Lifelines

- Fix bug in `plot_covariate_groups` that wasn't allowing for strata to be used.
- change name of `multicenter_aids_cohort_study` to `load_multicenter_aids_cohort_study`
- `groups` is now called `values` in `CoxPHFitter.plot_covariate_groups`

- Fix in `compute_residuals` when using `schoenfeld` and the minumum duration has only censored subjects.

- Another round of serious performance improvements for the Cox models. Up to 2x faster for CoxPHFitter and CoxTimeVaryingFitter. This was mostly the result of using NumPy's `einsum` to simplify a previous `for` loop. The downside is the code is more esoteric now. I've added comments as necessary though 🤞

- adding bottleneck as a dependency. This library is highly-recommended by Pandas, and in lifelines we see some nice performance improvements with it too. (~15% for `CoxPHFitter`)
- There was a small bug in `CoxPHFitter` when using `batch_mode` that was causing coefficients to deviate from their MLE value. This bug eluded tests, which means that it's discrepancy was less than 0.0001 difference. It's fixed now, and even more accurate tests are added.
- Faster `CoxPHFitter._compute_likelihood_ratio_test()`
- Fixes a Pandas performance warning in `CoxTimeVaryingFitter`.
- Performances improvements to `CoxTimeVaryingFitter`.

- corrected behaviour in `CoxPHFitter` where `score_` was not being refreshed on every new `fit`.
- Reimplentation of `AalenAdditiveFitter`. There were significant changes to it:
- implementation is at least 10x faster, and possibly up to 100x faster for some datasets.
- memory consumption is way down
- removed the time-varying component from `AalenAdditiveFitter`. This will return in a future release.
- new `print_summary`
- `weights_col` is added
- `nn_cumulative_hazard` is removed (may add back)
- some plotting improvemnts to `plotting.plot_lifetimes`

- More `CoxPHFitter` performance improvements. Up to a 40% reduction vs 0.16.2 for some datasets.