Lifelines

New features
- 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.

API changes
- 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 Cox models, the column names in `confidence_intervals_` has changed to include the alpha value.
- In Cox models, some column names in `.summary` and `.print_summary` has changed to include the alpha value.
- In Cox 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.

Bug fixes
- for parametric univariate models, the `conditional_time_to_event_` is now exact instead of an approximation.
- fixed a name error bug in `CoxTimeVaryingFitter.plot`

I'm skipping 0.21.4 version because of deployment issues.

New features
- `scoring_method` now a kwarg on `sklearn_adapter`

Bug fixes
- fixed an implicit import of scikit-learn. scikit-learn is an optional package.
- fixed visual bug that misaligned x-axis ticks and at-risk counts. Thanks christopherahern!

New features
- include in lifelines is a scikit-learn adapter so lifeline's models can be used with scikit-learn's API. See [documentation here](https://lifelines.readthedocs.io/en/latest/Compatibility%20with%20scikit-learn.html).
- `CoxPHFitter.plot` now accepts a `hazard_ratios` (boolean) parameter that will plot the hazard ratios (and CIs) instead of the log-hazard ratios.
- `CoxPHFitter.check_assumptions` now accepts a `columns` parameter to specify only checking a subset of columns.

Bug fixes
- `covariates_from_event_matrix` handle nulls better

New features
- New regression model: `PiecewiseExponentialRegressionFitter` is available. See blog post here: https://dataorigami.net/blogs/napkin-folding/churn
- Regression models have a new method `log_likelihood_ratio_test` that computes, you guessed it, the log-likelihood ratio test. Previously this was an internal API that is being exposed.

API changes
- The default behavior of the `predict` method on non-parametric estimators (`KaplanMeierFitter`, etc.) has changed from (previous) linear interpolation to (new) return last value. Linear interpolation is still possible with the `interpolate` flag.
- removing `_compute_likelihood_ratio_test` on regression models. Use `log_likelihood_ratio_test` now.

Bug fixes

New features
- users can provided their own start and stop column names in `add_covariate_to_timeline`
- PiecewiseExponentialFitter now allows numpy arrays as breakpoints

API changes
- output of `survival_table_from_events` when collapsing rows to intervals now removes the "aggregate" column multi-index.

Bug fixes
- fixed bug in CoxTimeVaryingFitter when ax is provided, thanks j-i-l!

New features
- `weights` is now a optional kwarg for parametric univariate models.
- all univariate and multivariate parametric models now have ability to handle left, right and interval censored data (the former two being special cases of the latter). Users can use the `fit_right_censoring` (which is an alias for `fit`), `fit_left_censoring` and `fit_interval_censoring`.
- a new interval censored dataset is available under `lifelines.datasets.load_diabetes`

API changes
- `left_censorship` on all univariate fitters has been deprecated. Please use the new
api `model.fit_left_censoring(...)`.
- `invert_y_axis` in `model.plot(...` has been removed.
- `entries` property in multivariate parametric models has a new Series name: `entry`

Bug fixes
- lifelines was silently converting any NaNs in the event vector to True. An error is now thrown instead.
- Fixed an error that didn't let users use Numpy arrays in prediction for AFT models