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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "AGBQR" in publications use:'
type: software
license: MIT
title: 'AGBQR: Adaptive Generalized Bayesian Quantile Regression'
version: 0.1.0
abstract: 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) <https://doi.org/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.
authors:
- family-names: Alakkari
  given-names: Khder
  email: khderalakkari1990@gmail.com
preferred-citation:
  type: manual
  title: 'AGBQR: Adaptive Generalized Bayesian Quantile Regression'
  authors:
  - family-names: Alakkari
    given-names: Khder
    email: khderalakkari1990@gmail.com
  year: '2026'
  notes: R package version 0.1.0
repository: https://cran.r-universe.dev
commit: e8b489a8ecc49244c85651a05631ac66ddcaf9e4
date-released: '2026-06-16'
contact:
- family-names: Alakkari
  given-names: Khder
  email: khderalakkari1990@gmail.com
references:
- type: article
  title: Generalized Bayesian Composite Quantile Regression with an Application to
    Equity Premium Forecasting
  authors:
  - family-names: Hardy
    given-names: Nicolas
  - family-names: Korobilis
    given-names: Dimitris
  journal: SSRN Electronic Journal
  year: '2026'
  doi: 10.2139/ssrn.6618603