Package: BayesS5
Type: Package
Title: Bayesian Variable Selection Using Simplified Shotgun Stochastic
        Search with Screening (S5)
Version: 1.31
Date: 2018-10-25
Author: Minsuk Shin and Ruoxuan Tian
Maintainer: Minsuk Shin <minsuk000@gmail.com>
Depends: R (>= 3.3)
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).
URL: https://arxiv.org/pdf/1507.07106.pdf
License: GPL (>= 2)
Repository: CRAN
NeedsCompilation: no
Packaged: 2018-10-26 05:58:43 UTC; shinminsuk
Date/Publication: 2018-10-26 06:20:03 UTC
