Bayesian Cure Rate Modeling for Time-to-Event Data R package bayesCureRateModel version 1.6 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. Software Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.12), survival, doParallel, parallel, foreach, mclust, coda, HDInterval, VGAM, calculus, flexsurv LinkingTo: Rcpp, RcppArmadillo Panagiotis Papastamoulis <papapast@yahoo.gr> Comprehensive R Archive Network (CRAN) Panagiotis Papastamoulis [aut, cre] (ORCID: <https://orcid.org/0000-0001-9468-7613>), Fotios Milienos [aut] (ORCID: <https://orcid.org/0000-0003-1423-7132>) GPL-2 2026-01-19 application/tgz https://CRAN.R-project.org/package=bayesCureRateModel doi:10.32614/CRAN.package.bayesCureRateModel