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/>.

Package details

AuthorDonald Williams [aut, cre], Joris Mulder [aut]
MaintainerDonald Williams <>
Package repositoryView on CRAN
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BGGM documentation built on Aug. 20, 2021, 5:08 p.m.