Bayenet: Robust Bayesian Elastic Net
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 |
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