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.
Version: 0.3 Depends: R (≥ 3.5.0) Imports: Rcpp, stats, MCMCpack, base, gsl, VGAM, MASS, hbmem, SuppDists LinkingTo: Rcpp, RcppArmadillo Published: 2025-03-19 DOI: 10.32614/CRAN.package.Bayenet Author: Xi Lu [aut, cre], Cen Wu [aut] Maintainer: Xi Lu <xilu at ksu.edu> License: GPL-2 NeedsCompilation: yes CRAN checks: Bayenet results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=Bayenet to link to this page.
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