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' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/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 univ-poitiers.fr>
BugReports: https://github.com/foucher-y/survivalmodels/issues
License: MIT + file LICENSE
URL: https://github.com/RaphaelS1/survivalmodels/
NeedsCompilation: yes
Materials: README
CRAN checks: survivalmodels results

Documentation:

Reference manual: survivalmodels.html , survivalmodels.pdf

Downloads:

Package source: survivalmodels_0.1.191.tar.gz
Windows binaries: r-devel: survivalmodels_0.1.191.zip, r-release: survivalmodels_0.1.191.zip, r-oldrel: survivalmodels_0.1.191.zip
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

Linking:

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