Package: survkl 1.0.0

Yubo Shao

survkl: Estimate Survival Data with Data Integration

Provides flexible and efficient tools for integrating external risk scores into Cox proportional hazards models while accounting for population heterogeneity. Enables robust estimation, improved predictive accuracy, and user-friendly workflows for modern survival analysis. For more information, see Wang et al. (2023) <doi:10.48550/arXiv.2302.11123>.

Authors:Yubo Shao [aut, cre], Lingfeng Luo [aut], Xiaohan Liu [aut], Junyi Qiu [aut], Di Wang [aut], Kevin He [aut]

survkl_1.0.0.tar.gz
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survkl_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
survkl/json (API)
NEWS

# Install 'survkl' in R:
install.packages('survkl', repos = c('https://um-kevinhe.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/um-kevinhe/survkl/issues

Pkgdown/docs site:https://um-kevinhe.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

5.62 score 2 scripts 452 downloads 11 exports 23 dependencies

Last updated from:e56d70a48f. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK240
linux-devel-x86_64OK187
source / vignettesOK430
linux-release-arm64OK3923
linux-release-x86_64OK168
macos-release-arm64OK184
macos-release-x86_64OK526
macos-oldrel-arm64OK209
macos-oldrel-x86_64OK338
windows-develOK184
windows-releaseOK156
windows-oldrelOK125
wasm-releaseOK1572

Exports:cal_surv_probcoxklcoxkl_enetcoxkl_ridgecv.coxklcv.coxkl_enetcv.coxkl_ridgecv.plotgenerate_etaloss_fntest_eval

Dependencies:clicowplotcpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMatrixR6RColorBrewerRcppRcppArmadilloRcppParallelrlangS7scalesvctrsviridisLitewithr

Methods for Transfer-learning Based Integrated Cox Models

Rendered fromMethods.Rmdusingknitr::rmarkdownon Jun 06 2026.

Last update: 2026-06-06
Started: 2025-12-09

survkl: Transfer-Learning Based Integrated Cox Models

Rendered fromsurvkl.Rmdusingknitr::rmarkdownon Jun 06 2026.

Last update: 2026-06-06
Started: 2025-12-09

Readme and manuals

Help Manual

Help pageTopics
Calculate Survival Probabilitiescal_surv_prob
Extract Coefficients from a 'coxkl' Objectcoef.coxkl
Extract Coefficients from a 'coxkl_enet' Objectcoef.coxkl_enet
Extract Coefficients from a 'coxkl_ridge' Objectcoef.coxkl_ridge
Cox Proportional Hazards Model with KL Divergence for Data Integrationcoxkl
Cox Proportional Hazards Model with KL Divergence for Data Integration and Lasso & Elastic Net Penaltycoxkl_enet
Cox Proportional Hazards Model with Ridge Penalty and External Informationcoxkl_ridge
Cross-Validated Selection of Integration Parameter ('eta') for the Cox–KL Modelcv.coxkl
Cross-Validation for CoxKL Model with elastic net & lasso penaltycv.coxkl_enet
Cross-Validation for CoxKL Ridge Model (eta tuning)cv.coxkl_ridge
Plot Cross-Validation Results vs Etacv.plot
Example high-dimensional survival dataExampleData_highdim
Example low-dimensional survival dataExampleData_lowdim
Generate a Sequence of Tuning Parameters (eta)generate_eta
Calculate the Log-Partial Likelihood for a Stratified Cox Modelloss_fn
Plot Model Performance vs Eta for 'coxkl'plot.coxkl
Plot Model Performance vs Lambda for 'coxkl_enet'plot.coxkl_enet
Plot Model Performance vs Lambda for 'coxkl_ridge'plot.coxkl_ridge
Predict Linear Predictors from a 'coxkl' Objectpredict.coxkl
Predict Linear Predictors from a coxkl_enet Objectpredict.coxkl_enet
Predict Linear Predictors from a coxkl_ridge Objectpredict.coxkl_ridge
Study to Understand Prognoses Preferences Outcomes and Risks of Treatmentsupport
Evaluate model performance on test datatest_eval