Package: bayesCureRateModel 1.2

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 (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.

Authors:Panagiotis Papastamoulis [aut, cre], Fotios Milienos [aut]

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Peer review:

Bug tracker:https://github.com/mqbssppe/bayesian_cure_rate_model/issues

On CRAN:

17 exports 0.36 score 63 dependencies 250 downloads

Last updated 3 days agofrom:769c5623ec. Checks:OK: 9. Indexed: yes.

Exports:complete_log_likelihood_generalcompute_fdr_tprcure_rate_MC3cure_rate_mcmclog_dagumlog_gammalog_gamma_mixturelog_gompertzlog_logLogisticlog_lomaxlog_user_mixturelog_weibullplot.bayesCureModelpredict.bayesCureModelprint.bayesCureModelresiduals.bayesCureModelsummary.bayesCureModel

Dependencies:assertthatbbmlebdsmatrixBHcalculusclicodacodetoolscolorspacecpp11data.tabledeSolvedoParalleldplyrfansifarverfastGHQuadflexsurvforeachgenericsggplot2gluegtableHDIntervalisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmclustmgcvmstatemuhazmunsellmvtnormnlmenumDerivpillarpkgconfigpurrrquadprogR6RColorBrewerRcppRcppArmadillorlangrstpm2scalesstatmodstringistringrsurvivaltibbletidyrtidyselectutf8vctrsVGAMviridisLitewithr