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BDPTobitQR  

Bayesian Double-Penalty Tobit Quantile Regression for Longitudinal Interval-Censored Data
View on CRAN: Click here


Download and install BDPTobitQR package within the R console
Install from CRAN:
install.packages("BDPTobitQR")

Install from Github:
library("remotes")
install_github("cran/BDPTobitQR")

Install by package version:
library("remotes")
install_version("BDPTobitQR", "0.1.0")



Attach the package and use:
library("BDPTobitQR")
Maintained by
Shikhar Tyagi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-06
Latest Update: 2026-08-06
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>.
How to cite:
Shikhar Tyagi (2026). BDPTobitQR: Bayesian Double-Penalty Tobit Quantile Regression for Longitudinal Interval-Censored Data. R package version 0.1.0, https://cran.r-project.org/web/packages/BDPTobitQR. Accessed 24 Oct. 2026.
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