pqrBayes: Bayesian Penalized Quantile Regression
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.(2025)
<doi:10.48550/arXiv.2507.10975>). The Markov chain Monte Carlo (MCMC) algorithms of
the proposed and alternative models are implemented in C++.
| Version: |
1.2.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp, glmnet, splines, stats |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2025-12-08 |
| DOI: |
10.32614/CRAN.package.pqrBayes |
| Author: |
Kun Fan [aut],
Cen Wu [aut, cre],
Jie Ren [aut],
Xiaoxi Li [aut],
Fei Zhou [aut] |
| Maintainer: |
Cen Wu <wucen at ksu.edu> |
| BugReports: |
https://github.com/cenwu/pqrBayes/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/cenwu/pqrBayes |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
pqrBayes results |
Documentation:
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