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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 |
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 |
Reference manual: | survivalmodels.pdf |
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 suggests: | survivalSL |
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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.