BGGM: 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) <doi:10.31234/>, Williams and Mulder (2019) <doi:10.31234/>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: mvnfast (≥ 0.2.5), foreach (≥ 1.4.7), doParallel (≥ 1.0.15), ggplot2 (≥ 3.2.1), mvtnorm (≥ 1.0.11), stringr (≥ 1.4.0), ggridges (≥ 0.5.1), GGally (≥ 1.4.0), pracma (≥ 2.2.5), network (≥ 1.15), bayesplot (≥ 1.7.1), sna (≥ 2.5), shiny (≥ 1.4.0), reshape2 (≥ 1.4.3), cowplot (≥ 1.0.0), stats, parallel, Matrix, reshape, MASS
Suggests: knitr, rmarkdown, dplyr
Published: 2020-02-06
Author: Donald Williams [aut, cre], Joris Mulder [aut]
Maintainer: Donald Williams <drwwilliams at>
License: GPL-2
NeedsCompilation: no
Materials: README
In views: Psychometrics
CRAN checks: BGGM results


Reference manual: BGGM.pdf
Vignettes: Credible Intervals
Plotting the Network Structure
Comparing GGMs with the Posterior Predicive Distributions
Predictability: Part One
Package source: BGGM_1.0.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: BGGM_1.0.0.tgz, r-oldrel: BGGM_1.0.0.tgz


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