survivalmodels

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.

Last 30 days

This package has been downloaded 545 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 8,362 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Mar 21, 2024 with 86 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.