rgm
Advanced Inference with Random Graphical Models
Implements Random Graphical Models for multivariate data analysis across multiple environments, providing 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. C., and Richter, F. (2026) "Random Graphical Model of Microbiome Interactions in Related Environments" <doi:10.1007/s13253-024-00638-6>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.1 |
rolling linux/jammy R-4.5 | rgm_1.2.1.tar.gz |
969.6 KiB |
1.2.1 |
rolling linux/noble R-4.5 | rgm_1.2.1.tar.gz |
970.3 KiB |
1.2.1 |
rolling source/ R- | rgm_1.2.1.tar.gz |
1.7 MiB |
1.2.1 |
latest linux/jammy R-4.5 | rgm_1.2.1.tar.gz |
969.6 KiB |
1.2.1 |
latest linux/noble R-4.5 | rgm_1.2.1.tar.gz |
970.3 KiB |
1.2.1 |
latest source/ R- | rgm_1.2.1.tar.gz |
1.7 MiB |
1.2.1 |
2026-04-23 source/ R- | rgm_1.2.1.tar.gz |
0 B |
1.0.4 |
2025-04-20 source/ R- | rgm_1.0.4.tar.gz |
1.6 MiB |