipart
Licenses: Modified BSD
Synopsis: HTTP multipart library
Description:

HTTP multipart split out of the cgi package, for Haskell.

python-mujson 1.4
Channel: guix
Location: gnu/packages/python-xyz.scm (gnu packages python-xyz)
Home page: https://github.com/mattgiles/mujson
Licenses: Expat
Synopsis: Use the fastest JSON functions available at import time
Description:

This package selects the fastest JSON functions available at import time.

mumps-openmpi 5.8.0
Dependencies: openmpi@4.1.6 scalapack@2.2.2 pt-scotch@7.0.7 gfortran@11.4.0 openblas@0.3.29 metis@5.1.0
Channel: guix
Location: gnu/packages/maths.scm (gnu packages maths)
Home page: https://mumps-solver.org
Licenses: CeCILL-C
Synopsis: Multifrontal sparse direct solver (with MPI)
Description:

MUMPS (MUltifrontal Massively Parallel sparse direct Solver) solves a sparse system of linear equations A x = b using Gaussian elimination.

python-mudata 0.3.1
Propagated dependencies: python-anndata@0.11.1 python-h5py@3.13.0 python-pandas@2.2.3
Channel: guix
Location: gnu/packages/bioinformatics.scm (gnu packages bioinformatics)
Home page: https://github.com/scverse/mudata
Licenses: Modified BSD
Synopsis: Python package for multi-omics data analysis
Description:

Mudata is a Python package for multi-omics data analysis. It is designed to provide functionality to load, process, and store multimodal omics data.

cl-murmurhash 0.0.0-1.5433f5e
Propagated dependencies: cl-babel@0.5.0-3.627d6a6 cl-fiveam@1.4.2
Channel: guix
Location: gnu/packages/lisp-xyz.scm (gnu packages lisp-xyz)
Home page: https://github.com/ruricolist/cl-murmurhash/
Licenses: Expat
Synopsis: 32-bit version of Murmurhash3 for Common Lisp
Description:

This Common Lisp package offers an implementation of the 32-bit variant of MurmurHash3 (https://github.com/aappleby/smhasher), a fast non-crytographic hashing algorithm.

r-multimolang 0.1.1
Propagated dependencies: r-arrow@20.0.0.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/daedalusLAB/multimolang
Licenses: GPL 3
Synopsis: 'multimolang': Multimodal Language Analysis
Description:

Process OpenPose human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA NewsScape archive.

r-multilevlca 2.0.1
Propagated dependencies: r-tidyr@1.3.1 r-tictoc@1.2.1 r-rcpparmadillo@14.4.3-1 r-rcpp@1.0.14 r-pracma@2.4.4 r-mass@7.3-65 r-magrittr@2.0.3 r-klar@1.7-3 r-foreach@1.5.2 r-dplyr@1.1.4 r-clustmixtype@0.4-2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multilevLCA
Licenses: GPL 2+
Synopsis: Estimates and Plots Single-Level and Multilevel Latent Class Models
Description:

Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. For more technical details, see Lyrvall et al (2023) <doi:10.48550/arXiv.2305.07276>.

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+
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-multigraphr 0.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/termehs/multigraphr
Licenses: Expat
Synopsis: Probability Models and Statistical Analysis of Random Multigraphs
Description:

This package provides methods and models for analysing multigraphs as introduced by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit tests.

r-multiplencc 1.2-5
Propagated dependencies: r-survival@3.8-3 r-mgcv@1.9-3
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
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-multirobust 1.0.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiRobust
Licenses: GPL 2+
Synopsis: Multiply Robust Methods for Missing Data Problems
Description:

Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.

Total results: 525