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 et al. (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>.

Version: 0.1.2
Depends: R (≥ 4.0.0)
Imports: survival, nnls, future.apply, stats, dplyr, magrittr
Suggests: aorsf, BART, CoxBoost, glmnet, gbm, mgcv, randomForestSRC, ranger, rpart, survivalsvm, xgboost, fastshap, survex, ggplot2, tidyr, quadprog, ggforce, patchwork, knitr, rmarkdown
Published: 2026-05-04
DOI: 10.32614/CRAN.package.SuperSurv
Author: Yue Lyu [aut, cre]
Maintainer: Yue Lyu <yuelyu0521 at gmail.com>
BugReports: https://github.com/yuelyu21/SuperSurv/issues
License: MIT + file LICENSE
URL: https://github.com/yuelyu21/SuperSurv, https://yuelyu21.github.io/SuperSurv/
NeedsCompilation: no
Citation: SuperSurv citation info
Materials: README
CRAN checks: SuperSurv results

Documentation:

Reference manual: SuperSurv.html , SuperSurv.pdf
Vignettes: 06. Machine Learning with Random Survival Forests (source, R code)
09. Causal Effects and Adjusted Marginal Contrasts (RMST) (source, R code)
11. Extending SuperSurv (source, R code)
05. Advanced Hyperparameter Tuning & Grid Search (source, R code)
00. Installation & Setup (source, R code)
02. Model Performance & Benchmarking (source, R code)
07. Parametric Survival Models (source, R code)
10. Scaling Up with Parallel Processing (source, R code)
04. High-Dimensional Data & Variable Screening (source, R code)
08. Interpreting the Black Box with SHAP & survex (source, R code)
03. Ensemble vs. Best Model Selection (source, R code)
01. SuperSurv with Ensemble (source, R code)

Downloads:

Package source: SuperSurv_0.1.2.tar.gz
Windows binaries: r-devel: SuperSurv_0.1.2.zip, r-release: SuperSurv_0.1.1.zip, r-oldrel: SuperSurv_0.1.2.zip
macOS binaries: r-release (arm64): SuperSurv_0.1.2.tgz, r-oldrel (arm64): SuperSurv_0.1.2.tgz, r-release (x86_64): SuperSurv_0.1.2.tgz, r-oldrel (x86_64): SuperSurv_0.1.2.tgz
Old sources: SuperSurv archive

Linking:

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