Package: bayesCureRateModel 1.3

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 (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]

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'))

Peer review:

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

openblascpp

1.60 score 1 scripts 246 downloads 22 exports 63 dependencies

Last updated 2 months agofrom:04a79a8260. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 03 2024
R-4.5-linux-x86_64OKDec 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