Package: BayesS5
Type: Package
Title: Bayesian Variable Selection Using Simplified Shotgun Stochastic
        Search with Screening (S5)
Version: 1.41
Date: 2020-03-20
Author: Minsuk Shin and Ruoxuan Tian
Maintainer: Minsuk Shin <minsuk000@gmail.com>
Depends: R (>= 3.4.0)
Imports: Matrix, stats, snowfall, abind, splines2
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" (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. 
URL: https://arxiv.org/abs/1507.07106v4
License: GPL (>= 2)
Repository: CRAN
NeedsCompilation: no
Packaged: 2020-03-23 16:44:09 UTC; mshin
Date/Publication: 2020-03-24 07:40:14 UTC
Built: R 4.3.3; ; 2025-01-24 04:29:08 UTC; unix
