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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.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

Reverse dependencies:

Reverse suggests: survivalSL

Linking:

Please use the canonical form https://CRAN.R-project.org/package=survivalmodels to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.