Bayesian regularized quantile regression utilizing sparse priors to promote 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 with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) <doi:10.1002/sta4.70078>). The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++.
Version: 1.1.4 Depends: R (≥ 3.5.0) Imports: Rcpp, glmnet, splines, stats LinkingTo: Rcpp, RcppArmadillo Published: 2025-07-25 DOI: 10.32614/CRAN.package.pqrBayes Author: Kun Fan [aut], Cen Wu [aut, cre], Jie Ren [aut], Xiaoxi Li [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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=pqrBayes to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4