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 |
| Reference manual: | Bayenet.html , Bayenet.pdf |
| Package source: | Bayenet_0.3.tar.gz |
| Windows binaries: | r-devel: Bayenet_0.3.zip, r-release: Bayenet_0.3.zip, r-oldrel: Bayenet_0.3.zip |
| macOS binaries: | r-release (arm64): Bayenet_0.3.tgz, r-oldrel (arm64): Bayenet_0.3.tgz, r-release (x86_64): Bayenet_0.3.tgz, r-oldrel (x86_64): Bayenet_0.3.tgz |
| Old sources: | Bayenet archive |
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