Xgboost

* Faster tree construction module
- Allows subsample columns during tree construction via bst:col_samplebytree=ratio
* Support for boosting from initial predictions
* Experimental version of LambdaRank
* Linear booster is now parallelized, using parallel coordinated descent.
* Add [Code Guide](src/README.md) for customizing objective function and evaluation
* Add R module

- Updated XGBoost to 1.0.0

- Fixed `Could not find XGBoost` error on some Linux platforms
- Fixed `SignalException` on Windows

- Prefer `XGBoost` over `Xgb`
- Changed to Apache 2.0 license to match XGBoost
- Added shared libraries
- Added support for booster attributes

- Added support for missing values
- Fixed Daru training and prediction
- Fixed error with JRuby

- Friendlier message when XGBoost not found
- Free memory when objects are destroyed
- Added `Ranker`
- Added early stopping to Scikit-Learn API