Lifelines

- reorganized lifelines directories:
- moved test files out of main directory.
- moved `utils.py` into it's own directory.
- moved all estimators `fitters` directory.
- added a `at_risk` column to the output of `group_survival_table_from_events` and `survival_table_from_events`
- added sample size and power calculations for statistical tests. See `lifeline.statistics. sample_size_necessary_under_cph` and `lifelines.statistics. power_under_cph`.
- fixed a bug when using KaplanMeierFitter for left-censored data.

- addition of a l2 `penalizer` to `CoxPHFitter`.
- dropped Fortran implementation of efficient Python version. Lifelines is pure python once again!
- addition of `strata` keyword argument to `CoxPHFitter` to allow for stratification of a single or set of
categorical variables in your dataset.
- `datetimes_to_durations` now accepts a list as `na_values`, so multiple values can be checked.
- fixed a bug in `datetimes_to_durations` where `fill_date` was not properly being applied.
- Changed warning in `datetimes_to_durations` to be correct.
- refactor each fitter into it's own submodule. For now, the tests are still in the same file. This will also *not* break the API.

- allow for multiple fitters to be passed into `k_fold_cross_validation`.
- statistical tests in `lifelines.statistics`. now return a `StatisticalResult` object with properties like `p_value`, `test_results`, and `summary`.
- fixed a bug in how log-rank statistical tests are performed. The covariance matrix was not being correctly calculated. This resulted in slightly different p-values.
- `WeibullFitter`, `ExponentialFitter`, `KaplanMeierFitter` and `BreslowFlemingHarringtonFitter` all have a `conditional_time_to_event_` property that measures the median duration remaining until the death event, given survival up until time t.

- addition of `median_` property to `WeibullFitter` and `ExponentialFitter`.
- `WeibullFitter` and `ExponentialFitter` will use integer timelines instead of float provided by `linspace`. This is
so if your work is to sum up the survival function (for expected values or something similar), it's more difficult to
make a mistake.

- Inclusion of the univariate fitters `WeibullFitter` and `ExponentialFitter`.
- Removing `BayesianFitter` from lifelines.
- Added new penalization scheme to AalenAdditiveFitter. You can now add a smoothing penalizer
that will try to keep subsequent values of a hazard curve close together. The penalizing coefficient
is `smoothing_penalizer`.
- Changed `penalizer` keyword arg to `coef_penalizer` in AalenAdditiveFitter.
- new `ridge_regression` function in `utils.py` to perform linear regression with l2 penalizer terms.
- Matplotlib is no longer a mandatory dependency.
- `.predict(time)` method on univariate fitters can now accept a scalar (and returns a scalar) and an iterable (and returns a numpy array)
- In `KaplanMeierFitter`, `epsilon` has been renamed to `precision`.

- New API for `CoxPHFitter` and `AalenAdditiveFitter`: the default arguments for `event_col` and `duration_col`. `duration_col` is now mandatory, and `event_col` now accepts a column, or by default, `None`, which assumes all events are observed (non-censored).
- Fix statistical tests.
- Allow negative durations in Fitters.
- New API in `survival_table_from_events`: `min_observations` is replaced by `birth_times` (default `None`).
- New API in `CoxPHFitter` for summary: `summary` will return a dataframe with statistics, `print_summary()` will print the dataframe (plus some other statistics) in a pretty manner.
- Adding "At Risk" counts option to univariate fitter `plot` methods, `.plot(at_risk_counts=True)`, and the function `lifelines.plotting.add_at_risk_counts`.
- Fix bug Epanechnikov kernel.