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baygel (version 0.3.0)

Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models

Description

This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) ; Smith et al. (2022) and Smith et al. (2023) , respectively.

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Install

install.packages('baygel')

Monthly Downloads

193

Version

0.3.0

License

GPL (>= 3)

Issues

0

Pull Requests

0

Stars

1

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Maintainer

Jarod Smith

Last Published

November 11th, 2023

Functions in baygel (0.3.0)

blockBAGL

Bayesian adaptive graphical lasso block Gibbs sampler for Gaussian graphical models.
blockBAGENII

Type II naive Bayesian adaptive graphical elastic net block Gibbs sampler for Gaussian graphical models.
blockBAGENI

Type I naive Bayesian adaptive graphical elastic net block Gibbs sampler for Gaussian graphical models.
blockBAGR

Bayesian adaptive graphical ridge block Gibbs sampler for Gaussian graphical models.
blockBGL

Bayesian graphical lasso block Gibbs sampler for Gaussian graphical models.
blockBGR

Bayesian graphical ridge block Gibbs sampler for Gaussian graphical models.
blockBGEN

Naive Bayesian graphical elastic net block Gibbs sampler for Gaussian graphical models.