Package: BDPTobitQR
Title: Bayesian Double-Penalty Tobit Quantile Regression for
        Longitudinal Interval-Censored Data
Version: 0.1.0
Authors@R: c(
    person("Shikhar", "Tyagi", email = "shikhar1093tyagi@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1606-0844")),
    person("Arvind", "Pandey", email = "arvindmzu@gmail.com", role = "aut"),
    person("Bhupendra", "Singh", email = "bhupendra.rana@gmail.com", role = "aut"),
    person("Vrijesh", "Tripathi", email = "vrijesh.tripathi@uwi.edu", role = "aut")
  )
Author: Shikhar Tyagi [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-1606-0844>),
  Arvind Pandey [aut],
  Bhupendra Singh [aut],
  Vrijesh Tripathi [aut]
Maintainer: Shikhar Tyagi <shikhar1093tyagi@gmail.com>
Description: Implements Bayesian Double-Penalty Tobit Quantile Regression
  methods for longitudinal interval-censored data as proposed by Zhao
  et al. (2024) <doi:10.3390/math12121782>. Supports Bayesian Tobit quantile
  regression with double adaptive Lasso penalty ('PDAL-BTQR'), double
  Lasso penalty ('PDL-BTQR'), and unpenalized mixed-effects ('P-BTQR').
  Handles left, right, interval, and bilateral censoring schemes in
  longitudinal and clustered structures. Includes Gibbs sampling algorithms,
  parameter estimation, standard error computation, posterior credible
  intervals, forecast predictions, DIC, LPML, and diagnostic plotting.
  References: Tobin (1958) <doi:10.2307/1907382>; Koenker and Bassett (1978)
  <doi:10.2307/1913643>; Zou (2006) <doi:10.1198/016214506000000735>;
  Alhamzawi and Yu (2012) <doi:10.1016/j.csda.2011.11.018>; Zhao et al.
  (2024) <doi:10.3390/math12121782>.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.0.0)
Imports: stats, graphics, grDevices
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.3.1
NeedsCompilation: no
Packaged: 2026-07-28 02:54:12 UTC; shikhar tyagi
Repository: CRAN
Date/Publication: 2026-08-06 07:00:08 UTC
Built: R 4.5.2; ; 2026-08-06 09:02:20 UTC; unix
