A Unified Framework for Machine Learning Ensembles in Survival Analysis R package SuperSurv version 0.1.1 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>. Software Depends: R (>= 4.0.0) Imports: survival, nnls, future.apply, stats, dplyr, magrittr Suggests: aorsf, BART, CoxBoost, glmnet, gbm, mgcv, randomForestSRC, ranger, rpart, survivalsvm, xgboost, fastshap, survex, ggplot2, tidyr, quadprog, ggforce, patchwork, knitr, rmarkdown Yue Lyu <yuelyu0521@gmail.com> Comprehensive R Archive Network (CRAN) Yue Lyu [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=SuperSurv/LICENSE) 2026-03-26 application/tgz https://CRAN.R-project.org/package=SuperSurv doi:10.32614/CRAN.package.SuperSurv