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) . 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) .


Reference manual

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0.1 by Ashutosh Srivastava, 3 years ago

Browse source code at

Authors: Ashutosh Srivastava<[email protected]> , Anindya Bhadra<[email protected]>

Documentation:   PDF Manual  

GPL-2 license

Depends on stats, MASS

See at CRAN