CRAN/E | BGGM

BGGM

Bayesian Gaussian Graphical Models

Installation

About

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/osf.io/x8dpr, Williams and Mulder (2019) doi:10.31234/osf.io/ypxd8, Williams, Rast, Pericchi, and Mulder (2019) doi:10.31234/osf.io/yt386.

Citation BGGM citation info
Bug report File report

Key Metrics

Version 2.0.4
R ≥ 3.5.0
Published 2021-08-20 977 days ago
Needs compilation? yes
License GPL-2
CRAN checks BGGM results

Downloads

Yesterday 886 -0%
Last 7 days 5.387 -14%
Last 30 days 22.239 -8%
Last 90 days 45.984 +10745%
Last 365 days 51.078 +905%

Maintainer

Maintainer

Donald Williams

drwwilliams@ucdavis.edu

Authors

Donald Williams

aut / cre

Joris Mulder

aut

Material

NEWS
Reference manual
Package source

Vignettes

Controlling for Variables
Three Ways to Test the Same Hypothesis
Confirmatory and Exploratory Testing
Installation
MCMC Diagnostics
Network Plots
Custom Network Statistics
Custom Network Comparisons
Predictability: Binary, Ordinal, and Continuous
Testing Sums
Graphical VAR

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

BGGM archive

Depends

R ≥ 3.5.0

Imports

BFpack ≥ 0.2.1
GGally ≥ 1.4.0
ggplot2 ≥ 3.2.1
ggridges ≥ 0.5.1
grDevices
MASS ≥ 7.3-51.5
methods
mvnfast ≥ 0.2.5
network ≥ 1.15
reshape ≥ 0.8.8
Rcpp ≥ 1.0.4.6
Rdpack ≥ 0.11-1
sna ≥ 2.5
stats
utils

Suggests

abind ≥ 1.4-5
assortnet ≥ 0.12
networktools ≥1.2.3
mice ≥ 3.8.0
psych
knitr
rmarkdown

LinkingTo

Rcpp
RcppArmadillo
RcppDist
RcppProgress

Reverse Suggests

bayeslincom
BBcor