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>.
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[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut]