A crash-update to lifelines
summary
Lifelines now uses automatic differentiation to enable new statistical models and faster computation, it maintains a rapid release cadence of every two to three weeks, and it implements recent methods such as survival calibration often within a day of publication. The library ships working features for model fitting and validation, and identifies concrete gaps: a robust scikit‑learn wrapper, cleaner time‑varying dataframe builders, and better xarray/dask integration.
AI-written summary · about
related talks
01h 23mPredictive survival analysis with scikit-learn, scikit-survival and lifelines
27mSoftware Library APIs: Lessons Learned from scikit-learn
25mLifesaving AI and JavaScript
30mCrash detector
21mKeynote: Easier Machine Learning Thoughts From Scikit-Learn
38mBridging the Chasm Between Research & Software Development
35mScikit-Learn can do THAT?!
54mFrom the Life of a Data Scientist