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Team Members:
  • Richard Guo
  • Ronan Perry
  • Gavin Mischler
  • Theodore Lee
  • Alexander Chang
  • Arman Koul
  • Cameron Franz
  • Joshua Vogelstein, PhD
  • Benjamin Pedigo
  • Jaewon Chung


In many datasets, there are multiple measurement modalities of the same subject, i.e. multiple X matrices (views) for the same class label vector y. For example, a set of diseased and healthy patients in a neuroimaging study may undergo both CT and MRI scans. Traditional methods for inference and analysis are often poorly suited to account for multiple views of the same subject as they cannot account for complementing views that hold different statistical properties. While single-view methods are consolidated in well-documented packages such as scikit-learn, there is no equivalent for multi-view methods. In this package, we provide a well-documented and tested collection of utilities and algorithms designed for the processing and analysis of Multiview data sets. Our Python package is pip-installable, open source, and free to use.

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