SachaEpskamp/BayesGGM: Bayesian Estimators for Gaussian Graphical Models

BayesGGM is a R package to estimate Gaussian Graphical Models (GGMs) using 6 different Bayesian estimation methods. Researchers can choose between 1) a Bayesian GLASSO, 2) an Adaptive Bayesian GLASSO, 3) a Graphical Horseshoe, 4) a (Ridge-type) penalized estimator based on an Eigenvalue decompostion of the Wishart prior, 5) an adaptive (Ridge-type) penalized estimator based on an Eigenvalue decomposition of the Wishart prior, and 6) a normal Wishart prior with no regularization. In addition, BayesGGM also provide 95\% Credibility Intervals for the strength, closeness, and betweenness centrality, and imputes missing data.

Getting started

Package details

AuthorJoran Jongerling & Sacha Epskamp
MaintainerJoran Jongerling <[email protected]>
LicenseGPL-2
Version0.1.5
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("SachaEpskamp/BayesGGM")
SachaEpskamp/BayesGGM documentation built on Aug. 22, 2018, 8:06 a.m.