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: 1.4 Depends: R (≥ 3.5.0) Imports: Rcpp (≥ 1.0.12), survival, doParallel, parallel, foreach, mclust, coda, HDInterval, VGAM, calculus, flexsurv LinkingTo: Rcpp, RcppArmadillo Published: 2025-06-18 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bayesCureRateModel to link to this page.
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