AGBQR
Adaptive Generalized Bayesian Quantile Regression
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | AGBQR_0.1.0.tar.gz |
22.1 KiB |
0.1.0 |
rolling linux/noble R-4.5 | AGBQR_0.1.0.tar.gz |
22.0 KiB |
0.1.0 |
rolling source/ R- | AGBQR_0.1.0.tar.gz |
5.3 KiB |
0.1.0 |
latest linux/jammy R-4.5 | AGBQR_0.1.0.tar.gz |
22.1 KiB |
0.1.0 |
latest linux/noble R-4.5 | AGBQR_0.1.0.tar.gz |
22.0 KiB |
0.1.0 |
latest source/ R- | AGBQR_0.1.0.tar.gz |
5.3 KiB |
0.1.0 |
2026-04-23 source/ R- | AGBQR_0.1.0.tar.gz |
0 B |