lifelines 0.22.0 on Python PyPI

Ability to create custom parametric regression models by specifying the cumulative hazard. This enables new and extensions of AFT models.
percentile(p) method added to univariate models that solves the equation p = S(t) for t
for parametric univariate models, the conditional_time_to_event_ is now exact instead of an approximation.

In Cox models, the attribute hazards_ has been renamed to params_. This aligns better with the other regression models, and is more clear (what is a hazard anyways?)
In Cox models, a new hazard_ratios_ attribute is available which is the exponentiation of params_.
In regression models, the column names in confidence_intervals_ has changed to include the alpha value.
In regression models, some column names in .summary and .print_summary has changed to include the alpha value.
In regression models, some column names in .summary and .print_summary includes confidence intervals for the exponential of the value.
Significant changes to internal AFT code.
A change to how fit_intercept works in AFT models. Previously one could set fit_intercept to False and not have to set ancillary_df - now one must specify a DataFrame.

for parametric univariate models, the conditional_time_to_event_ is now exact instead of an approximation.

lifelines 0.22.0 v0.22.0 on Python PyPI