Package: Bayenet 0.2

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.

Authors:Xi Lu [aut, cre], Cen Wu [aut]