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: NA
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:

Reference manual: Bayenet.pdf

Downloads:

Package source: Bayenet_0.2.tar.gz
Windows binaries: r-devel: Bayenet_0.2.zip, r-release: Bayenet_0.2.zip, r-oldrel: Bayenet_0.2.zip
macOS binaries: r-release (arm64): Bayenet_0.2.tgz, r-oldrel (arm64): Bayenet_0.2.tgz, r-release (x86_64): Bayenet_0.2.tgz, r-oldrel (x86_64): Bayenet_0.2.tgz
Old sources: Bayenet archive

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