Package: Bayenet
Type: Package
Title: Robust Bayesian Elastic Net
Version: 0.3
Date: 2025-03-19
Authors@R: c( person("Xi", "Lu", role = c("aut", "cre"),
                      email = "xilu@ksu.edu"),
              person("Cen", "Wu", role = "aut"))
Description: 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.
Depends: R (>= 3.5.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, stats, MCMCpack, base, gsl, VGAM, MASS, hbmem, SuppDists
RoxygenNote: 7.3.1
NeedsCompilation: yes
Repository: CRAN
Packaged: 2025-03-19 20:45:58 UTC; xilu0
Author: Xi Lu [aut, cre],
  Cen Wu [aut]
Maintainer: Xi Lu <xilu@ksu.edu>
Date/Publication: 2025-03-19 21:00:01 UTC
Built: R 4.3.3; aarch64-apple-darwin20; 2025-03-19 22:44:33 UTC; unix
Archs: Bayenet.so.dSYM
