Lifelines

- `LogNormalFitter` is a new univariate fitter you can use.
- `WeibullFitter` now correctly returns the confidence intervals (previously returned only NaNs)
- `WeibullFitter.print_summary()` displays p-values associated with its parameters not equal to 1.0 - previously this was (implicitly) comparing against 0, which is trivially always true (the parameters must be greater than 0)
- `ExponentialFitter.print_summary()` displays p-values associated with its parameters not equal to 1.0 - previously this was (implicitly) comparing against 0, which is trivially always true (the parameters must be greater than 0)
- `ExponentialFitter.plot` now displays the cumulative hazard, instead of the survival function. This is to make it easier to compare to `WeibullFitter` and `LogNormalFitter`
- Univariate fitters' `cumulative_hazard_at_times`, `hazard_at_times`, `survival_function_at_times` return pandas Series now (use to be numpy arrays)
- remove `alpha` keyword from all statistical functions. This was never being used.
- Gone are astericks and dots in `print_summary` functions that represent signficance thresholds.
- In models' `summary` (including `print_summary`), the `log(p)` term has changed to `-log2(p)`. This is known as the s-value. See https://lesslikely.com/statistics/s-values/
- introduce new statistical tests between univariate datasets: `survival_difference_at_fixed_point_in_time_test`,...
- new warning message when Cox models detects possible non-unique solutions to maximum likelihood.
- Generally: clean up lifelines exception handling. Ex: catch `LinAlgError: Matrix is singular.` and report back to the user advice.

- more bugs in `plot_covariate_groups` fixed when using non-numeric strata.

- 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`.