Lifelines

- 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!

- fixed confidence intervals in cumulative hazards for parametric univarite models. They were previously
serverly depressed.
- adding left-truncation support to parametric univarite models with the `entry` kwarg in `.fit`

- Some performance improvements to parametric univariate models.
- Suppressing some irrelevant NumPy and autograd warnings, so lifeline warnings are more noticeable.
- Improved some warning and error messages.

- New univariate fitter `PiecewiseExponentialFitter` for creating a stepwise hazard model. See docs online.
- Ability to create novel parametric univariate models using the new `ParametericUnivariateFitter` super class. See docs online for how to do this.
- Unfortunately, parametric univariate fitters are not serializable with `pickle`. The library `dill` is still useable.
- Complete overhaul of all internals for parametric univariate fitters. Moved them all (most) to use `autograd`.
- `LogNormalFitter` no longer models `log_sigma`.

- bug fixes in `LogNormalFitter` variance estimates
- improve convergence of `LogNormalFitter`. We now model the log of sigma internally, but still expose sigma externally.
- use the `autograd` lib to help with gradients.
- New `LogLogisticFitter` univariate fitter available.