Package: bayesCureRateModel 1.3
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:
bayesCureRateModel_1.3.tar.gz
bayesCureRateModel_1.3.tar.gz(r-4.5-noble)bayesCureRateModel_1.3.tar.gz(r-4.4-noble)
bayesCureRateModel_1.3.tgz(r-4.4-emscripten)bayesCureRateModel_1.3.tgz(r-4.3-emscripten)
bayesCureRateModel.pdf |bayesCureRateModel.html✨
bayesCureRateModel/json (API)
# Install 'bayesCureRateModel' in R: |
install.packages('bayesCureRateModel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mqbssppe/bayesian_cure_rate_model/issues
- marriage_dataset - Marriage data
- sim_mix_data - Simulated dataset
Last updated 2 months agofrom:04a79a8260. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 03 2024 |
R-4.5-linux-x86_64 | OK | Dec 03 2024 |
Exports:complete_log_likelihood_generalcompute_fdr_tprcure_rate_MC3cure_rate_mcmclog_dagumlog_gammalog_gamma_mixturelog_gompertzlog_logLogisticlog_lomaxlog_user_mixturelog_weibulllogLik.bayesCureModelplot.bayesCureModelplot.predict_bayesCureModelpredict.bayesCureModelprint.bayesCureModelprint.predict_bayesCureModelprint.summary_bayesCureModelresiduals.bayesCureModelsummary.bayesCureModelsummary.predict_bayesCureModel
Dependencies:assertthatbbmlebdsmatrixBHcalculusclicodacodetoolscolorspacecpp11data.tabledeSolvedoParalleldplyrfansifarverfastGHQuadflexsurvforeachgenericsggplot2gluegtableHDIntervalisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmclustmgcvmstatemuhazmunsellmvtnormnlmenumDerivpillarpkgconfigpurrrquadprogR6RColorBrewerRcppRcppArmadillorlangrstpm2scalesstatmodstringistringrsurvivaltibbletidyrtidyselectutf8vctrsVGAMviridisLitewithr