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.

How to install

• To install from GitHub, run these two lines of code in R

install.packages("devtools")
devtools::install_github("cenwu/pqrBayes")

News

pqrBayes 1.0.3 [2024-12-21]

• Fixed the issue of no output in the VCselect() function and added examples.
• Updated the list of pqrBayes output objects.
• Added non-robust sparse Bayesian varying coefficient models and corresponding examples.
• Updated the documentation.

Methods

This package provides implementation for methods proposed in

• Zhou, F., Ren, J., Ma, S. and Wu, C. (2023). The Bayesian Regularized Quantile Varying Coefficient Model. {, 107808}