Welcome to SuperSurv! | Step 1: Install the Core Package | Step 2: Install Base Learners (Optional but Recommended) | Specialized Packages | Step 3: You're Ready! | References
Package: SuperSurv 0.1.8
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>.
Authors:
SuperSurv_0.1.8.tar.gz
SuperSurv_0.1.8.zip(r-4.7-any)SuperSurv_0.1.8.zip(r-4.6-any)SuperSurv_0.1.8.zip(r-4.5-any)
SuperSurv_0.1.8.tgz(r-4.6-any)SuperSurv_0.1.8.tgz(r-4.5-any)
SuperSurv_0.1.8.tar.gz(r-4.7-any)SuperSurv_0.1.8.tar.gz(r-4.6-any)
SuperSurv_0.1.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SuperSurv/json (API)
| # Install 'SuperSurv' in R: |
| install.packages('SuperSurv', repos = c('https://yuelyu21.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yuelyu21/supersurv/issues
Pkgdown/docs site:https://yuelyu21.github.io
Datasets:
- metabric - METABRIC Breast Cancer Dataset
Last updated from:494d23eebe. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 235 | ||
| source / vignettes | OK | 478 | ||
| linux-release | OK | 203 | ||
| macos-release | OK | 177 | ||
| macos-oldrel | OK | 144 | ||
| windows-devel | OK | 184 | ||
| windows-release | OK | 146 | ||
| windows-oldrel | OK | 174 | ||
| wasm-release | OK | 118 |
Exports:censor_weightscreate_gridestimate_marginal_rmsteval_benchmarkeval_briereval_cindexeval_loglosseval_summaryeval_timeROCeval_timesevent_weightsexplain_kernelexplain_survexlearner_nameslist_wrappersplot_beeswarmplot_benchmarkplot_calibrationplot_dependenceplot_global_importanceplot_marginal_rmst_curveplot_patient_waterfallplot_predictplot_rmst_vs_obsplot_survival_heatmapscreen.allscreen.elasticnetscreen.glmnetscreen.margscreen.rfsrcscreen.varselected_variablesSuperSurvsurv.aorsfsurv.bartsurv.coxboostsurv.coxphsurv.coxtimesurv.deephitsurv.deepsurvsurv.exponentialsurv.flexsurvregsurv.flexsurvsplinesurv.gamsurv.gbmsurv.glmnetsurv.grfsurv.kmsurv.loglogisticsurv.lognormalsurv.mboostsurv.parametricsurv.rangersurv.rfsrcsurv.ridgesurv.rpartsurv.survPensurv.svmsurv.weibullsurv.xgboosttraining_variables
Dependencies:clicodetoolsdigestdplyrfuturefuture.applygenericsglobalsgluelatticelifecyclelistenvmagrittrMatrixnnlsparallellypillarpkgconfigR6rlangsurvivaltibbletidyselectutf8vctrswithr
Installation & Setup
Last update: 2026-09-23
Started: 2026-03-03
SuperSurv with Ensemble
Introduction | 1. Load Data & Prepare Matrices | 2. Define the Ensemble Library | 3. Train the SuperSurv Metalearner | The Data Inputs | The Model Libraries | The Meta-Learner & Tuning | 4. Package Object Interface | 5. Compare the Fitting Objectives | 6. Generating Predictions on New Data | Understanding the Output Matrix: | 7. Visualizing Patient-Specific Predictions | 8. Next Steps
Last update: 2026-09-23
Started: 2026-02-25
Model Performance & Benchmarking
Introduction | 1. Data Preparation & The Extrapolation Rule | 2. Train the Benchmark Ensemble | 3. Extracting Integrated Metrics | 4. Visualizing Longitudinal Benchmarks | Interpreting the Curves: | 5. Assessing Clinical Calibration
Last update: 2026-09-23
Started: 2026-02-25
Ensemble vs. Best Model Selection
Introduction | 1. Prepare the Data | 2. Fit Both Super Learners | 3. Inspecting the Weights | 4. Evaluate and Compare Performance
Last update: 2026-09-23
Started: 2026-02-25
High-Dimensional Data & Variable Screening
Introduction | 1. Simulating High-Dimensional Data | 2. Defining the Screening Algorithms | 3. Training with Embedded Screening | 4. Evaluating the Screened Ensemble | 5. Identifying the Selected Features
Last update: 2026-09-23
Started: 2026-02-25
Advanced Hyperparameter Tuning & Grid Search
Introduction | 1. Prepare the Data | 2. Defining the Search Space | 3. Generating the Tuning Grid | 4. Fitting the Tuned Ensemble
Last update: 2026-09-23
Started: 2026-03-03
Machine Learning with Random Survival Forests
Introduction | 1. Prepare the Data | 2. Standalone Machine Learning | 3. Evaluating the Standalone Model | 4. Train the Benchmark Ensemble | 5. Visualizing Ensemble vs. Base Learners
Last update: 2026-09-23
Started: 2026-02-25
Interpreting the Black Box with SHAP & survex
Introduction | 1. Setup and Model Fitting | 2. Global Explanations (Kernel SHAP) | Global Importance Bar Plot | SHAP Beeswarm Summary Plot | 3. Local (Patient-Level) Explanations | 4. Feature Dependence Plots | 5. Patient Risk Trajectories (Survival Heatmap) | 6. Time-Dependent Explanations (survex Integration) | A. Dynamic Feature Importance | B. Time-Dependent Partial Dependence Profiles | C. SurvSHAP(t): Local Explanations Over Time | D. Ecosystem Compatibility: Global Model Performance
Last update: 2026-09-23
Started: 2026-02-25
Causal Effects and Adjusted Marginal Contrasts (RMST)
Moving Beyond the Hazard Ratio | Philosophy: "Causal Effect" vs. "Marginal Contrast" | Estimating the Effect with SuperSurv | 1. Train the Super Learner | 2. Estimate the Adjusted Marginal RMST Contrast | 3. Visualizing the Effect Over Time | 4. Diagnostic: Predicted RMST vs. Observed Time
Last update: 2026-09-23
Started: 2026-03-02
Extending SuperSurv
How Learners Are Found | Learner Wrapper Contract | Prediction Method Contract | Direct Survival Curves vs Risk Scores | Optional Additional Learners | Minimal Custom Learner | Screening Method Contract | Development Checklist
Last update: 2026-09-23
Started: 2026-04-21
Parametric Survival Models
Introduction | 1. Setup and Library Definition | 2. Fitting the Parametric Ensemble | 3. Evaluating the "Safety Net" | Interpretation
Last update: 2026-05-03
Started: 2026-02-25
