Package: pqrBayes
Type: Package
Title: Bayesian Penalized Quantile Regression
Version: 1.2.2
Date: 2026-04-29
Authors@R: c( 
              person("Kun", "Fan", role = "aut"),
              person("Cen", "Wu", role = c("aut", "cre"),email = "wucen@ksu.edu"),
              person("Jie", "Ren", role = "aut"),
              person("Xiaoxi", "Li", role = "aut"),
              person("Fei", "Zhou", role = "aut"))
Description: Bayesian regularized quantile regression utilizing two
        major classes of shrinkage priors (the spike-and-slab priors
        and the horseshoe family of priors) leads to efficient Bayesian
        shrinkage estimation, variable selection and valid statistical
        inference. In this package, we have implemented robust Bayesian
        variable selection with spike-and-slab priors under
        high-dimensional linear regression models (Fan et al. (2024)
        <doi:10.3390/e26090794> and Ren et al. (2023)
        <doi:10.1111/biom.13670>), and regularized quantile varying
        coefficient models (Zhou et al.(2023)
        <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.
        Additional models with spike-and-slab priors include robust
        Bayesian group LASSO and robust binary Bayesian LASSO (Fan and
        Wu (2025) <doi:10.1002/sta4.70078>). Besides, robust sparse
        Bayesian regression with the horseshoe family of (horseshoe,
        horseshoe+ and regularized horseshoe) priors has also been
        implemented and yielded valid inference results under
        heavy-tailed model errors (Fan et al.(2026)
        <doi:10.1016/j.csda.2026.108358>). The Markov chain Monte Carlo
        (MCMC) algorithms of the proposed and alternative models are
        implemented in C++.
Depends: R (>= 3.5.0)
License: GPL-2
Encoding: UTF-8
URL: https://github.com/cenwu/pqrBayes
BugReports: https://github.com/cenwu/pqrBayes/issues
LazyData: true
Imports: Rcpp,glmnet,splines, stats
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.3
NeedsCompilation: yes
Repository: https://cenwu.r-universe.dev
Date/Publication: 2026-04-30 03:46:04 UTC
RemoteUrl: https://github.com/cenwu/pqrbayes
RemoteRef: HEAD
RemoteSha: 4ced7491efb82005e9599968772b4dba8deef859
Packaged: 2026-07-02 07:42:08 UTC; root
Author: Kun Fan [aut],
  Cen Wu [aut, cre],
  Jie Ren [aut],
  Xiaoxi Li [aut],
  Fei Zhou [aut]
Maintainer: Cen Wu <wucen@ksu.edu>