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 ORCID iD [aut, cre], Fotios Milienos ORCID iD [aut]
Maintainer: Panagiotis Papastamoulis <papapast at>
License: GPL-2
NeedsCompilation: yes
CRAN checks: bayesCureRateModel results


Reference manual: bayesCureRateModel.pdf


Package source: bayesCureRateModel_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bayesCureRateModel_1.0.tgz, r-oldrel (arm64): bayesCureRateModel_1.0.tgz, r-release (x86_64): bayesCureRateModel_1.0.tgz, r-oldrel (x86_64): bayesCureRateModel_1.0.tgz


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