emBayes: Robust Bayesian Variable Selection via Expectation-Maximization

Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'.

Version:	0.1.6
Depends:	R (≥ 4.2.0)
Imports:	Rcpp, glmnet
LinkingTo:	Rcpp, RcppArmadillo
Published:	2024-09-15
DOI:	10.32614/CRAN.package.emBayes
Author:	Yuwen Liu [aut, cre], Cen Wu [aut]
Maintainer:	Yuwen Liu <yuwenliu9 at gmail.com>
License:	GPL-2
NeedsCompilation:	yes
CRAN checks:	emBayes results

Documentation:

Reference manual:

emBayes.pdf

Downloads:

Package source:	emBayes_0.1.6.tar.gz
Windows binaries:	r-devel: emBayes_0.1.6.zip, r-release: emBayes_0.1.6.zip, r-oldrel: emBayes_0.1.6.zip
macOS binaries:	r-release (arm64): emBayes_0.1.6.tgz, r-oldrel (arm64): emBayes_0.1.6.tgz, r-release (x86_64): emBayes_0.1.6.tgz, r-oldrel (x86_64): emBayes_0.1.6.tgz
Old sources:	emBayes archive

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