v0.25.0

0.25.0 - 2020-07-27

New features

• Formulas! lifelines now supports R-like formulas in regression models. See docs here.
• plot_covariate_group now can plot other y-values like hazards and cumulative hazards (default: survival function).
• CoxPHFitter now accepts late entries via entry_col.
• calibration.survival_probability_calibration now works with out-of-sample data.
• print_summary now accepts a column argument to filter down the displayed values. This helps with clutter in notebooks, latex, or on the terminal.
• add_at_risk_counts now follows the cool new KMunicate suggestions

API Changes

• With the introduction of formulas, all models can be using formulas under the hood.
  • For both custom regression models or non-AFT regression models, this means that you no longer need to add a constant column to your DataFrame (instead add a 1 as a formula string in the regressors dict). You may also need to remove the T and E columns from regressors. I've updated the models in the \examples folder with examples of this new model building.
• Unfortunately, if using formulas, your model will not be able to be pickled. This is a problem with an upstream library, and I hope to have it resolved in the near future.
• plot_covariate_groups has been deprecated in favour of plot_partial_effects_on_outcome.
• The baseline in plot_covariate_groups has changed from the mean observation (including dummy-encoded categorical variables) to median for ordinal (including continuous) and mode for categorical.
• Previously, lifelines used the label "_intercept" to when it added a constant column in regressions. To align with Patsy, we are now using "Intercept".
• In AFT models, ancillary_df kwarg has been renamed to ancillary. This reflects the more general use of the kwarg (not always a DataFrame, but could be a boolean or string now, too).
• Some column names in datasets shipped with lifelines have changed.
• The never used "lifelines.metrics" is deleted.
• With the introduction of formulas, plot_covariate_groups (now called plot_partial_effects_on_outcome) behaves differently for transformed variables. Users no longer need to add "derivatives" features, and encoding is done implicitly. See docs here.
• all exceptions and warnings have moved to lifelines.exceptions

Bug fixes

• The p-value of the log-likelihood ratio test for the CoxPHFitter with splines was returning the wrong result because the degrees of freedom was incorrect.
• better print_summary logic in IDEs and Jupyter exports. Previously it should not be displayed.
• p-values have been corrected in the SplineFitter. Previously, the "null hypothesis" was no coefficient=0, but coefficient=0.01. This is now set to the former.
• fixed NaN bug in survival_table_from_events with intervals when no events would occur in a interval.