{ "_id": "6ae206a5327abcdf8686518b", "Package": "survdnn", "Title": "Deep Neural Networks for Survival Analysis with R 'torch'", "Version": "1.0.2", "Authors@R": "c(\nperson(given = \"Imad\",\nfamily = \"El Badisy\",\nemail = \"elbadisyimad@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(given = \"Daniel\",\nfamily = \"Falbel\",\nrole = \"ctb\")\n)", "Description": "Provides deep learning models for right-censored survival\ndata using the 'torch' backend. Supports multiple loss\nfunctions, including Cox partial likelihood, L2-penalized Cox,\ntime-dependent Cox, and accelerated failure time (AFT) loss.\nOffers a formula-based interface, built-in support for\ncross-validation, hyperparameter tuning, survival curve\nplotting, and evaluation metrics such as the C-index, Brier\nscore, and integrated Brier score. For methodological details,\nsee Kvamme et al. (2019)\n. 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