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 or survival probabilities. Models are either implemented from 'Python' via 'reticulate' <>, from code in GitHub packages, or novel implementations using 'Rcpp' <>. Neural networks are implemented from the 'Python' package 'pycox' <>.

Version: 0.1.191
Imports: Rcpp (≥ 1.0.5)
LinkingTo: Rcpp
Suggests: keras (≥ 2.11.0), pseudo, reticulate, survival
Published: 2024-03-19
DOI: 10.32614/CRAN.package.survivalmodels
Author: Raphael Sonabend ORCID iD [aut], Yohann Foucher ORCID iD [cre]
Maintainer: Yohann Foucher <yohann.foucher at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README
CRAN checks: survivalmodels results


Reference manual: survivalmodels.pdf


Package source: survivalmodels_0.1.191.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): survivalmodels_0.1.191.tgz, r-oldrel (arm64): survivalmodels_0.1.191.tgz, r-release (x86_64): survivalmodels_0.1.191.tgz, r-oldrel (x86_64): survivalmodels_0.1.191.tgz
Old sources: survivalmodels archive

Reverse dependencies:

Reverse suggests: survivalSL


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