Lifelines

New features
- new AFT models: `LogNormalAFTFitter` and `LogLogisticAFTFitter`.
- AFT models now accept a `weights_col` argument to `fit`.
- Robust errors (sandwich errors) are now avilable in AFT models using the `robust=True` kwarg in `fit`.
- Performance increase to `print_summary` in the `CoxPHFitter` and `CoxTimeVaryingFitter` model.

New features
- `ParametricUnivariateFitters`, like `WeibullFitter`, have smoothed plots when plotting (vs stepped plots)

Bug fixes
- The `ExponentialFitter` log likelihood _value_ was incorrect - inference was correct however.
- Univariate fitters are more flexiable and can allow 2-d and DataFrames as inputs.

New features
- improved stability of `LogNormalFitter`
- Matplotlib for Python3 users are not longer forced to use 2.x.

API changes
- **Important**: we changed the parameterization of the `PiecewiseExponential` to the same as `ExponentialFitter` (from `\lambda * t` to `t / \lambda`).

New features
- New regression model `WeibullAFTFitter` for fitting accelerated failure time models. Docs have been added to our [documentation](https://lifelines.readthedocs.io/) about how to use `WeibullAFTFitter` (spoiler: it's API is similar to the other regression models) and how to interpret the output.
- `CoxPHFitter` performance improvements (about 10%)
- `CoxTimeVaryingFitter` performance improvements (about 10%)


API changes
- **Important**: we changed the `.hazards_` and `.standard_errors_` on Cox models to be pandas Series (instead of Dataframes). This felt like a more natural representation of them. You may need to update your code to reflect this. See notes here: https://github.com/CamDavidsonPilon/lifelines/issues/636
- **Important**: we changed the `.confidence_intervals_` on Cox models to be transposed. This felt like a more natural representation of them. You may need to update your code to reflect this. See notes here: https://github.com/CamDavidsonPilon/lifelines/issues/636
- **Important**: we changed the parameterization of the `WeibullFitter` and `ExponentialFitter` from `\lambda * t` to `t / \lambda`. This was for a few reasons: 1) it is a more common parameterization in literature, 2) it helps in convergence.
- **Important**: in models where we add an intercept (currently only `AalenAdditiveModel`), the name of the added column has been changed from `baseline` to `_intercept`
- **Important**: the meaning of `alpha` in all fitters has changed to be the standard interpretation of alpha in confidence intervals. That means that the _default_ for alpha is set to 0.05 in the latest lifelines, instead of 0.95 in previous versions.

Bug Fixes
- Fixed a bug in the `_log_likelihood_` property of `ParametericUnivariateFitter` models. It was showing the "average" log-likelihood (i.e. scaled by 1/n) instead of the total. It now displays the total.
- In model `print_summary`s, correct a label erroring. Instead of "Likelihood test", it should have read "Log-likelihood test".
- Fixed a bug that was too frequently rejecting the dtype of `event` columns.
- Fixed a calculation bug in the concordance index for stratified Cox models. Thanks airanmehr!
- Fixed some Pandas <0.24 bugs.

- some improvements to the output of `check_assumptions`. `show_plots` is turned to `False` by default now. It only shows `rank` and `km` p-values now.
- some performance improvements to `qth_survival_time`.

- added new plotting methods to parametric univariate models: `plot_survival_function`, `plot_hazard` and `plot_cumulative_hazard`. The last one is an alias for `plot`.
- added new properties to parametric univarite models: `confidence_interval_survival_function_`, `confidence_interval_hazard_`, `confidence_interval_cumulative_hazard_`. The last one is an alias for `confidence_interval_`.
- Fixed some overflow issues with `AalenJohansenFitter`'s variance calculations when using large datasets.
- Fixed an edgecase in `AalenJohansenFitter` that causing some datasets with to be jittered too often.
- Add a new kwarg to `AalenJohansenFitter`, `calculate_variance` that can be used to turn off variance calculations since this can take a long time for large datasets. Thanks pzivich!