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' < https://CRAN.R-project.org/package=distr6>. Models are either implemented from 'Python' via 'reticulate' < https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' < https://CRAN.R-project.org/package=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' < https://github.com/havakv/pycox> and are detailed by Kvamme et al. (2019) < https://jmlr.org/papers/v20/18-424.html>. The 'Akritas' estimator is defined in Akritas (1994) . 'DNNSurv' is defined in Zhao and Feng (2020) .


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install.packages("survivalmodels")

0.1.1 by Raphael Sonabend, 20 hours ago


https://github.com/RaphaelS1/survivalmodels/


Report a bug at https://github.com/RaphaelS1/survivalmodels/issues


Browse source code at https://github.com/cran/survivalmodels


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