A Unified Framework for Machine Learning Ensembles in Survival Analysis
Overview
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.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-18
Show 3 earlier changesHide 3 earlier changes
- ERROR11 OK, 2 ERROR2026-05-28
- OK12 OK2026-04-25
- NOTE6 OK, 1 NOTE2026-03-27
Documentation
- Help pages
- 68 (4 internal)
- Examples that run
- 96%53 run in a plain check (33 of them under a condition) · 2 never run
- Exported functions with examples
- 88%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 0%
- 00. Installation & Setup HTML
- 01. SuperSurv with Ensemble HTML
- 02. Model Performance & Benchmarking HTML
- 03. Ensemble vs. Best Model Selection HTML
- 04. High-Dimensional Data & Variable Screening HTML
- 05. Advanced Hyperparameter Tuning & Grid Search HTML
- 06. Machine Learning with Random Survival Forests HTML
- 07. Parametric Survival Models HTML
- 08. Interpreting the Black Box with SHAP & survex HTML
- 09. Causal Effects and Adjusted Marginal Contrasts (RMST) HTML
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Downloads
Repository
- PRs merged (all time)
- 100%
- Median days to close, 50 most recently updated closed issues
- not measured
- Median days to close, 50 most recently updated closed PRs
- 2
Stars over time
PRs over time
Commits over time
7 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/yuelyu21/supersurv on 2026-10-18.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
GitHub Pages site last deployed 2026-10-07 · The repository has a site deploy workflow
pkgdown site last built 2026-10-07
Dependencies
Installing SuperSurv adds 22 packages (12 with compiled code, 6.4 MB of source tarballs). Heaviest: vctrs 1.0 MB, dplyr 902 KB, rlang 766 KB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- 0.1.9Latest2026-10-02 · current release · diff ↗ ·
added 10 exports
eval_benchmark, eval_logloss, surv.coxtime, surv.deephit, surv.deepsurv, surv.flexsurvreg, surv.flexsurvspline, surv.grf, surv.mboost, surv.survPen - unarchivedReturned to CRAN2026-06-11
- 0.1.72026-06-11 · diff ↗
- archivedRemoved from CRAN2026-06-10issues were not corrected in time
- 0.1.22026-05-04 · diff ↗ ·
added 6 exports
censor_weights, eval_times, event_weights, learner_names, selected_variables, training_variables - RR 4.6.0 released · 2026-04-24
- 0.1.12026-03-26
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-03-26
- Total releases
- 4 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 65 KB / 1 file
- Download size
- 7.0 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("SuperSurv")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.64898/2026.03.11.711010.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.