survivalmodels

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) doi:10.1214/aos/1176325630. 'DNNSurv' is defined in Zhao and Feng (2020) .