| SuperSurv::base-learner-rfsrc | 06. Machine Learning with Random Survival Forests | HTML | source | R code | |
| SuperSurv::causal-rmst | 09. Causal Effects and Adjusted Marginal Contrasts (RMST) | HTML | source | R code | |
| SuperSurv::extending-supersurv | 11. Extending SuperSurv | HTML | source | R code | |
| SuperSurv::grid-search | 05. Advanced Hyperparameter Tuning & Grid Search | HTML | source | R code | |
| SuperSurv::installation | 00. Installation & Setup | HTML | source | R code | |
| SuperSurv::model-performance | 02. Model Performance & Benchmarking | HTML | source | R code | |
| SuperSurv::parametric-models | 07. Parametric Survival Models | HTML | source | R code | |
| SuperSurv::scaleup-parallel | 10. Scaling Up with Parallel Processing | HTML | source | R code | |
| SuperSurv::screening-methods | 04. High-Dimensional Data & Variable Screening | HTML | source | R code | |
| SuperSurv::shap-explanations | 08. Interpreting the Black Box with SHAP & survex | HTML | source | R code | |
| SuperSurv::supersurv-best | 03. Ensemble vs. Best Model Selection | HTML | source | R code | |
| SuperSurv::supersurv-ensemble | 01. SuperSurv with Ensemble | HTML | source | R code |