Title:	Adaptive Bayesian Graphical Lasso
Version:	0.1.1
Description:	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>.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.1.1.9000
Imports:	MASS, pracma, stats, statmod
Suggests:	testthat
NeedsCompilation:	no
Packaged:	2021-07-13 08:43:20 UTC; QXZ0GWG
Author:	Jarod Smith [aut, cre], Mohammad Arashi [aut], Andriette Bekker [aut]
Maintainer:	Jarod Smith <jarodsmith706@gmail.com>
Repository:	CRAN
Date/Publication:	2021-07-13 22:10:05 UTC

Adaptive Bayesian graphical lasso MCMC sampler

Description

A Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler is adapted from the MATLAB routines used in Wang (2012).

Usage

BayesGlassoBlock(X, burnin = 1000, nmc = 2000)

Arguments

Value

list containing:

Sig

A p by p by nmc array of saved posterior samples of covariance matrices.

Omega

A p by p by nmc array of saved posterior samples of precision matrices.

Lambda

A 1 by nmc vector of saved posterior samples of lambda values.

References

Wang, H. (2012). Bayesian graphical lasso models and efficient posterior computation. Bayesian Analysis, 7(4). doi: 10.1214/12-BA729.

Examples

# Generate true covariance matrix:
p             <- 10
n             <- 50
SigTrue       <- pracma::Toeplitz(c(0.7^rep(1:p-1)))
CTrue         <- pracma::inv(SigTrue)
# Generate expected value vector:
mu            <- rep(0,p)
# Generate multivariate normal distribution:
set.seed(123)
X             <- MASS::mvrnorm(n,mu=mu,Sigma=SigTrue)
abglasso_post <- BayesGlassoBlock(X,burnin = 1000,nmc = 2000)