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  "Title": "Semiparametric Bayesian Regression for Dependent Current Status\nData",
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  "Description": "Implements a semiparametric Bayesian regression framework\nusing Bernstein polynomial baseline models for analyzing\ndependent current status data. The package accommodates\nproportional hazards (PH) and proportional odds (PO) regression\nmodels with Archimedean copulas ('Gumbel', 'Frank', and\n'Clayton') to model the joint dependence structure between\nevent and observation or censoring times. Estimation is\nperformed using a Robust Adaptive Metropolis (RAM) Markov Chain\nMonte Carlo ('MCMC') algorithm. Model comparison metrics\nincluding Deviance Information Criterion ('DIC') and posterior\nsummaries with Highest Posterior Density ('HPD') intervals and\nKendall's tau are provided. Methodological details are\ndescribed in Sharma and Balakrishnan (2026)\n<doi:10.1080/02664763.2026.2701921>.",
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