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pqrBayes
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]
[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 21 Nov. 2024.
Previous versions and publish date:
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imports, suggests or enhances
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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