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SuperSurv

A Unified Framework for Machine Learning Ensembles in Survival Analysis

v0.1.1 · Mar 26, 2026 · MIT + file LICENSE

Description

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>.

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installed package size

installed size is  8.1Mb
  sub-directories of 1Mb or more:
    doc    1.4Mb
    help   6.3Mb
NOTE r-oldrel-windows-x86_64

installed package size

installed size is  8.0Mb
  sub-directories of 1Mb or more:
    doc    1.4Mb
    help   6.3Mb

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NOTE 6 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 27, 2026
NOTE r-oldrel-macos-x86_64

installed package size

installed size is  8.1Mb
  sub-directories of 1Mb or more:
    doc    1.4Mb
    help   6.3Mb

Dependency Network

Dependencies Reverse dependencies survival nnls future.apply dplyr magrittr SuperSurv

Version History

new 0.1.1 Mar 26, 2026