{ "_id": "6a1fe376b401979e7343a88c", "Package": "SuperSurv", "Type": "Package", "Title": "A Unified Framework for Machine Learning Ensembles in Survival\nAnalysis", "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 ", "Description": "Implements a Super Learner framework for right-censored\nsurvival data. The package fits convex combinations of\nparametric, semiparametric, and machine learning survival\nlearners by minimizing cross-validated risk using inverse\nprobability of censoring weighting (IPCW). It provides tools\nfor automated hyperparameter grid search, high-dimensional\nvariable screening, and evaluation of prediction performance\nusing metrics such as the Brier score, Uno's C-index, and\ntime-dependent area under the curve (AUC). Additional utilities\nsupport model interpretation for survival ensembles, including\nShapley additive explanations (SHAP), and estimation of\ncovariate-adjusted restricted mean survival time (RMST)\ncontrasts. The methodology is related to treatment-specific\nsurvival curve estimation using machine learning described by\nWestling et al. (2024) , and\nthe unified ensemble framework described in Lyu et al. 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