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bayesCureRateModel
View on CRAN: Click
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Download and install bayesCureRateModel package within the R console
Install from CRAN:
install.packages("bayesCureRateModel")
Install from Github:
library("remotes")
install_github("cran/bayesCureRateModel")
Install by package version:
library("remotes")
install_version("bayesCureRateModel", "1.3")
Attach the package and use:
library("bayesCureRateModel")
Maintained by
Panagiotis Papastamoulis
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[Scholar Profile | Author Map]
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First Published: 2024-06-27
Latest Update: 2024-06-27
Description:
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.
How to cite:
Panagiotis Papastamoulis (2024). bayesCureRateModel: Bayesian Cure Rate Modeling for Time-to-Event Data. R package version 1.3, https://cran.r-project.org/web/packages/bayesCureRateModel. Accessed 21 Nov. 2024.
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