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 gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://arxiv.org/abs/1507.07106v4
NeedsCompilation:	no
CRAN checks:	BayesS5 results

Documentation:

Reference manual:

BayesS5.pdf

Downloads:

Package source:	BayesS5_1.41.tar.gz
Windows binaries:	r-devel: BayesS5_1.41.zip, r-release: BayesS5_1.41.zip, r-oldrel: BayesS5_1.41.zip
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

Linking:

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