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) .


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Reference manual

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install.packages("BGGM")

1.0.0 by Donald Williams, 2 months ago


Browse source code at https://github.com/cran/BGGM


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


Documentation:   PDF Manual  


Task views: Psychometric Models and Methods


GPL-2 license


Imports mvnfast, foreach, doParallel, ggplot2, mvtnorm, stringr, ggridges, GGally, pracma, network, bayesplot, sna, shiny, reshape2, cowplot, stats, parallel, Matrix, reshape, MASS

Suggests knitr, rmarkdown, dplyr


See at CRAN