# Estimate Survival Data with Data Integration

## Documentation for package ‘survkl’ version 1.0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

cal_surv_prob | Calculate Survival Probabilities
coef.coxkl | Extract Coefficients from a 'coxkl' Object
coef.coxkl_enet | Extract Coefficients from a 'coxkl_enet' Object
coef.coxkl_ridge | Extract Coefficients from a 'coxkl_ridge' Object
coxkl | Cox Proportional Hazards Model with KL Divergence for Data Integration
coxkl_enet | Cox Proportional Hazards Model with KL Divergence for Data Integration and Lasso & Elastic Net Penalty
coxkl_ridge | Cox Proportional Hazards Model with Ridge Penalty and External Information
cv.coxkl | Cross-Validated Selection of Integration Parameter ('eta') for the Cox–KL Model
cv.coxkl_enet | Cross-Validation for CoxKL Model with elastic net & lasso penalty
cv.coxkl_ridge | Cross-Validation for CoxKL Ridge Model (eta tuning)
cv.plot | Plot Cross-Validation Results vs Eta
ExampleData_highdim | Example high-dimensional survival data
ExampleData_lowdim | Example low-dimensional survival data
generate_eta | Generate a Sequence of Tuning Parameters (eta)
loss_fn | Calculate the Log-Partial Likelihood for a Stratified Cox Model
plot.coxkl | Plot Model Performance vs Eta for 'coxkl'
plot.coxkl_enet | Plot Model Performance vs Lambda for 'coxkl_enet'
plot.coxkl_ridge | Plot Model Performance vs Lambda for 'coxkl_ridge'
predict.coxkl | Predict Linear Predictors from a 'coxkl' Object
predict.coxkl_enet | Predict Linear Predictors from a coxkl_enet Object
predict.coxkl_ridge | Predict Linear Predictors from a coxkl_ridge Object
support | Study to Understand Prognoses Preferences Outcomes and Risks of Treatment
test_eval | Evaluate model performance on test data