BDPTobitQR: Bayesian Double-Penalty Tobit Quantile Regression for Longitudinal Interval-Censored Data

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>.

Version:	0.1.0
Depends:	R (≥ 4.0.0)
Imports:	stats, graphics, grDevices
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-08-06
DOI:	10.32614/CRAN.package.BDPTobitQR (may not be active yet)
Author:	Shikhar Tyagi [aut, cre], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]
Maintainer:	Shikhar Tyagi <shikhar1093tyagi at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	BDPTobitQR results

Documentation:

Reference manual:	BDPTobitQR.html , BDPTobitQR.pdf
Vignettes:	Bayesian Double-Penalty Tobit Quantile Regression for Longitudinal Interval-Censored Data (source, R code)

Downloads:

Package source:	BDPTobitQR_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): BDPTobitQR_0.1.0.tgz, r-oldrel (arm64): BDPTobitQR_0.1.0.tgz, r-release (x86_64): BDPTobitQR_0.1.0.tgz, r-oldrel (x86_64): not available

Linking:

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