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.
- Version2.1.4
- R versionR (≥ 4.0.0)
- LicenseGPL-2
- Needs compilation?Yes
- BGGM citation info
- Last release12/13/2024
Documentation
- VignetteControlling for Variables
- VignetteThree Ways to Test the Same Hypothesis
- VignetteIn Tandem: Confirmatory and Exploratory Testing
- VignetteMCMC Diagnostics
- VignetteNetwork Plots
- VignetteCustom Network Statistics
- VignetteCustom Network Comparisons
- VignettePredictability: Binary, Ordinal, and Continuous
- VignetteTesting Sums
- VignetteGraphical VAR
- MaterialNEWS
Team
Philippe Rast
MaintainerShow author detailsJoris Mulder
Show author detailsRolesAuthorDonald Williams
Show author detailsRolesAuthor
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- Imports11 packages
- Suggests8 packages
- Linking To4 packages
- Reverse Imports1 package