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)
NeedsCompilation: no
Packaged: 2026-09-18 06:33:24 UTC; root
Repository: https://minsuk000.r-universe.dev
Date/Publication: 2020-03-24 06:40:14 UTC
RemoteUrl: https://github.com/cran/BayesS5
RemoteRef: HEAD
RemoteSha: 1d78d767f0a8407eaf7fa96f9113ca3336931657