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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.
| Version: | 0.1.0 |
| Imports: | quantreg, MASS, stats |
| Suggests: | testthat |
| Published: | 2026-06-22 |
| DOI: | 10.32614/CRAN.package.AGBQR |
| Author: | Khder Alakkari [aut, cre] |
| Maintainer: | Khder Alakkari <khderalakkari1990 at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Citation: | AGBQR citation info |
| CRAN checks: | AGBQR results |
| Reference manual: | AGBQR.html , AGBQR.pdf |
| Package source: | AGBQR_0.1.0.tar.gz |
| Windows binaries: | r-devel: AGBQR_0.1.0.zip, r-release: not available, r-oldrel: AGBQR_0.1.0.zip |
| macOS binaries: | r-release (arm64): AGBQR_0.1.0.tgz, r-oldrel (arm64): AGBQR_0.1.0.tgz, r-release (x86_64): AGBQR_0.1.0.tgz, r-oldrel (x86_64): AGBQR_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.