It provides access to multiple state-of-art ensemble imbalanced learning (EIL) methods, visualizer, and utility functions for dealing with the class imbalance problem. These ensemble methods include resampling-based, e.g., under/over-sampling, and reweighting-based ones, e.g., cost-sensitive learning. Beyond the implementation, we also extend conventional binary EIL algorithms with new functionalities like multi-class support and resampling scheduler, thereby enabling them to handle more complex tasks. The package was developed under a simple, well-documented API design follows that of scikit-learn for increased ease of use.