Scikit-survival

This release adds support for numpy 1.14 and pandas up to 0.23. In addition, the new class `sksurv.util.Surv` makes it easier to construct a structured array from numpy arrays, lists, or a pandas data frame.

Changes:

- Support numpy 1.14 and pandas 0.22, 0.23 (36).
- Enable support for cvxopt with Python 3.5+ on Windows (requires cvxopt >=1.1.9).
- Add `max_iter` parameter to `sksurv.svm.MinlipSurvivalAnalysis` and `sksurv.svm.HingeLossSurvivalSVM`.
- Fix score function of `sksurv.svm.NaiveSurvivalSVM` to use concordance index.
- `sksurv.linear_model.CoxnetSurvivalAnalysis` now throws an exception if coefficients get too large (47).
- Add `sksurv.util.Surv` class to ease constructing a structured array (26).

This release adds support for scikit-learn 0.19 and pandas 0.21. In turn, support for older versions is dropped, namely Python 3.4, scikit-learn 0.18, and pandas 0.18.

This release adds *sksurv.linear_model.CoxnetSurvivalAnalysis* which implements an efficient algorithm to fit Cox's proportional hazards model with LASSO, ridge, and elastic net penalty. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.

This release adds `predict_survival_function` and `predict_cumulative_hazard_function` to `sksurv.linear_model.CoxPHSurvivalAnalysis`, which return the survival function and cumulative hazard function using Breslow's estimator.

Moreover, it fixes a build error on Windows (3) and adds the `sksurv.preprocessing.OneHotEncoder` class, which can be used in a [scikit-learn pipeline](http://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html).

This release adds support for Python 3.6, and pandas 0.19 and 0.20.