Version 0.1.0

The first release of the algorithm. It supports the following functionalities for learning descriptive rules:

• The quality of rules is assessed by comparing the predictions of the current model to the ground truth in terms of the Pearson correlation coefficient.
• When learning a new rule, random samples of the features may be used.
• Hyper-parameters that provide control over the specificity/generality of rules are available.
• The algorithm can natively handle numerical, ordinal and nominal features (without the need for pre-processing techniques such as one-hot encoding).
• The algorithm is able to deal with missing feature values, i.e., occurrences of NaN in the feature matrix.