I’m pleased to announce that scikit-survival version 0.4 has been released.
This release adds CoxnetSurvivalAnalysis, which implements an efficient algorithm to fit Cox’s proportional hazards model with LASSO, ridge, and elastic net penalty. This allows fitting a Cox model to high-dimensional data and perform feature selection. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.
You can install the latest version via Anaconda (OSX and Linux):
conda install scikit-survival
or via pip (all platforms):
pip install -U scikit-survival
Last week, I presented an Introduction to Survival Analysis with scikit-survival at PyCon UK in Cardiff in front of a packed audience of genuinely interested people. I hope some people will give scikit-survial a try and use it in their work.
The slides of my presentation are available at https://k-d-w.org/pyconuk-2017/.