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" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.

Version: 1.41
Depends: R (≥ 3.4.0)
Imports: Matrix, stats, snowfall, abind, splines2
Published: 2020-03-24
DOI: 10.32614/CRAN.package.BayesS5
Author: Minsuk Shin and Ruoxuan Tian
Maintainer: Minsuk Shin <minsuk000 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: BayesS5 results


Reference manual: BayesS5.pdf


Package source: BayesS5_1.41.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BayesS5_1.41.tgz, r-oldrel (arm64): BayesS5_1.41.tgz, r-release (x86_64): BayesS5_1.41.tgz, r-oldrel (x86_64): BayesS5_1.41.tgz
Old sources: BayesS5 archive


Please use the canonical form to link to this page.