Package index • SuperSurv

Main functions

SuperSurv(): Super Learner for conditional survival functions
predict(<SuperSurv>): Predict method for SuperSurv fits
eval_times(): Access SuperSurv prediction evaluation times
event_weights() censor_weights(): Access SuperSurv ensemble weights
coef(<SuperSurv>): Extract SuperSurv ensemble coefficients
learner_names(): Access SuperSurv learner names
selected_variables(): Access variables selected by SuperSurv screeners
training_variables(): Access SuperSurv training variable names
print(<SuperSurv>): Print a SuperSurv fit
summary(<SuperSurv>) print(<summary.SuperSurv>): Summarize a SuperSurv fit
create_grid(): Create a Tuning Grid of Survival Learners

Base learners and screening

surv.aorsf(): Wrapper for AORSF (Oblique Random Survival Forest)
surv.bart(): Wrapper for BART (Bayesian Additive Regression Trees)
surv.coxboost(): Wrapper function for Component-Wise Boosting (CoxBoost)
surv.coxph(): Wrapper for standard Cox Proportional Hazards
surv.exponential(): Parametric Survival Prediction Wrapper (Exponential)
surv.gam(): Wrapper for Generalized Additive Cox Regression (GAM)
surv.gbm(): Wrapper function for Gradient Boosting (GBM) prediction algorithm
surv.glmnet(): Wrapper function for Penalized Cox Regression (GLMNET)
surv.km(): Kaplan-Meier Prediction Algorithm
surv.loglogistic(): Parametric Survival Prediction Wrapper (Log-Logistic)
surv.lognormal(): Parametric Survival Prediction Wrapper (Log-Normal)
surv.parametric(): Universal Parametric Survival Wrapper
surv.ranger(): Wrapper function for Ranger Random Survival Forest
surv.rfsrc(): Wrapper function for Random Survival Forests (RFSRC)
surv.ridge(): Wrapper for Ridge Regression (Penalized Cox)
surv.rpart(): Wrapper for Survival Regression Trees (rpart)
surv.svm(): Wrapper for Survival Support Vector Machine (survivalsvm)
surv.weibull(): Parametric Survival Prediction Wrapper (Weibull)
surv.xgboost(): Wrapper for XGBoost (Robust CV-Tuned + Safe Prediction)
screen.all(): Keep All Variables Screener
screen.elasticnet(): Elastic Net Screening Algorithm
screen.glmnet(): GLMNET (Lasso) Screening
screen.marg(): Marginal Cox Regression Screening
screen.rfsrc(): Random Survival Forest Screening Algorithm
screen.var(): High Variance Screening Algorithm (Unsupervised)
list_wrappers(): List Available Wrappers and Screeners in SuperSurv

Evaluation and interpretation

eval_brier(): IPCW Brier Score and Integrated Brier Score (IBS)
eval_cindex(): Calculate Concordance Index (Harrell's or Uno's)
eval_summary(): Evaluate SuperSurv predictions on test data
eval_timeROC(): Time-Dependent AUC and Integrated AUC
eval_times(): Access SuperSurv prediction evaluation times
plot_beeswarm(): Beeswarm Summary Plot for SuperSurv SHAP
plot_benchmark(): Plot Longitudinal Benchmark Metrics
plot_calibration(): Plot Survival Calibration Curve
plot_dependence(): Plot SHAP Dependence for SuperSurv
plot_global_importance(): Plot Global Feature Importance for SuperSurv
plot_marginal_rmst_curve(): Plot Adjusted Marginal RMST Contrast Over Time
plot_patient_waterfall(): Waterfall Plot for an Individual Patient
plot_predict(): Plot Predicted Survival Curves
plot_rmst_vs_obs(): Plot Predicted RMST vs. Observed Survival Times
plot_survival_heatmap(): Survival Probability Heatmap
explain_kernel(): Explain Predictions with Global SHAP (Kernel SHAP)
explain_survex(): Create a Time-Dependent Survex Explainer
estimate_marginal_rmst(): Estimate an Adjusted Marginal RMST Contrast
get_rmst(): Calculate Restricted Mean Survival Time (RMST)

Data

metabric: METABRIC Breast Cancer Dataset

Low-level predict methods

S3 methods for fitted base-learner wrapper objects.