install_torch

See <a rd-options="reticulate:py_install" href="/link/reticulate%3A%3Apy_install?package=survivalmodels&version=0.1.13&to=reticulate%3Apy_install" data-mini-rdoc="reticulate:py_install::reticulate::py_install">reticulate::py_install</a>

method, conda, pip

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

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>.

Raphael Sonabend

survivalmodels

Models for Survival Analysis

install_torch function

See <a rd-options='reticulate:py_install' href='reticulate::py_install'>reticulate::py_install</a>

install_torch: Install Torch With Reticulate

Description

Usage

Arguments