This package is considered a duplicate. The official version of this package is found at:https://ocbe-uio.r-universe.dev/GPTCM

Package: GPTCM 2.0.0

Zhi Zhao

GPTCM: Generalized Promotion Time Cure Model with Bayesian Shrinkage Priors

Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) <doi:10.48550/arXiv.2509.01001>). The Bayesian GPTCMs are applicable for both low- and high-dimensional data.

Authors:Zhi Zhao [aut, cre]

GPTCM_2.0.0.tar.gz
GPTCM_2.0.0.tar.gz(r-4.7-arm64)GPTCM_2.0.0.tar.gz(r-4.7-x86_64)GPTCM_2.0.0.tar.gz(r-4.6-arm64)GPTCM_2.0.0.tar.gz(r-4.6-x86_64)
GPTCM_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GPTCM/json (API)
NEWS

# Install 'GPTCM' in R:

install.packages('GPTCM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ocbe-uio/gptcm/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

openblas cpp openmp

2.60 score 2 scripts 205 downloads 8 exports 117 dependencies

Last updated from:0d2c62fed5. Checks:4 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	NOTE	262
linux-devel-x86_64	NOTE	264
source / vignettes	OK	334
linux-release-arm64	NOTE	243
linux-release-x86_64	NOTE	244
wasm-release	OK	197

Exports:getEstimator GPTCM metropolis_sampler plotBrier plotCoeff plotMCMC simData target

Dependencies:abind backports base64enc BH bslib cachem checkmate cli cluster cmprsk codetools colorspace cpp11 data.table diagram digest distr distributional doParallel evaluate farver fastmap fontawesome foreach foreign Formula fs future future.apply generics ggplot2 ggridges glmnet globals glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv loo magrittr MASS Matrix MatrixModels matrixStats memoise mets miCoPTCM mime multcomp mvnfast mvtnorm nleqslv nlme nnet numDeriv parallelly pillar pkgconfig plotrix polspline posterior prodlim progressr Publish quantreg R6 ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen riskRegression rlang rmarkdown rms rpart rstudioapi S7 sandwich sass scales sfsmisc shape SparseM SQUAREM startupmsg stringi stringr survival tensorA TH.data tibble timereg tinytex utf8 vctrs viridisLite withr xfun yaml zoo

Introduction

Rendered fromGPTCM.Rmdusingknitr::rmarkdownon Jun 08 2026.

Last update: 2026-06-03
Started: 2025-09-16

Citation

Readme and manuals

Help Manual

Help page	Topics
Extract the posterior estimate of parameters	getEstimator
Fit Bayesian GPTCM Models	GPTCM
Metropolis sampler for a target density	metropolis_sampler
Plot curves of time-dependent Brier score	plotBrier
Plot posterior estimates of regression coefficients	plotCoeff
MCMC trace-plots	plotMCMC
Prediction of survival probability	predict.GPTCM
Main function implemented in C++ for the MCMC loop	run_mcmc
Simulate data	simData
Target density	target