DESCRIPTION

Package: pqrBayes
Type: Package
Title: Bayesian Penalized Quantile Regression
Version: 1.0.4
Date: 2025-01-24
Authors@R: c( 
              person("Kun", "Fan", role = "aut"),
              person("Cen", "Wu", role = c("aut", "cre"),email = "wucen@ksu.edu"),
              person("Jie", "Ren", role = "aut"),
              person("Fei", "Zhou", role = "aut"))
Description: 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.   
Depends: R (>= 3.5.0)
License: GPL-2
Encoding: UTF-8
URL: https://github.com/cenwu/pqrBayes
BugReports: https://github.com/cenwu/pqrBayes/issues
LazyData: true
Imports: Rcpp,glmnet,splines, stats
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.2
NeedsCompilation: yes
Repository: CRAN
Packaged: 2025-01-24 23:47:43 UTC; cenwu
Author: Kun Fan [aut],
  Cen Wu [aut, cre],
  Jie Ren [aut],
  Fei Zhou [aut]
Maintainer: Cen Wu <wucen@ksu.edu>
Date/Publication: 2025-01-25 00:00:02 UTC