Bayenet

Bayesian Quantile Elastic Net for Genetic Study

CRAN Package

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.

  • Version0.2
  • R version≥ 3.5.0
  • LicenseGPL-2
  • Needs compilation?Yes
  • Last release04/05/2024

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  • Imports7 packages
  • Linking To2 packages