Robust Bayesian Elastic Net R package Bayenet version 0.3 As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R. Software Depends: R (>= 3.5.0) Imports: Rcpp, stats, MCMCpack, base, gsl, VGAM, MASS, hbmem, SuppDists LinkingTo: Rcpp, RcppArmadillo Xi Lu <xilu@ksu.edu> Comprehensive R Archive Network (CRAN) Xi Lu [aut, cre], Cen Wu [aut] GPL-2 2025-03-19 application/tgz https://CRAN.R-project.org/package=Bayenet doi:10.32614/CRAN.package.Bayenet