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>.

Models for Survival Analysis

package-ubuntu-20.04-r-4.0.5#cran#survivalmodels-0.1.11

<p>Installs the python 'torch' package via reticulate. Note the
default for <code>pip</code> is changed to <code>TRUE</code>.
</p>

install_torch(method = "auto", conda = "auto", pip = TRUE)

<table summary="R argblock"><tr valign="top">
<td><code>method, conda, pip</code></td>
<td>
<p>See <a href="../../reticulate/html/py_install.html">reticulate::py_install</a></p>
</td>
</tr></table>

package-ubuntu-20.04-r-4.0.5#cran#survivalmodels-0.1.11#docs#install_torch

install_torch

Description

Usage

Arguments

survivalmodels

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