bayesCureRateModel: Bayesian Cure Rate Modeling for Time-to-Event Data
A fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) <doi:10.1007/s11749-024-00942-w> and Papastamoulis and Milienos (2024b) <doi:10.48550/arXiv.2409.10221>. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.
| Version: | 1.5 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 1.0.12),survival,doParallel, parallel,foreach,mclust,coda,HDInterval,VGAM,calculus,flexsurv |
| LinkingTo: | Rcpp,RcppArmadillo |
| Published: | 2025-10-31 |
| DOI: | 10.32614/CRAN.package.bayesCureRateModel |
| Author: | Panagiotis Papastamoulis [aut, cre], Fotios Milienos [aut] |
| Maintainer: | Panagiotis Papastamoulis <papapast at yahoo.gr> |
| License: | GPL-2 |
| URL: | https://github.com/mqbssppe/Bayesian_cure_rate_model |
| NeedsCompilation: | yes |
| Citation: | bayesCureRateModel citation info |
| CRAN checks: | bayesCureRateModel results |
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