Lifelines

- corrected bug that was returning the wrong baseline survival and hazard values in `CoxPHFitter` when `normalize=True`.
- removed `normalize` kwarg in `CoxPHFitter`. This was causing lots of confusion for users, and added code complexity. It's really nice to be able to remove it.
- correcting column name in `CoxPHFitter.baseline_survival_`
- `CoxPHFitter.baseline_cumulative_hazard_` is always centered, to mimic R's `basehaz` API.
- new `predict_log_partial_hazards` to `CoxPHFitter`

- adding `plot_loglogs` to `KaplanMeierFitter`
- added a (correct) check to see if some columns in a dataset will cause convergence problems.
- removing `flat` argument in `plot` methods. It was causing confusion. To replicate it, one can set `ci_force_lines=True` and `show_censors=True`.
- adding `strata` keyword argument to `CoxPHFitter` on initialization (ex: `CoxPHFitter(strata=['v1', 'v2'])`. Why? Fitters initialized with `strata` can now be passed into `k_fold_cross_validation`, plus it makes unit testing `strata` fitters easier.
- If using `strata` in `CoxPHFitter`, access to strata specific baseline hazards and survival functions are available (previously it was a blended valie). Prediction also uses the specific baseline hazards/survivals.
- performance improvements in `CoxPHFitter` - should see at least a 10% speed improvement in `fit`.

- deprecates Pandas versions before 0.18.
- throw an error if no admissable pairs in the c-index calculation. Previously a NaN was returned.

- add two summary functions to Weibull and Exponential fitter, solves 224

- new prediction function in `CoxPHFitter`, `predict_log_hazard_relative_to_mean`, that mimics what R's `predict.coxph` does.
- removing the `predict` method in CoxPHFitter and AalenAdditiveFitter. This is because the choice of `predict_median` as a default was causing too much confusion, and no other natual choice as a default was available. All other `predict_` methods remain.
- Default predict method in `k_fold_cross_validation` is now `predict_expectation`

- supports matplotlib 1.5.
- introduction of a param `nn_cumulative_hazards` in AalenAdditiveModel's `__init__` (default True). This parameter will truncate all non-negative cumulative hazards in prediction methods to 0.
- bug fixes including:
- fixed issue where the while loop in `_newton_rhaphson` would break too early causing a variable not to be set properly.
- scaling of smooth hazards in NelsonAalenFitter was off by a factor of 0.5.