SuperSurv
A Unified Framework for Machine Learning Ensembles in Survival Analysis
Implements a Super Learner framework for right-censored survival data. The package fits convex combinations of parametric, semiparametric, and machine learning survival learners by minimizing cross-validated risk using inverse probability of censoring weighting (IPCW). It provides tools for automated hyperparameter grid search, high-dimensional variable screening, and evaluation of prediction performance using metrics such as the Brier score, Uno's C-index, and time-dependent area under the curve (AUC). Additional utilities support model interpretation for survival ensembles, including Shapley additive explanations (SHAP), and estimation of covariate-adjusted restricted mean survival time (RMST) contrasts. The methodology is related to treatment-specific survival curve estimation using machine learning described by Westling, Luedtke, Gilbert and Carone (2024) <doi:10.1080/01621459.2023.2205060>, and the unified ensemble framework described in Lyu et al. (2026) <doi:10.64898/2026.03.11.711010>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | SuperSurv_0.1.1.tar.gz |
7.3 MiB |
0.1.1 |
rolling linux/noble R-4.5 | SuperSurv_0.1.1.tar.gz |
7.3 MiB |
0.1.1 |
rolling source/ R- | SuperSurv_0.1.1.tar.gz |
7.1 MiB |
0.1.1 |
latest linux/jammy R-4.5 | SuperSurv_0.1.1.tar.gz |
7.3 MiB |
0.1.1 |
latest linux/noble R-4.5 | SuperSurv_0.1.1.tar.gz |
7.3 MiB |
0.1.1 |
latest source/ R- | SuperSurv_0.1.1.tar.gz |
7.1 MiB |
0.1.1 |
2026-04-26 source/ R- | SuperSurv_0.1.1.tar.gz |
7.1 MiB |
0.1.1 |
2026-04-23 source/ R- | SuperSurv_0.1.1.tar.gz |
7.1 MiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | SuperSurv_0.1.1.zip |
7.4 MiB |