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

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2.0.2 by Donald Williams, 2 months ago

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

Documentation:   PDF Manual  

Task views: Psychometric Models and Methods

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 bayeslincom, insight.

See at CRAN