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.

Documentation

Manual: BayesS5.pdf
Vignette: None available.

Maintainer: Minsuk Shin <minsuk000 at gmail.com>

Author(s): Minsuk Shin and Ruoxuan Tian

Install package and any missing dependencies by running this line in your R console:

install.packages("BayesS5")

Depends R (>= 3.2.4)
Imports Matrix, stats, snowfall, abind
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Package BayesS5
Materials
URL http://www.stat.tamu.edu/~minsuk/publications/nonlocal_sinica7.pdf
Task Views
Version 1.30
Published 2017-02-24
License GPL (>= 2)
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks BayesS5 check results
Package source BayesS5_1.30.tar.gz