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) . 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) ; Koenker and Bassett (1978) ; Zou (2006) ; Alhamzawi and Yu (2012) ; Zhao et al. (2024) .