version 1.0

DESCRIPTION

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+Package: bayesCureRateModel
+Type: Package
+Title: Bayesian Cure Rate Modeling for Time-to-Event Data
+Version: 1.0
+Date: 2024-06-26
+Authors@R: 
+    c(person(given = "Panagiotis",
+             family = "Papastamoulis",
+             email = "papapast@yahoo.gr",
+             role = c( "aut", "cre"),
+             comment = c(ORCID = "0000-0001-9468-7613")),
+	person(given = "Fotios",
+             family = "Milienos",
+             email = "milienos@panteion.gr",
+             role = c( "aut"),
+             comment = c(ORCID = "0000-0003-1423-7132"))             
+             )
+Maintainer: Panagiotis Papastamoulis <papapast@yahoo.gr>
+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.
+License: GPL-2
+URL: https://github.com/mqbssppe/Bayesian_cure_rate_model
+Imports: Rcpp (>= 1.0.12),doParallel, foreach, mclust, coda,
+        HDInterval, VGAM, calculus, flexsurv
+LinkingTo: Rcpp, RcppArmadillo
+NeedsCompilation: yes
+Packaged: 2024-06-26 11:12:12 UTC; panagiotis
+Author: Panagiotis Papastamoulis [aut, cre]
+    (<https://orcid.org/0000-0001-9468-7613>),
+  Fotios Milienos [aut] (<https://orcid.org/0000-0003-1423-7132>)
+Depends: R (>= 3.5.0)
+Repository: CRAN
+Date/Publication: 2024-06-27 14:20:06 UTC

MD5

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+56b4b47d72bad1e844a5131c24044156 *DESCRIPTION
+60b24c51f612d4bd83c8a2e34ab8c957 *NAMESPACE
+21e967f0d302ac33cb6005fd353aa3bd *R/RcppExports.R
+4260cc1b6f2fd4c502e005f3c51ef974 *R/bayesian_cure_rate_model.R
+67e6a7f7cdea1f804b310792c9c0b1f1 *build/partial.rdb
+56cc7d845e494be425fa35a2c0a3029b *data/marriage_dataset.RData
+e77ed844f7bf562884b414c39f6835cd *man/bayesCureRateModel-package.Rd
+4a8ee8d9f3760d3f1a996f185fcc0f39 *man/complete_log_likelihood_general.Rd
+0722782a2296b4d658c0239ddee156cb *man/cure_rate_MC3.Rd
+76a9b849d84c9daf772f84c66cb8968d *man/cure_rate_mcmc.Rd
+0d6af03d3c24d89673a9675fcdb15c15 *man/log_dagum.Rd
+76818d2bd38084ee0318c14b6e1bcb56 *man/log_gamma.Rd
+21bf11cc80dd46d4fe2af1c9a9abf441 *man/log_gamma_mixture.Rd
+349c3a6157f89cd1d6bdfa9d56dbb24e *man/log_gompertz.Rd
+bd451c6e627533c83dfd9fe8b489c60b *man/log_logLogistic.Rd
+11054a0a2cf28077b1f4d89969d43915 *man/log_lomax.Rd
+e51f0290b81a292149e4eb023b520041 *man/log_weibull.Rd
+08d88124945271efe7be11ace3690259 *man/marriage_dataset.Rd
+73d7632cba0b4f1ebaa7ffa9101dfade *man/plot.bayesCureModel.Rd
+523c58fb765062457488ea192fe4f27a *man/print.bayesCureModel.Rd
+ede9a7bf59808d79114625860269b671 *man/summary.bayesCureModel.Rd
+7fc9230b45e1aba7899d7a92ef95ff54 *src/Makevars
+7fc9230b45e1aba7899d7a92ef95ff54 *src/Makevars.win
+41e044a3a421f72bc08b8b4e8f2393a1 *src/RcppExports.cpp
+3cf97a5cfaea3bde3f369a675aafc78e *src/cll_reparameterization_fix.cpp

NAMESPACE

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+useDynLib(bayesCureRateModel, .registration=TRUE)
+importFrom(Rcpp, evalCpp)
+exportPattern("^[[:alpha:]]+")
+S3method(print, bayesCureModel)
+S3method(summary, bayesCureModel)
+S3method(plot, bayesCureModel)
+import(coda)
+import(doParallel)
+import(foreach)
+import('HDInterval')
+import('calculus')
+import("mclust")
+importFrom('flexsurv', "dgompertz", "pgompertz", "dllogis", "pllogis")
+importFrom('VGAM', "dlomax", "plomax", "ddagum", "pdagum")
+importFrom("grDevices", "heat.colors", "topo.colors", "dev.new", "dev.off", "rgb", "col2rgb")
+importFrom("graphics", "polygon", "abline", "layout", "legend", "matplot", "points", "par", "rect", "text", "axis", "box", "plot", "title")
+importFrom("stats", "density", "dgamma", "dnorm", "mahalanobis",
+             "pgamma", "rlnorm", "rbinom", "dbinom", "dpois", "ppois", "rbeta", "rgamma", "runif", "quantile", "rnorm", "var", "sd", "median", "qnorm")

R/RcppExports.R

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+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+log_weibull <- function(y, a1, a2, c_under) {
+    .Call(`_bayesCureRateModel_log_weibull`, y, a1, a2, c_under)
+}
+
+complete_log_likelihood_general <- function(y, X, Censoring_status, g, lambda, log_f, log_F, b, I_sim, alpha) {
+    .Call(`_bayesCureRateModel_complete_log_likelihood_general`, y, X, Censoring_status, g, lambda, log_f, log_F, b, I_sim, alpha)
+}
+