pqrBayes: Bayesian Penalized Quantile Regression
Bayesian regularized quantile regression utilizing sparse priors to
promote 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 (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>). The Markov Chain Monte Carlo (MCMC) algorithms
of the proposed and alternative models are implemented in C++.
| Version: |
1.1.4 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp, glmnet, splines, stats |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2025-07-25 |
| 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:
Downloads:
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