{ "_id": "6a33ae5b3efcd9bda43ac849", "Package": "bayesCureRateModel", "Type": "Package", "Title": "Bayesian Cure Rate Modeling for Time-to-Event Data", "Version": "1.6", "Date": "2026-01-19", "Authors@R": "c(person(given = \"Panagiotis\",\nfamily = \"Papastamoulis\",\nemail = \"papapast@yahoo.gr\",\nrole = c( \"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9468-7613\")),\nperson(given = \"Fotios\",\nfamily = \"Milienos\",\nemail = \"milienos@panteion.gr\",\nrole = c( \"aut\"),\ncomment = c(ORCID = \"0000-0003-1423-7132\"))\n)", "Maintainer": "Panagiotis Papastamoulis ", "Description": "A fully Bayesian approach in order to estimate a general\nfamily of cure rate models under the presence of covariates,\nsee Papastamoulis and Milienos (2024)\n and Papastamoulis and Milienos\n(2024b) . The promotion time can\nbe modelled (a) parametrically using typical distributional\nassumptions for time to event data (including the Weibull,\nExponential, Gompertz, log-Logistic distributions), or (b)\nsemiparametrically using finite mixtures of distributions. In\nboth cases, user-defined families of distributions are allowed\nunder some specific requirements. Posterior inference is\ncarried out by constructing a Metropolis-coupled Markov chain\nMonte Carlo (MCMC) sampler, which combines Gibbs sampling for\nthe latent cure indicators and Metropolis-Hastings steps with\nLangevin diffusion dynamics for parameter updates. 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