Models for Survival Analysis

Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk, survival probabilities, or survival distributions with 'distr6' <>. Models are either implemented from 'Python' via 'reticulate' <>, from code in GitHub packages, or novel implementations using 'Rcpp' <>. Novel machine learning survival models wil be included in the package in near-future updates. Neural networks are implemented from the 'Python' package 'pycox' <> and are detailed by Kvamme et al. (2019) <>. The 'Akritas' estimator is defined in Akritas (1994) . 'DNNSurv' is defined in Zhao and Feng (2020) .


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0.1.9 by Raphael Sonabend, 2 months ago

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Authors: Raphael Sonabend [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Rcpp

Suggests distr6, keras, pseudo, reticulate, survival, testthat

Linking to Rcpp

See at CRAN