Bayesian Double-Penalty Tobit Quantile Regression for Longitudinal Interval-Censored Data
Overview
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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Health
- OK13 OKNow
- OK1 OK2026-08-06
Documentation
- Help pages
- 4includes a package overview page
- Examples that run
- 67%2 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 82%
- Return-value docs
- 100%
- Help pages citing references
- 100%
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Installing BDPTobitQR adds no packages beyond those that come with R.
Code & Tests
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People & History
- 0.1.0Latestfirst seen 2026-08-06 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First seen
- 2026-08-06
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 7.7 KB / 1 file
- Download size
- 25 KB
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