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 (2023) <doi:10.48550/arXiv.2310.06926>. 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 Gamma distributions. 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: |
1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp (≥ 1.0.12), doParallel, foreach, mclust, coda, HDInterval, VGAM, calculus, flexsurv |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2024-06-27 |
| 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 |
| CRAN checks: |
bayesCureRateModel results |
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