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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