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pqrBayes  

Bayesian Penalized Quantile Regression
View on CRAN: Click here


Download and install pqrBayes package within the R console
Install from CRAN:
install.packages("pqrBayes")

Install from Github:
library("remotes")
install_github("cran/pqrBayes")

Install by package version:
library("remotes")
install_version("pqrBayes", "1.0.2")



Attach the package and use:
library("pqrBayes")
Maintained by
Cen Wu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-14
Latest Update: 2023-09-14
Description:
The quantile varying coefficient model is robust to data heterogeneity, outliers and heavy-tailed distributions in the response variable due to the check loss function in quantile regression. In addition, it can flexibly model the dynamic pattern of regression coefficients through nonparametric varying coefficient functions. Although high dimensional quantile varying coefficient model has been examined extensively in the frequentist framework, the corresponding Bayesian variable selection methods have rarely been developed. In this package, we have implemented the Gibbs samplers of the penalized Bayesian quantile varying coefficient model with the spike-and-slab priors [Zhou et al.(2023)]. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models can be efficiently performed by using the package.
How to cite:
Cen Wu (2023). pqrBayes: Bayesian Penalized Quantile Regression. R package version 1.0.2, https://cran.r-project.org/web/packages/pqrBayes. Accessed 07 Nov. 2024.
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Complete documentation for pqrBayes
Functions, R codes and Examples using the pqrBayes R package
Some associated functions: data . pqrBayes-package . pqrBayes . predict.pqrBayes . print.VCselect . print.pqrBayes . print.pqrBayes.pred . select.VC . 
Some associated R codes: RcppExports.R . data.R . pqrBayes-package.R . pqrBayes.R . predict.pqrBayes.R . print.pqrBayes.R . select.VC.R .  Full pqrBayes package functions and examples
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