Bayesian Gaussian Graphical Models

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) , Williams and Mulder (2019) , Williams, Rast, Pericchi, and Mulder (2019) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


2.0.4 by Donald Williams, 5 months ago

Report a bug at

Browse source code at

Authors: Donald Williams [aut, cre] , Joris Mulder [aut]

Documentation:   PDF Manual  

Task views:

GPL-2 license

Imports BFpack, GGally, ggplot2, ggridges, grDevices, MASS, methods, mvnfast, network, reshape, Rcpp, Rdpack, sna, stats, utils

Suggests abind, assortnet, networktools, mice, psych, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo, RcppDist, RcppProgress

Suggested by BBcor, bayeslincom.

See at CRAN