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GHS  

Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler
View on CRAN: Click here


Download and install GHS package within the R console
Install from CRAN:
install.packages("GHS")

Install from Github:
library("remotes")
install_github("cran/GHS")

Install by package version:
library("remotes")
install_version("GHS", "0.1")



Attach the package and use:
library("GHS")
Maintained by
Ashutosh Srivastava
[Scholar Profile | Author Map]
First Published: 2018-10-30
Latest Update: 2018-10-30
Description:
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) .
How to cite:
Ashutosh Srivastava (2018). GHS: Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler. R package version 0.1, https://cran.r-project.org/web/packages/GHS. Accessed 08 May. 2025.
Previous versions and publish date:
No previous versions
Other packages that cited GHS R package
View GHS citation profile
Other R packages that GHS depends, imports, suggests or enhances
Complete documentation for GHS
Functions, R codes and Examples using the GHS R package
Some associated functions: GHS_est . 
Some associated R codes: GHS_function_test.R . ex.R .  Full GHS package functions and examples
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