Package: GPTCM 2.1.0

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_2.1.0.tar.gz
GPTCM_2.1.0.zip(r-4.7)GPTCM_2.1.0.zip(r-4.6)GPTCM_2.1.0.zip(r-4.5)
GPTCM_2.1.0.tgz(r-4.6-x86_64)GPTCM_2.1.0.tgz(r-4.6-arm64)GPTCM_2.1.0.tgz(r-4.5-x86_64)GPTCM_2.1.0.tgz(r-4.5-arm64)
GPTCM_2.1.0.tar.gz(r-4.7-arm64)GPTCM_2.1.0.tar.gz(r-4.7-x86_64)GPTCM_2.1.0.tar.gz(r-4.6-arm64)GPTCM_2.1.0.tar.gz(r-4.6-x86_64)
GPTCM_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GPTCM/json (API)

# Install 'GPTCM' in R:

install.packages('GPTCM', repos = c('https://ocbe-uio.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:

bayesian-inference cell-populations multiscale-data omics-data-integration survival-analysis tumor-heterogeneity tumor-microenvironment variable-selection openblas cpp openmp

4.93 score 4 scripts 423 downloads 8 exports 131 dependencies

Last updated from:6417d59b2e. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	291
linux-devel-x86_64	NOTE	321
source / vignettes	OK	333
linux-release-arm64	NOTE	334
linux-release-x86_64	NOTE	330
macos-release-arm64	NOTE	217
macos-release-x86_64	NOTE	495
macos-oldrel-arm64	NOTE	165
macos-oldrel-x86_64	NOTE	675
windows-devel	NOTE	477
windows-release	NOTE	409
windows-oldrel	NOTE	394
wasm-release	OK	238

Exports:getEstimator GPTCM metropolis_sampler plotBrier plotCoeff plotMCMC simData target

Dependencies:abind backports base64enc BH brio bslib cachem callr checkmate cli cluster cmprsk codetools colorspace cpp11 crayon data.table desc diagram diffobj 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 otel parallelly pillar pkgbuild pkgconfig pkgload plotrix polspline posterior praise processx prodlim progressr ps Publish quantreg R6 ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen riskRegression rlang rmarkdown rms rpart rprojroot rstudioapi S7 sandwich sass scales sfsmisc shape SparseM SQUAREM startupmsg stringi stringr survival tensorA testthat TH.data tibble timereg tinytex utf8 vctrs viridisLite waldo withr xfun yaml zoo

Introduction

Installation | A simulation study | References

Rendered fromGPTCM.Rmdusingknitr::rmarkdown

Last update: 2026-06-04
Started: 2025-09-07

Citation

Development and contributors

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