# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "GHS" in publications use:' type: software license: GPL-2.0-only title: 'GHS: Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler' version: '0.1' doi: 10.32614/CRAN.package.GHS abstract: 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) . authors: - family-names: Srivastava given-names: Ashutosh email: srivas48@purdue.edu - family-names: Bhadra given-names: Anindya email: bhadra@purdue.edu repository: https://CRAN.R-project.org/package=GHS date-released: '2018-10-30' contact: - family-names: Srivastava given-names: Ashutosh email: srivas48@purdue.edu