crepes 0.2.0

Features

• Modified sigma_knn to allow for calculating difficulty in three ways; using distances only, using standard deviation of the target and using the absolute residuals of the nearest neighbors.
• Added sigma_knn_oob in crepes.fillings
• Renamed the performance metric efficiency to eff_mean (mean efficiency) and added eff_med (median efficiency) to the evaluate method in ConformalRegressor and ConformalPredictiveSystem
• Added warning messages for the case that the calibration set is too small for the specified confidence level or lower/higher percentiles [thanks to Geethen for highlighting this]
• Added examples in comments
• The documentation has been generated using Sphinx and resides in crepes.readthedocs.io

Fixes

• Extended type checks to include NumPy floats and integers [thanks to patpizio for pointing this out]
• Corrected a bug in the assignment of min/max values for Mondrian conformal predictive systems
• The Jupyter notebook with examples has been updated, changed name to crepes_nb.ipynb and moved to the docs folder
• Changed the default k to 25 in sigma_knn