pqrBayes: Bayesian Penalized Quantile Regression

The quantile varying coefficient model is robust to data heterogeneity, outliers and heavy-tailed distributions in the response variable. In addition, it can flexibly model dynamic patterns of regression coefficients through nonparametric varying coefficient functions. In this package, we have implemented the Gibbs samplers of the penalized Bayesian quantile varying coefficient model with spike-and-slab priors [Zhou et al.(2023)]<doi:10.1016/j.csda.2023.107808> for efficient Bayesian shrinkage estimation, variable selection and statistical inference. In particular, valid Bayesian inferences on sparse quantile varying coefficient functions can be validated on finite samples. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models can be efficiently performed by using the package.

Version:	1.0.3
Depends:	R (≥ 3.5.0)
Imports:	Rcpp, glmnet
LinkingTo:	Rcpp, RcppArmadillo
Published:	2024-12-21
DOI:	10.32614/CRAN.package.pqrBayes
Author:	Cen Wu [aut, cre], Kun Fan [aut], 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
CRAN checks:	pqrBayes results

Documentation:

Reference manual:

pqrBayes.pdf

Downloads:

Package source:	pqrBayes_1.0.3.tar.gz
Windows binaries:	r-devel: pqrBayes_1.0.3.zip, r-release: pqrBayes_1.0.2.zip, r-oldrel: pqrBayes_1.0.2.zip
macOS binaries:	r-release (arm64): pqrBayes_1.0.3.tgz, r-oldrel (arm64): pqrBayes_1.0.3.tgz, r-release (x86_64): pqrBayes_1.0.3.tgz, r-oldrel (x86_64): pqrBayes_1.0.3.tgz
Old sources:	pqrBayes archive

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