baygel - Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models
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) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.
Last updated 1 years ago
openblascppopenmp
2.70 score 2 scripts 265 downloadsabglasso - Adaptive Bayesian Graphical Lasso
Implements a Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler simulates the posterior distribution of precision matrices of a Gaussian Graphical Model. This sampler was adapted from the original MATLAB routine proposed in Wang (2012) <doi:10.1214/12-BA729>.
Last updated 4 years ago
1.70 score 1 scripts 342 downloads