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>.

Version:	0.1.1
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:	2026-03-26
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:

6. Machine Learning with Random Survival Forests (source, R code) 9. Causal Effects and Adjusted Marginal Contrasts (RMST) (source, R code) 5. Advanced Hyperparameter Tuning & Grid Search (source, R code) 0. Installation & Setup (source, R code) 2. Model Performance & Benchmarking (source, R code) 7. Parametric Survival Models (source, R code) 10. Scaling Up with Parallel Processing (source, R code) 4. High-Dimensional Data & Variable Screening (source, R code) 8. Interpreting the Black Box with SHAP & survex (source, R code) 3. Ensemble vs. Best Model Selection (source, R code) 1. SuperSurv with Ensemble (source, R code)

Downloads:

Package source:	SuperSurv_0.1.1.tar.gz
Windows binaries:	r-devel: SuperSurv_0.1.1.zip, r-release: SuperSurv_0.1.1.zip, r-oldrel: SuperSurv_0.1.1.zip
macOS binaries:	r-release (arm64): SuperSurv_0.1.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): SuperSurv_0.1.1.tgz, r-oldrel (x86_64): SuperSurv_0.1.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=SuperSurv to link to this page.