Package: SuperSurv
Type: Package
Title: A Unified Framework for Machine Learning Ensembles in Survival
        Analysis
Version: 0.1.2
Authors@R: 
    person("Yue", "Lyu", email = "yuelyu0521@gmail.com", role = c("aut", "cre"))
Author: Yue Lyu [aut, cre]
Maintainer: Yue Lyu <yuelyu0521@gmail.com>
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 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>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.0
Depends: R (>= 4.0.0)
LazyData: true
Imports: survival, nnls, future.apply, stats, dplyr, magrittr
URL: https://github.com/yuelyu21/SuperSurv,
        https://yuelyu21.github.io/SuperSurv/
BugReports: https://github.com/yuelyu21/SuperSurv/issues
Suggests: aorsf, BART, CoxBoost, glmnet, gbm, mgcv, randomForestSRC,
        ranger, rpart, survivalsvm, xgboost, fastshap, survex, ggplot2,
        tidyr, quadprog, ggforce, patchwork, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-04 08:47:29 UTC; a98455
Repository: CRAN
Date/Publication: 2026-05-04 09:30:03 UTC
