Package: pqrBayes
Type: Package
Title: Bayesian Penalized Quantile Regression
Version: 1.1.3
Date: 2025-05-14
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("Xiaoxi", "Li", role = "aut"),
              person("Fei", "Zhou", role = "aut"))
Description: 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. Additional models including robust Bayesian 
    group LASSO and robust Bayesian binary LASSO are also included. The Markov Chain Monte Carlo (MCMC) algorithms 
    of the proposed and alternative models are implemented in C++. 
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: 2025-05-15 03:06:57 UTC; cenwu
Author: Kun Fan [aut],
  Cen Wu [aut, cre],
  Jie Ren [aut],
  Xiaoxi Li [aut],
  Fei Zhou [aut]
Maintainer: Cen Wu <wucen@ksu.edu>
Date/Publication: 2025-05-15 05:00:02 UTC
Built: R 4.4.1; aarch64-apple-darwin20; 2025-05-15 05:59:40 UTC; unix
Archs: pqrBayes.so.dSYM
