Bayenet: Bayesian Quantile Elastic Net for Genetic Study
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 for quantile regression in genetic analysis. 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: |
0.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp, stats, MCMCpack, base, gsl, VGAM, MASS, hbmem, SuppDists |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2024-04-05 |
| 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.