survivalmodels: 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) <doi:10.1214/aos/1176325630>. 'DNNSurv' is defined in Zhao and Feng (2020) <arXiv:1908.02337>.

Version: 0.1.1
Imports: Rcpp (≥ 1.0.5)
LinkingTo: Rcpp
Suggests: distr6 (≥ 1.4.4), keras, pseudo, reticulate, survival, testthat
Published: 2020-10-20
Author: Raphael Sonabend ORCID iD [aut, cre]
Maintainer: Raphael Sonabend <raphael.sonabend.15 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: survivalmodels results


Reference manual: survivalmodels.pdf
Package source: survivalmodels_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: survivalmodels_0.1.1.tgz, r-oldrel: survivalmodels_0.1.1.tgz
Old sources: survivalmodels archive


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