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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 ORCID iD [aut, cre], Mohammad Arashi ORCID iD [aut], Andriette Bekker ORCID iD [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

X

Numeric matrix.

burnin

An integer specifying the number of burn-in iterations.

nmc

An integer specifying the number of MCMC samples.

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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