Package: BayesS5
Type: Package
Title: Bayesian Variable Selection Using Simplified Shotgun Stochastic
        Search with Screening (S5)
Version: 1.30
Date: 2017-02-24
Author: Minsuk Shin and Ruoxuan Tian
Maintainer: Minsuk Shin <minsuk000@gmail.com>
Depends: R (>= 3.2.4)
Imports: Matrix, stats, snowfall, abind
Description: 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. 
URL: http://www.stat.tamu.edu/~minsuk/publications/nonlocal_sinica7.pdf
License: GPL (>= 2)
Repository: CRAN
NeedsCompilation: no
Packaged: 2017-02-24 22:22:19 UTC; minsuk
Date/Publication: 2017-02-24 23:33:26
