This release improved a number of areas:
* Huge performance improvements, especially if categorical variables were being imputed. These come from not predicting candidate data if we don't need to, using a much faster neighbors search, using numpy internally for indexing instead of pandas, and others.
* Ability to tune parameters of models, and use best parameters for mice.
* Improvements to code layout - got rid of ImputationSchema.
* Raw data is now stored as a numpy array to save space and improve indexing.
* Numpy arrays can be imputed, if you want to avoid pandas.
* Options of multiple build-in mean matching functions.
* Mean matching functions can handle most lightgbm objectives.