Package: GHS
Title: Graphical Horseshoe MCMC Sampler Using Data Augmented Block
        Gibbs Sampler
Version: 0.1
Author: Ashutosh Srivastava<srivas48@purdue.edu>, Anindya Bhadra<bhadra@purdue.edu>
Maintainer: Ashutosh Srivastava<srivas48@purdue.edu>
Description: Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.
Depends: R (>= 3.4.0), stats, MASS
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1.9000
NeedsCompilation: no
Packaged: 2018-10-24 16:05:44 UTC; Ashutosh Srivastava
Repository: CRAN
Date/Publication: 2018-10-30 18:00:07 UTC
Built: R 4.6.0; ; 2026-03-10 02:43:42 UTC; windows
