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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>. 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: | 1.3 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp (≥ 1.0.12), survival, doParallel, parallel, foreach, mclust, coda, HDInterval, VGAM, calculus, flexsurv |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2024-10-03 |
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 |
Reference manual: | bayesCureRateModel.pdf |
Package source: | bayesCureRateModel_1.3.tar.gz |
Windows binaries: | r-devel: bayesCureRateModel_1.3.zip, r-release: bayesCureRateModel_1.3.zip, r-oldrel: bayesCureRateModel_1.3.zip |
macOS binaries: | r-release (arm64): bayesCureRateModel_1.3.tgz, r-oldrel (arm64): bayesCureRateModel_1.3.tgz, r-release (x86_64): bayesCureRateModel_1.3.tgz, r-oldrel (x86_64): bayesCureRateModel_1.3.tgz |
Old sources: | bayesCureRateModel archive |
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