Installation | A simulation study | References
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:
GPTCM_2.0.0.tar.gz
GPTCM_2.0.0.zip(r-4.7-x86_64)GPTCM_2.0.0.zip(r-4.7-arm64)GPTCM_2.0.0.zip(r-4.6-x86_64)GPTCM_2.0.0.zip(r-4.6-arm64)
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✨
DESCRIPTION |NEWS
card.svg |card.png
GPTCM/json (API)
| # 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:
Last updated from:0d2c62fed5. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 209 | ||
| linux-devel-x86_64 | OK | 238 | ||
| source / vignettes | OK | 341 | ||
| linux-release-arm64 | OK | 219 | ||
| linux-release-x86_64 | OK | 265 | ||
| windows-devel-arm64 | OK | 534 | ||
| windows-devel-x86_64 | OK | 294 | ||
| windows-release-arm64 | OK | 345 | ||
| windows-release-x86_64 | OK | 264 | ||
| wasm-release | OK | 182 |
Exports:getEstimatorGPTCMmetropolis_samplerplotBrierplotCoeffplotMCMCsimDatatarget
Dependencies:abindbackportsbase64encBHbslibcachemcheckmatecliclustercmprskcodetoolscolorspacecpp11data.tablediagramdigestdistrdistributionaldoParallelevaluatefarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2ggridgesglmnetglobalsgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvloomagrittrMASSMatrixMatrixModelsmatrixStatsmemoisemetsmiCoPTCMmimemultcompmvnfastmvtnormnleqslvnlmennetnumDerivparallellypillarpkgconfigplotrixpolsplineposteriorprodlimprogressrPublishquantregR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenriskRegressionrlangrmarkdownrmsrpartrstudioapiS7sandwichsassscalessfsmiscshapeSparseMSQUAREMstartupmsgstringistringrsurvivaltensorATH.datatibbletimeregtinytexutf8vctrsviridisLitewithrxfunyamlzoo
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 |