GHS: Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler

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>.

Version: 0.1
Depends: R (≥ 3.4.0), stats, MASS
Published: 2018-10-30
Author: Ashutosh Srivastava, Anindya Bhadra
Maintainer: Ashutosh Srivastava <srivas48 at purdue.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: GHS results

Downloads:

Reference manual: GHS.pdf
Package source: GHS_0.1.tar.gz
Windows binaries: r-devel: GHS_0.1.zip, r-release: GHS_0.1.zip, r-oldrel: GHS_0.1.zip
OS X binaries: r-release: GHS_0.1.tgz, r-oldrel: GHS_0.1.tgz

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