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:
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