Version 0.9.0

New Features

• Added evaluate.permutation_test, a permutation test for hypothesis testing (or A/B testing) to test if two samples come from the same distribution. Or in other words, a procedure to test the null hypothesis that that two groups are not significantly different (e.g., a treatment and a control group). (#250)
• Added 'leverage' and 'conviction as evaluation metrics to the frequent_patterns.association_rules function. (#246 & #247)
• Added a loadings_ attribute to PrincipalComponentAnalysis to compute the factor loadings of the features on the principal components. (#251)
• Allow grid search over classifiers/regressors in ensemble and stacking estimators. (#259)
• New make_multiplexer_dataset function that creates a dataset generated by a n-bit Boolean multiplexer for evaluating supervised learning algorithms. (#263)
• Added a new BootstrapOutOfBag class, an implementation of the out-of-bag bootstrap to evaluate supervised learning algorithms. (#265)
• The parameters for StackingClassifier, StackingCVClassifier, StackingRegressor, StackingCVRegressor, and EnsembleVoteClassifier can now be tuned using scikit-learn's GridSearchCV (#254 via James Bourbeau)

Changes

• The 'support' column returned by frequent_patterns.association_rules was changed to compute the support of "antecedant union consequent", and new antecedant support' and 'consequent support' column were added to avoid ambiguity. (#245)
• Allow the OnehotTransactions to be cloned via scikit-learn's clone function, which is required by e.g., scikit-learn's FeatureUnion or GridSearchCV (via Iaroslav Shcherbatyi). (#249)

Bug Fixes

• Fix issues with self._init_time parameter in _IterativeModel subclasses. (#256)
• Fix imprecision bug that occurred in plot_ecdf when run on Python 2.7. (264)
• The vectors from SVD in PrincipalComponentAnalysis are no being scaled so that the eigenvalues via solver='eigen' and solver='svd' now store eigenvalues that have the same magnitudes. (#251)