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]

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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'))

Peer review:

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

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

On CRAN:

4.78 score 5 scripts 147 downloads 6 exports 37 dependencies

Last updated 8 months agofrom:67c9a11af6. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-win-x86_64OKNov 06 2024
R-4.5-linux-x86_64OKNov 06 2024
R-4.4-win-x86_64OKNov 06 2024
R-4.4-mac-x86_64OKNov 06 2024
R-4.4-mac-aarch64OKNov 06 2024
R-4.3-win-x86_64OKNov 06 2024
R-4.3-mac-x86_64OKNov 06 2024
R-4.3-mac-aarch64OKNov 06 2024

Exports:bprGmcmcrgmrotsample.datasim.rgm

Dependencies:BDgraphclicolorspacecpp11fansifarverggplot2gluegtablehugeigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrpROCR6RColorBrewerRcppRcppEigenrlangscalestibbletruncnormutf8vctrsviridisLitewithr

An Introduction to rgm

Rendered fromrgm.Rmdusingknitr::rmarkdownon Nov 06 2024.

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