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>.
Package details |
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Author | Ashutosh Srivastava<srivas48@purdue.edu>, Anindya Bhadra<bhadra@purdue.edu> |
Maintainer | Ashutosh Srivastava<srivas48@purdue.edu> |
License | GPL-2 |
Version | 0.1 |
Package repository | View on CRAN |
Installation |
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