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AGBQR  

Adaptive Generalized Bayesian Quantile Regression
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("AGBQR", "0.1.0")



Attach the package and use:
library("AGBQR")
Maintained by
Khder Alakkari
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-22
Latest Update: 2026-06-22
Description:
Implements adaptive generalized Bayesian quantile regression with quantile-specific learning rates, HAC-based calibration, Gibbs posterior simulation, posterior summaries, predictive evaluation, and visualization tools. The package builds on the generalized Bayesian composite quantile regression framework of Hardy and Korobilis (2026) <doi:10.2139/ssrn.6618603> by allowing learning rates to vary across quantile levels. The implementation is designed for empirical work with small and moderate time-series samples where posterior calibration and tail-specific inference are important.
How to cite:
Khder Alakkari (2026). AGBQR: Adaptive Generalized Bayesian Quantile Regression. R package version 0.1.0, https://cran.r-project.org/web/packages/AGBQR. Accessed 14 Jul. 2026.
Previous versions and publish date:
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Complete documentation for AGBQR
Functions, R codes and Examples using the AGBQR R package
Full AGBQR package functions and examples
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