BayesS5: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings (2017+), by Minsuk Shin, Anirban Bhattachary, and Valen E. Johnson, accepted in Statistica Sinica.

Version: 1.30
Depends: R (≥ 3.2.4)
Imports: Matrix, stats, snowfall, abind
Published: 2017-02-24
Author: Minsuk Shin and Ruoxuan Tian
Maintainer: Minsuk Shin <minsuk000 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.stat.tamu.edu/~minsuk/publications/nonlocal_sinica7.pdf
NeedsCompilation: no
CRAN checks: BayesS5 results

Downloads:

Reference manual: BayesS5.pdf
Package source: BayesS5_1.30.tar.gz
Windows binaries: r-devel: BayesS5_1.30.zip, r-release: BayesS5_1.30.zip, r-oldrel: BayesS5_1.30.zip
OS X Mavericks binaries: r-release: BayesS5_1.30.tgz, r-oldrel: BayesS5_1.30.tgz
Old sources: BayesS5 archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BayesS5 to link to this page.