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  "Title": "Graphical Horseshoe MCMC Sampler Using Data Augmented Block\nGibbs Sampler",
  "Version": "0.1",
  "Author": "Ashutosh Srivastava<srivas48@purdue.edu>, Anindya\nBhadra<bhadra@purdue.edu>",
  "Maintainer": "Ashutosh Srivastava<srivas48@purdue.edu>",
  "Description": "Draw posterior samples to estimate the precision matrix\nfor multivariate Gaussian data. Posterior means of the samples\nis the graphical horseshoe estimate by Li, Bhadra and\nCraig(2017) <arXiv:1707.06661>. The function uses matrix\ndecomposition and variable change from the Bayesian graphical\nlasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable\naugmentation for sampling under the horseshoe prior by Makalic\nand Schmidt(2016) <arXiv:1508.03884>. Structure of the\ngraphical horseshoe function was inspired by the Bayesian\ngraphical lasso function using blocked sampling, authored by\nWang(2012) <doi:10.1214/12-BA729>.",
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