A Unified Framework for Machine Learning Ensembles in Survival Analysis.
SuperSurv: A Unified Framework for Machine Learning Ensembles in Survival Analysis 
SuperSurv is an R package for building, evaluating, and interpreting ensemble models for right-censored survival data.
At its core, the package implements a Super Learner-style ensemble framework for continuous-time survival prediction under right censoring. Using inverse probability of censoring weighting (IPCW), it combines heterogeneous base learners by minimizing cross-validated prediction risk. The framework supports learners that return full survival curves as well as learners that return only risk scores, which are calibrated to a common survival-probability scale on a shared evaluation time grid.
Beyond ensemble fitting, SuperSurv provides tools for:
- hyperparameter tuning
- high-dimensional screening
- time-dependent model benchmarking
- SHAP-based interpretability
- covariate-adjusted restricted mean survival time (RMST) contrasts
The package also provides a more user-friendly model interface through print(), summary(), coef(), and exported accessors such as event_weights(), censor_weights(), learner_names(), training_variables(), selected_variables(), and eval_times().
🚀 Get Started
The best place to start is the installation and setup tutorial:
👉 Tutorial 0: Installation & Setup
You can also browse the full documentation site here:
📦 Installation
Install the CRAN release:
install.packages("SuperSurv")
Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("yuelyu21/SuperSurv")
📦 Dependency philosophy
To keep installation lightweight, several heavier machine-learning engines are listed in Suggests rather than imported as strict dependencies. This means users can install only the modeling backends they plan to use. If a requested learner is unavailable, SuperSurv will prompt the user to install the required package.
📚 Included learners and screeners
SuperSurv currently standardizes a broad set of prediction wrappers and screening methods within a unified interface.
Prediction learners
- Machine learning: random forests, gradient boosting, XGBoost, support vector machines, BART, ranger
- Penalized/high-dimensional: elastic net, ridge regression, CoxBoost
- Tree-based: RPART
- Classical/parametric: Cox proportional hazards, Weibull, exponential, log-logistic, log-normal, generic parametric models
- Smoothing/splines: generalized additive models
- Baseline models: Kaplan–Meier
Screening methods
- keep all features
- marginal Cox screening
- variance-based screening
- elastic-net-based screening
- random forest variable hunting
The framework is extensible, and users can add custom learners and screeners. See the extensibility vignette for details.
📖 Documentation
The package website includes tutorials covering:
- 00. Installation & Setup
- 01. The SuperSurv Ensemble
- 02. Model Performance
- 03. Selection vs. Ensemble
- 04. Screening Methods
- 05. Hyperparameter Tuning
- 06. Random Forests
- 07. Parametric Models
- 08. SHAP Interpretability
- 09. Causal Inference (RMST)
- 10. Extending SuperSurv
📖 Citation
To cite the package, use:
citation("SuperSurv")
If you would also like to cite the accompanying preprint:
Lyu, Y., Lin, S. H., Huang, X., & Li, Z. (2026).
SuperSurv: A Unified Framework for Machine Learning Ensembles in Survival Analysis.
bioRxiv.
https://doi.org/10.64898/2026.03.11.711010
Related methodological work:
Westling, T., Luedtke, A., Gilbert, P. B., & Carone, M. (2024).
Inference for treatment-specific survival curves using machine learning.
Journal of the American Statistical Association.
https://doi.org/10.1080/01621459.2023.2205060