v0.4.0

• redflag can now be installed by the conda package and environment manager. To do so, use conda install -c conda-forge redflag.
• All of the sklearn components can now be instantiated with warn=False in order to trigger a ValueException instead of a warning. This allows you to build pipelines that will break if a detector is triggered.
• Added redflag.target.is_ordered() to check if a single-label categorical target is ordered in some way. The test uses a Markov chain analysis, applying chi-squared test to the transition matrix. In general, the Boolean result should only be used on targets with several classes, perhaps at least 10. Below that, it seems to give a lot of false positives.
• You can now pass groups to redflag.distributions.is_multimodal(). If present, the modality will be checked for each group, returning a Boolean array of values (one for each group). This allows you to check a feature partitioned by target class, for example.
• Added redflag.sklearn.MultimodalityDetector to provide a way to check for multimodal features. If y is passed and is categorical, it will be used to partition the data and modality will be checked for each class.
• Added redflag.sklearn.InsufficientDataDetector which checks that there are at least M² records (rows in X), where M is the number of features (i.e. columns) in X.
• Removed RegressionMultimodalDetector. Use MultimodalDetector instead.