Lifelines

New features
- `conditional_after` works for `CoxPHFitter` prediction models 😅

Bug fixes

API Changes
- `CoxPHFitter.baseline_cumulative_hazard_`'s column is renamed `"baseline cumulative hazard"` - previously it was `"baseline hazard"`. (Only applies if the model has no strata.)
- `utils.dataframe_interpolate_at_times` renamed to `utils.interpolate_at_times_and_return_pandas`.

New features
- Improvements to the __repr__ of models that takes into accounts weights.
- Better support for predicting on Pandas Series

Bug fixes
- Fixed issue where `fit_interval_censoring` wouldn't accept lists.
- Fixed an issue with `AalenJohansenFitter` failing to plot confidence intervals.

API Changes
- `_get_initial_value` in parametric univariate models is renamed `_create_initial_point`

New features
- Some performance improvements to regression models.
- lifelines will avoid penalizing the intercept (aka bias) variables in regression models.
- new `utils.restricted_mean_survival_time` that approximates the RMST using numerical integration against survival functions.

API changes
- `KaplanMeierFitter.survival_function_`'s' index is no longer given the name "timeline".

Bug fixes
- Fixed issue where `concordance_index` would never exit if NaNs in dataset.

New features
- model's now expose a `log_likelihood_` property.
- new `conditional_after` argument on `predict_*` methods that make prediction on censored subjects easier.
- new `lifelines.utils.safe_exp` to make `exp` overflows easier to handle.
- smarter initial conditions for parametric regression models.
- New regression model: `GeneralizedGammaRegressionFitter`

API changes
- removed `lifelines.utils.gamma` - use `autograd_gamma` library instead.
- removed bottleneck as a dependency. It offered slight performance gains only in Cox models, and only a small fraction of the API was being used.

Bug fixes
- AFT log-likelihood ratio test was not using weights correctly.
- corrected (by bumping) scipy and autograd dependencies
- convergence is improved for most models, and many `exp` overflow warnings have been eliminated.
- Fixed an error in the `predict_percentile` of `LogLogisticAFTFitter`. New tests have been added around this.

New features
- lifelines is now compatible with scipy>=1.3.0

Bug fixes
- fixed printing error when using robust=True in regression models
- `GeneralizedGammaFitter` is more stable, maybe.
- lifelines was allowing old version of numpy (1.6), but this caused errors when using the library. The correctly numpy has been pinned (to 1.14.0+)

New features
- New univariate model, `GeneralizedGammaFitter`. This model contains many sub-models, so it is a good model to check fits.
- added a warning when a time-varying dataset had instantaneous deaths.
- added a `initial_point` option in univariate parametric fitters.
- `initial_point` kwarg is present in parametric univariate fitters `.fit`
- `event_table` is now an attribute on all univariate fitters (if right censoring)
- improvements to `lifelines.utils.gamma`

API changes
- In AFT models, the column names in `confidence_intervals_` has changed to include the alpha value.
- In AFT models, some column names in `.summary` and `.print_summary` has changed to include the alpha value.
- In AFT models, some column names in `.summary` and `.print_summary` includes confidence intervals for the exponential of the value.

Bug fixes
- when using `censors_show` in plotting functions, the censor ticks are now reactive to the estimate being shown.
- fixed an overflow bug in `KaplanMeierFitter` confidence intervals
- improvements in data validation for `CoxTimeVaryingFitter`