pqrBayes: Bayesian Penalized Quantile Regression

The quantile varying coefficient model is robust to data heterogeneity, outliers and heavy-tailed distributions in the response variable due to the check loss function in quantile regression. In addition, it can flexibly model the dynamic pattern of regression coefficients through nonparametric varying coefficient functions. Although high dimensional quantile varying coefficient model has been examined extensively in the frequentist framework, the corresponding Bayesian variable selection methods have rarely been developed. In this package, we have implemented the Gibbs samplers of the penalized Bayesian quantile varying coefficient model with the spike-and-slab priors [Zhou et al.(2023)]<doi:10.1016/j.csda.2023.107808>. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models can be efficiently performed by using the package.

Version: 1.0.2
Depends: R (≥ 3.5.0)
Imports: Rcpp, glmnet
LinkingTo: Rcpp, RcppArmadillo
Published: 2023-09-14
Author: Cen Wu [aut, cre], Fei Zhou [aut], Jie Ren [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
CRAN checks: pqrBayes results

Documentation:

Reference manual: pqrBayes.pdf

Downloads:

Package source: pqrBayes_1.0.2.tar.gz
Windows binaries: r-devel: pqrBayes_1.0.2.zip, r-release: pqrBayes_1.0.2.zip, r-oldrel: pqrBayes_1.0.2.zip
macOS binaries: r-release (arm64): pqrBayes_1.0.2.tgz, r-oldrel (arm64): pqrBayes_1.0.2.tgz, r-release (x86_64): pqrBayes_1.0.2.tgz

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