Package: bbqr
Type: Package
Title: Bayesian Quantile Regression with Lasso and Adaptive Lasso
Version: 0.1.0
Authors@R: c(
    person("Fernando", "Rubio Garcia", email = "j332v755@wichita.edu",
           role = c("aut", "cre"),
           comment = "Wichita State University"),
    person("Dries F.", "Benoit", role = c("ctb", "cph"),
           comment = "Author of 'bayesQR', from which the Fortran RNG wrapper and package layout are derived"),
    person("Rahim", "Al-Hamzawi", role = "ctb"),
    person("Keming", "Yu", role = "ctb"),
    person("Dirk", "Van den Poel", role = "ctb"))
Description: 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)
    <doi: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)
    <doi:10.1002/jae.1216>; the Bayesian lasso, following Benoit, Al-Hamzawi
    and Yu (2013) <doi: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)
    <doi: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.
License: GPL (>= 2)
Copyright: See inst/COPYRIGHTS for the attribution of third-party
        components inherited from the 'bayesQR' package.
Encoding: UTF-8
Depends: R (>= 4.2)
Imports: graphics, stats, utils
Suggests: testthat (>= 3.0.0), coda, knitr, rmarkdown, quantreg
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: yes
RoxygenNote: 7.3.3
URL: https://github.com/fernandorubiogarcia/bbqr
BugReports: https://github.com/fernandorubiogarcia/bbqr/issues
Packaged: 2026-08-25 20:39:30 UTC; ferna
Author: Fernando Rubio Garcia [aut, cre] (Wichita State University),
  Dries F. Benoit [ctb, cph] (Author of 'bayesQR', from which the Fortran
    RNG wrapper and package layout are derived),
  Rahim Al-Hamzawi [ctb],
  Keming Yu [ctb],
  Dirk Van den Poel [ctb]
Maintainer: Fernando Rubio Garcia <j332v755@wichita.edu>
Repository: CRAN
Date/Publication: 2026-09-08 13:00:13 UTC
Built: R 4.6.1; x86_64-apple-darwin20; 2026-09-08 14:07:29 UTC; unix
Archs: bbqr.so.dSYM
