v0.3.0

• Added some accessors to give access to redflag functions directly from pandas.Series objects, via an 'accessor'. For example, for a Series s, one can call minority_classes = s.redflag.minority_classes() instead of redflag.minority_classes(s). Other functions include imbalance_degree(), dummy_scores() (see below). Probably not very useful yet, but future releases will add some reporting functions that wrap multiple Redflag functions. This is an experimental feature and subject to change.
• Added a Series accessor report() to perform a range of tests and make a small text report suitable for printing. Access for a Series s like s.redflag.report(). This is an experimental feature and subject to change.
• Added new documentation page for the Pandas accessor.
• Added redflag.target.dummy_classification_scores(), redflag.target.dummy_regression_scores(), which train a dummy (i.e. naive) model and compute various relevant scores (MSE and R2 for regression, F1 and ROC-AUC for classification tasks). Additionally, both most_frequent and stratified strategies are tested for classification tasks; only the mean strategy is employed for regression tasks. The helper function redflag.target.dummy_scores() tries to guess what kind of task suits the data and calls the appropriate function.
• Moved redflag.target.update_p() to redflag.utils.
• Added is_imbalanced() to return a Boolean depending on a threshold of imbalance degree. Default threshold is 0.5 but the best value is up for debate.
• Removed utils.has_low_distance_stdev.