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

Documentation:

Reference manual:

bayesCureRateModel.pdf

Downloads:

Package source:	bayesCureRateModel_1.0.tar.gz
Windows binaries:	r-devel: bayesCureRateModel_1.0.zip, r-release: bayesCureRateModel_1.0.zip, r-oldrel: bayesCureRateModel_1.0.zip
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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