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pqrBayes: Bayesian Penalized Quantile Regression

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 (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. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++.

Version: 1.1.0
Depends: R (≥ 3.5.0)
Imports: Rcpp, glmnet, splines, stats
LinkingTo: Rcpp, RcppArmadillo
Published: 2025-02-17
DOI: 10.32614/CRAN.package.pqrBayes
Author: Kun Fan [aut], Cen Wu [aut, cre], Jie Ren [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:

Reference manual: pqrBayes.pdf

Downloads:

Package source: pqrBayes_1.1.0.tar.gz
Windows binaries: r-devel: pqrBayes_1.0.5.zip, r-release: pqrBayes_1.1.0.zip, r-oldrel: pqrBayes_1.1.0.zip
macOS binaries: r-devel (arm64): pqrBayes_1.0.5.tgz, r-release (arm64): pqrBayes_1.0.5.tgz, r-oldrel (arm64): pqrBayes_1.0.5.tgz, r-devel (x86_64): pqrBayes_1.1.0.tgz, r-release (x86_64): pqrBayes_1.1.0.tgz, r-oldrel (x86_64): pqrBayes_1.1.0.tgz
Old sources: pqrBayes archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.