Package: rgm 1.0.1

Francisco Richter

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." <arxiv:2304.01956>.

Authors:Francisco Richter [aut, cre], Veronica Vinciotti [ctb], Ernst Wit [ctb]

rgm_1.0.1.tar.gz
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rgm.pdf |rgm.html
rgm/json (API)

# Install 'rgm' in R:
install.packages('rgm', repos = c('https://franciscorichter.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/franciscorichter/rgm/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

cpp

4.00 score 5 scripts 170 downloads 6 exports 37 dependencies

Last updated 12 months agofrom:67c9a11af6. Checks:11 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 04 2025
R-4.5-win-x86_64OKFeb 04 2025
R-4.5-mac-x86_64OKFeb 04 2025
R-4.5-mac-aarch64OKFeb 04 2025
R-4.5-linux-x86_64OKFeb 04 2025
R-4.4-win-x86_64OKFeb 04 2025
R-4.4-mac-x86_64OKFeb 04 2025
R-4.4-mac-aarch64OKFeb 04 2025
R-4.3-win-x86_64OKFeb 04 2025
R-4.3-mac-x86_64OKFeb 04 2025
R-4.3-mac-aarch64OKFeb 04 2025

Exports:bprGmcmcrgmrotsample.datasim.rgm

Dependencies:BDgraphclicolorspacecpp11fansifarverggplot2gluegtablehugeigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrpROCR6RColorBrewerRcppRcppEigenrlangscalestibbletruncnormutf8vctrsviridisLitewithr

An Introduction to rgm

Rendered fromrgm.Rmdusingknitr::rmarkdownon Feb 04 2025.

Last update: 2023-12-29
Started: 2023-12-20