Package: bayesCureRateModel 1.0
Panagiotis Papastamoulis
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.
Authors:
bayesCureRateModel_1.0.tar.gz
bayesCureRateModel_1.0.tar.gz(r-4.5-noble)bayesCureRateModel_1.0.tar.gz(r-4.4-noble)
bayesCureRateModel_1.0.tgz(r-4.4-emscripten)bayesCureRateModel_1.0.tgz(r-4.3-emscripten)
bayesCureRateModel.pdf |bayesCureRateModel.html✨
bayesCureRateModel/json (API)
| # InstallbayesCureRateModel 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
Last updated 8 days agofrom:0a4046c993
Exports:complete_log_likelihood_generalcure_rate_MC3cure_rate_mcmclog_dagumlog_gammalog_gamma_mixturelog_gompertzlog_logLogisticlog_lomaxlog_weibullplot.bayesCureModelprint.bayesCureModelsummary.bayesCureModel
Dependencies:assertthatbbmlebdsmatrixBHcalculusclicodacodetoolscolorspacecpp11data.tabledeSolvedoParalleldplyrfansifarverfastGHQuadflexsurvforeachgenericsggplot2gluegtableHDIntervalisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmclustmgcvmstatemuhazmunsellmvtnormnlmenumDerivpillarpkgconfigpurrrquadprogR6RColorBrewerRcppRcppArmadillorlangrstpm2scalesstatmodstringistringrsurvivaltibbletidyrtidyselectutf8vctrsVGAMviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian Cure Rate Modeling for Time-to-Event Data | bayesCureRateModel-package bayesCureRateModel |
| Logarithm of the complete log-likelihood for the general cure rate model. | complete_log_likelihood_general |
| Main function of the package | cure_rate_MC3 |
| The basic MCMC scheme. | cure_rate_mcmc |
| PDF and CDF of the Dagum distribution | log_dagum |
| PDF and CDF of the Gamma distribution | log_gamma |
| PDF and CDF of a Gamma mixture distribution | log_gamma_mixture |
| PDF and CDF of the Gompertz distribution | log_gompertz |
| PDF and CDF of the log-Logistic distribution. | log_logLogistic |
| PDF and CDF of the Lomax distribution | log_lomax |
| PDF and CDF of the Weibull distribution | log_weibull |
| Marriage data | marriage_dataset |
| Plot method | plot.bayesCureModel |
| Print method | print.bayesCureModel |
| Summary method. | summary.bayesCureModel |
