Package: bbqr
Type: Package
Title: Bayesian Quantile Regression with Lasso and Adaptive Lasso
Version: 0.2.0
Authors@R: c(
    person("Fernando", "Rubio Garcia", email = "j332v755@wichita.edu",
           role = c("aut", "cre"),
           comment = "Wichita State University"),
    person("Mai", "Dao", role = "ths",
           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), knitr, rmarkdown, quantreg, lpSolve
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: 2026-10-10 07:25:16 UTC; root
Author: Fernando Rubio Garcia [aut, cre] (Wichita State University),
        Mai Dao [ths] (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: https://cran.r-universe.dev
Date/Publication: 2026-10-10 00:10:09 UTC
RemoteUrl: https://github.com/cran/bbqr
RemoteRef: HEAD
RemoteSha: 7d651ea7141c746c04298ab3291e0acb54c6709f