Bayesian Gaussian Graphical Models R package BGGM version 2.1.6 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) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>. Software Depends: R (>= 4.0.0) Imports: BFpack (>= 1.2.3), GGally (>= 1.4.0), ggplot2 (>= 3.2.1), ggridges (>= 0.5.1), grDevices, MASS (>= 7.3-51.5), methods, mvnfast (>= 0.2.5), network (>= 1.15), reshape (>= 0.8.8), Rcpp (>= 1.0.4.6), Rdpack (>= 0.11-1), sna (>= 2.5), stats, utils, LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: abind (>= 1.4-5), assortnet (>= 0.12), networktools (>= 1.3.0), mice (>= 3.8.0), psych, knitr, rmarkdown, testthat (>= 3.0.0) Philippe Rast <rast.ph@gmail.com> Comprehensive R Archive Network (CRAN) Donald Williams [aut], Joris Mulder [aut], Philippe Rast [aut, cre] GPL-2 2025-12-02 application/tgz https://CRAN.R-project.org/package=BGGM doi:10.32614/CRAN.package.BGGM