rgm: Advanced Inference with Random Graphical Models

Implements state-of-the-art Random Graphical Models (RGMs) for multivariate data analysis across multiple environments, offering tools for exploring network interactions and structural relationships. Capabilities include joint inference across environments, integration of external covariates, and a Bayesian framework for uncertainty quantification. Applicable in various fields, including microbiome analysis. Methods based on Vinciotti, V., Wit, E., & Richter, F. (2023). "Random Graphical Model of Microbiome Interactions in Related Environments." <doi:10.48550/arXiv.2304.01956>.

Version: 1.0.4
Depends: R (≥ 3.5.0)
Imports: truncnorm, BDgraph, MASS, huge, ggplot2, stats, pROC, reshape2
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
Published: 2024-03-21
Author: Francisco Richter [aut, cre], Veronica Vinciotti [ctb], Ernst Wit [ctb]
Maintainer: Francisco Richter <richtf at usi.ch>
BugReports: https://github.com/franciscorichter/rgm/issues
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README
CRAN checks: rgm results

Documentation:

Reference manual: rgm.pdf
Vignettes: rgm

Downloads:

Package source: rgm_1.0.4.tar.gz
Windows binaries: r-prerel: rgm_1.0.4.zip, r-release: rgm_1.0.4.zip, r-oldrel: rgm_1.0.4.zip
macOS binaries: r-prerel (arm64): rgm_1.0.4.tgz, r-release (arm64): rgm_1.0.4.tgz, r-oldrel (arm64): rgm_1.0.4.tgz, r-prerel (x86_64): rgm_1.0.4.tgz, r-release (x86_64): rgm_1.0.4.tgz
Old sources: rgm archive

Linking:

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