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.
||R (≥ 3.5.0)
||Rcpp, stats, MCMCpack, base, gsl, VGAM, MASS, hbmem, SuppDists
||Xi Lu [aut, cre],
Cen Wu [aut]
||Xi Lu <xilu at ksu.edu>
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