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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) <doi:10.48550/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) <doi:10.48550/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 |
| DOI: | 10.32614/CRAN.package.GHS |
| Author: | Ashutosh Srivastava, Anindya Bhadra |
| Maintainer: | Ashutosh Srivastava <srivas48 at purdue.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | GHS results |
| 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 |
| macOS binaries: | r-release (arm64): GHS_0.1.tgz, r-oldrel (arm64): GHS_0.1.tgz, r-release (x86_64): GHS_0.1.tgz, r-oldrel (x86_64): GHS_0.1.tgz |
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These binaries (installable software) and packages are in development.
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