Nothing
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/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>.
Getting started
Package overview
Controlling for Variables
Custom Network Comparisons
Custom Network Statistics
Graphical VAR
In Tandem: Confirmatory and Exploratory Testing
MCMC Diagnostics
Network Plots
Predictability: Binary, Ordinal, and Continuous
Testing Sums
Three Ways to Test the Same Hypothesis
Troubleshoot
Browse package contents
Package details |
|
|---|---|
| Author | Donald Williams [aut], Joris Mulder [aut], Philippe Rast [aut, cre] |
| Maintainer | Philippe Rast <rast.ph@gmail.com> |
| License | GPL-2 |
| Version | 2.1.3 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the BGGM package in your browser
Any scripts or data that you put into this service are public.
