lifelines 0.13.0

v0.13

on Python PyPI

0.13.0

• Implements a new fitter CoxTimeVaryingFitter available under the lifelines namespace. This model implements the Cox model for time-varying covariates.
• Utils for creating time varying datasets available in utils.
• CoxPHFitter.fit now has accepts a weight_col kwarg so one can pass in weights per observation. This is very useful if you have many subjects, and the space of covariates is not large. Thus you can group the same subjects together and give that observation a weight equal to the count. Altogether, this means a much faster regression.
• removes is_significant and test_result from StatisticalResult. Users can instead choose their significance level by comparing to p_value. The string representation of this class has changed aswell.
• CoxPHFitter and AalenAdditiveFitter now have a score_ property that is the concordance-index of the dataset to the fitted model.
• CoxPHFitter has a slightly more intelligent (barely...) way to pick a step size, so convergence should generally be faster.
• CoxPHFitter and AalenAdditiveFitter no longer have the data property. It was an almost duplicate of the training data, but was causing the model to be very large when serialized.
• less noisy check for complete separation.
• removed datasets namespace from the main lifelines namespace