bgms

Bayesian Graphical Models in R

Estimate networks of mixed binary, ordinal, and continuous variables
Use Bayesian variable selection to:
- Quantify evidence for including or excluding edges
- Detect group differences in network parameters
Discover communities of related edges with Bayesian edge clustering
Fast posterior inference with:
- No-U-Turn sampler
- Parallel multi-chain sampling
- C++ backend

Explore structure in your network with principled Bayesian inference.

Quick Start

# Install from CRAN
install.packages("bgms")

# Fit an ordinal Markov random field to the Wenchuan earthquake data
library(bgms)
data(Wenchuan)
fit <- bgm(Wenchuan)
summary(fit)

User’s Guide

Learn bgms from the ground up: installation, basic usage, and statistical concepts behind Bayesian graphical models for mixed variable types.

Guide →

Reference

Complete documentation for all R functions: bgm(), bgmCompare(), S3 methods, and extractor functions.

Reference →

Manual

Technical documentation for contributors: C++ architecture, MCMC algorithms, and implementation details.

Manual →

Citation

If you use bgms in your research, please cite the software package.

Choose a citation format (version is set to the documented release):

Marsman, M., & van den Bergh, D. (2026). bgms: Bayesian analysis of graphical models (R package, version 0.2.0.0). https://CRAN.R-project.org/package=bgms


@Manual{bgms-package,
  title   = {bgms: Bayesian Analysis of Graphical Models},
  author  = {Marsman, Maarten and van den Bergh, Don},
  year    = {2026},
  note    = {R package version 0.2.0.0},
  url     = {https://CRAN.R-project.org/package=bgms}
}


TY  - COMP
TI  - bgms: Bayesian Analysis of Graphical Models
AU  - Marsman, Maarten
AU  - van den Bergh, Don
PY  - 2026
N1  - R package version 0.2.0.0
UR  - https://CRAN.R-project.org/package=bgms
ER  -

To generate citations directly from your installed package in R:

citation("bgms")
toBibtex(citation("bgms"))