Robust Bayesian Variable Selection via Expectation-Maximization R package emBayes version 0.1.6 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++'. Software Depends: R (>= 4.2.0) Imports: Rcpp, glmnet LinkingTo: Rcpp, RcppArmadillo Yuwen Liu <yuwenliu9@gmail.com> Comprehensive R Archive Network (CRAN) Yuwen Liu [aut, cre], Cen Wu [aut] GPL-2 2024-09-15 application/tgz https://CRAN.R-project.org/package=emBayes doi:10.32614/CRAN.package.emBayes