Lifelines

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

New features
- performance improvements for `print_summary`.

API changes
- `utils.survival_events_from_table` returns an integer weight vector as well as durations and censoring vector.
- in `AalenJohansenFitter`, the `variance` parameter is renamed to `variance_` to align with the usual lifelines convention.

Bug fixes
- Fixed an error in the `CoxTimeVaryingFitter`'s likelihood ratio test when using strata.
- Fixed some plotting bugs with `AalenJohansenFitter`

New features
- left-truncation support in AFT models, using the `entry_col` kwarg in `fit()`
- `generate_datasets.piecewise_exponential_survival_data` for generating piecewise exp. data
- Faster `print_summary` for AFT models.

API changes
- Pandas is now correctly pinned to >= 0.23.0. This was always the case, but not specified in setup.py correctly.

Bug fixes
- Better handling for extremely large numbers in `print_summary`
- `PiecewiseExponentialFitter` is available with `from lifelines import *`.