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# CITATION.cff 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 "bbqr" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'bbqr: Bayesian Quantile Regression with Lasso and Adaptive Lasso'
version: 0.1.0.9000
identifiers:
- type: doi
  value: 10.32614/CRAN.package.bbqr
abstract: 'Markov chain Monte Carlo samplers for Bayesian quantile regression, based
  on the asymmetric Laplace distribution and the location-scale mixture representation
  of Kozumi and Kobayashi (2011) <https://doi.org/10.1080/00949655.2010.496117>. A
  binary response and an observed continuous response are both supported, each with
  three penalty layers behind one interface: no penalty, following Benoit and Van
  den Poel (2012) <https://doi.org/10.1002/jae.1216>; the Bayesian lasso, following
  Benoit, Al-Hamzawi and Yu (2013) <https://doi.org/10.1007/s00180-013-0439-0>; and
  the Bayesian adaptive lasso of Rubio Garcia (2023) <https://soar.wichita.edu/entities/publication/a2f86232-4704-4ec2-b685-751e7b04ec42>.
  In the binary family each is available as published and in a corrected form, the
  default, in which every improper prior component is replaced by a proper one so
  that the posterior exists unconditionally; the continuous family ships the corrected
  form only. The continuous adaptive-lasso layer at its default reproduces the penalty
  of Alhamzawi, Yu and Benoit (2012) <https://doi.org/10.1177/1471082X1101200304>.
  A binary threshold model identifies the coefficient vector only up to a positive
  scale, so the binary samplers expose the identification anchor as an explicit argument,
  allowing fixing the scale of the error distribution, fixing a single coefficient,
  and constraining the norm of the coefficient vector to be compared directly; an
  observed response identifies the scale, so the continuous samplers have no anchor
  and draw it every sweep. The MCMC cores are written in Fortran and called from R.'
authors:
- family-names: Rubio Garcia
  given-names: Fernando
  email: j332v755@wichita.edu
preferred-citation:
  type: manual
  title: 'bbqr: Bayesian Quantile Regression with Lasso and Adaptive Lasso'
  authors:
  - family-names: Rubio Garcia
    given-names: Fernando
    email: j332v755@wichita.edu
  year: '2026'
  notes: R package version 0.1.0
  url: https://github.com/fernandorubiogarcia/bbqr
repository: https://fernandorubiogarcia.r-universe.dev
repository-code: https://github.com/fernandorubiogarcia/bbqr
commit: 41bb0f86318157a335be1ff1e8a2bdbca6b41b24
url: https://github.com/fernandorubiogarcia/bbqr
date-released: '2026-09-15'
contact:
- family-names: Rubio Garcia
  given-names: Fernando
  email: j332v755@wichita.edu
references:
- type: thesis
  title: Bayesian adaptive lasso binary quantile regression with hybrid resampling
    for classification of imbalanced data
  authors:
  - family-names: Rubio Garcia
    given-names: Fernando
  institution:
    name: Wichita State University, Dept. of Mathematics, Statistics, and Physics
  year: '2023'
  url: https://soar.wichita.edu/entities/publication/a2f86232-4704-4ec2-b685-751e7b04ec42
  thesis-type: Master's Thesis
- type: article
  title: Bayesian adaptive Lasso quantile regression
  authors:
  - family-names: Alhamzawi
    given-names: Rahim
  - family-names: Yu
    given-names: Keming
  - family-names: Benoit
    given-names: Dries F.
  journal: Statistical Modelling
  year: '2012'
  volume: '12'
  issue: '3'
  doi: 10.1177/1471082X1101200304
  start: '279'
  end: '297'