pqrBayes

Bayesian regularized quantile regression utilizing sparse priors to impose exact sparsity leads to efficient Bayesian shrinkage estimation, variable selection and statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (doi:10.3390/e26090794) and (doi:10.1111/biom.13670), and regularized quantile varying coefficient models (doi:10.1016/j.csda.2023.107808). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++.

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