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BayesS5  

Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)
View on CRAN: Click here


Download and install BayesS5 package within the R console
Install from CRAN:
install.packages("BayesS5")

Install from Github:
library("remotes")
install_github("cran/BayesS5")

Install by package version:
library("remotes")
install_version("BayesS5", "1.41")



Attach the package and use:
library("BayesS5")
Maintained by
Minsuk Shin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-01-17
Latest Update: 2020-03-24
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.
How to cite:
Minsuk Shin (2017). BayesS5: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5). R package version 1.41, https://cran.r-project.org/web/packages/BayesS5. Accessed 13 Oct. 2026.
Previous versions and publish date:
1.22 (2017-01-17 19:07), 1.30 (2017-02-24 23:33), 1.31 (2018-10-26 08:20), 1.40 (2020-03-23 12:10), (2026-07-09 07:58)
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