Package: GHS 0.1
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>.
Authors:
GHS_0.1.tar.gz
GHS_0.1.zip(r-4.5)GHS_0.1.zip(r-4.4)GHS_0.1.zip(r-4.3)
GHS_0.1.tgz(r-4.4-any)GHS_0.1.tgz(r-4.3-any)
GHS_0.1.tar.gz(r-4.5-noble)GHS_0.1.tar.gz(r-4.4-noble)
GHS_0.1.tgz(r-4.4-emscripten)GHS_0.1.tgz(r-4.3-emscripten)
GHS.pdf |GHS.html✨
GHS/json (API)
# Install 'GHS' in R: |
install.packages('GHS', repos = c('https://ash0204.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:41da5b01a8. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 07 2024 |
R-4.5-win | OK | Sep 07 2024 |
R-4.5-linux | OK | Sep 07 2024 |
R-4.4-win | OK | Sep 07 2024 |
R-4.4-mac | OK | Sep 07 2024 |
R-4.3-win | OK | Sep 07 2024 |
R-4.3-mac | OK | Sep 07 2024 |
Exports:GHS_est
Dependencies:MASS
Readme and manuals
Help Manual
Help page | Topics |
---|---|
GHS MCMC sampler using data-augmented block (column-wise) Gibbs sampler | GHS_est |