Lifelines

- some quality of life improvements for working with `CoxTimeVaryingFitter` including new `predict_` methods.

- fixed bug with using weights and strata in `CoxPHFitter`
- fixed bug in using non-integer weights in `KaplanMeierFitter`
- Performance optimizations in `CoxPHFitter` for up to 40% faster completion of `fit`.
- even smarter `step_size` calculations for iterative optimizations.
- simple code optimizations & cleanup in specific hot spots.
- Performance optimizations in `AalenAdditiveFitter` for up to 50% faster completion of `fit` for large dataframes, and up to 10% faster for small dataframes.

- adding `plot_covariate_groups` to `CoxPHFitter` to visualize what happens to survival as we vary a covariate, all else being equal.
- `utils` functions like `qth_survival_times` and `median_survival_times` now return the transpose of the DataFrame compared to previous version of lifelines. The reason for this is that we often treat survival curves as columns in DataFrames, and functions of the survival curve as index (ex: KaplanMeierFitter.survival_function_ returns a survival curve _at_ time _t_).
- `KaplanMeierFitter.fit` and `NelsonAalenFitter.fit` accept a `weights` vector that can be used for pre-aggregated datasets. See this [issue](https://github.com/CamDavidsonPilon/lifelines/issues/396).
- Convergence errors now return a custom `ConvergenceWarning` instead of a `RuntimeWarning`
- New checks for complete separation in the dataset for regressions.

- removes `is_significant` and `test_result` from `StatisticalResult`. Users can instead choose their significance level by comparing to `p_value`. The string representation of this class has changed aswell.
- `CoxPHFitter` and `AalenAdditiveFitter` now have a `score_` property that is the concordance-index of the dataset to the fitted model.
- `CoxPHFitter` and `AalenAdditiveFitter` no longer have the `data` property. It was an _almost_ duplicate of the training data, but was causing the model to be very large when serialized.
- Implements a new fitter `CoxTimeVaryingFitter` available under the `lifelines` namespace. This model implements the Cox model for time-varying covariates.
- Utils for creating time varying datasets available in `utils`.
- less noisy check for complete separation.
- removed `datasets` namespace from the main `lifelines` namespace
- `CoxPHFitter` has a slightly more intelligent (barely...) way to pick a step size, so convergence should generally be faster.
- `CoxPHFitter.fit` now has accepts a `weight_col` kwarg so one can pass in weights per observation. This is very useful if you have many subjects, and the space of covariates is not large. Thus you can group the same subjects together and give that observation a weight equal to the count. Altogether, this means a much faster regression.

- removes `include_likelihood` from `CoxPHFitter.fit` - it was not slowing things down much (empirically), and often I wanted it for debugging (I suppose others do too). It's also another exit condition, so we many exit from the NR iterations faster.
- added `step_size` param to `CoxPHFitter.fit` - the default is good, but for extremely large or small datasets this may want to be set manually.
- added a warning to `CoxPHFitter` to check for complete seperation: https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/
- Additional functionality to `utils.survival_table_from_events` to bin the index to make the resulting table more readable.

- No longer support matplotlib 1.X
- Adding `times` argument to `CoxPHFitter`'s `predict_survival_function` and `predict_cumulative_hazard` to predict the estimates at, instead uses the default times of observation or censorship.
- More accurate prediction methods parametrics univariate models.