Package: survivalmodels 0.1.19

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' <https://CRAN.R-project.org/package=distr6>. 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>. Novel machine learning survival models wil be included in the package in near-future updates. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox> and are detailed by Kvamme et al. (2019) <https://jmlr.org/papers/v20/18-424.html>. The 'Akritas' estimator is defined in Akritas (1994) <doi:10.1214/aos/1176325630>. 'DNNSurv' is defined in Zhao and Feng (2020) <arxiv:1908.02337>.

Authors:Raphael Sonabend [aut, cre], John Zobolas [aut]

survivalmodels_0.1.19.tar.gz
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survivalmodels.pdf |survivalmodels.html
survivalmodels/json (API)
NEWS

# Install 'survivalmodels' in R:
install.packages('survivalmodels', repos = c('https://raphaels1.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/raphaels1/survivalmodels/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

24 exports 56 stars 3.38 score 1 dependencies 1 dependents 895 downloads

Last updated 14 days agofrom:e7af58c7d8

Exports:akritasbuild_keras_netbuild_pytorch_netcindexcoxtimedeephitdeepsurvdnnsurvget_keras_optimizerget_pycox_activationget_pycox_callbacksget_pycox_initget_pycox_optiminstall_kerasinstall_pycoxinstall_torchloghazparametricpchazardpycox_prepare_train_datarequireNamespacesset_seedsimsurvdatasurv_to_risk

Dependencies:Rcpp