CRAN/E | BayesS5

BayesS5

Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

Installation

About

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.

arxiv.org/abs/1507.07106v4

Key Metrics

Version 1.41
R ≥ 3.4.0
Published 2020-03-24 1485 days ago
Needs compilation? no
License GPL-2
License GPL-3
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Maintainer

Maintainer

Minsuk Shin

minsuk000@gmail.com

Authors

Minsuk Shin
Ruoxuan Tian

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

BayesS5 archive

Depends

R ≥ 3.4.0

Imports

Matrix
stats
snowfall
abind
splines2