GHS (0.1)

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Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler.

http://cran.r-project.org/web/packages/GHS

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) . The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) , and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) . Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) .

Maintainer: Ashutosh Srivastava
Author(s): Ashutosh Srivastava<srivas48@purdue.edu>, Anindya Bhadra<bhadra@purdue.edu>

License: GPL-2

Uses: MASS

Released 8 months ago.


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