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  "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)",
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  "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<doi:10.1007/s11749-024-00942-w> and Papastamoulis and Milienos\n(2024b) <doi:10.48550/arXiv.2409.10221>. 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. The main\nMCMC algorithm is embedded within a parallel tempering scheme\nby considering heated versions of the target posterior\ndistribution.",
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        "bayesCureRateModel"
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