hyppo (HYPothesis testing in PythOn, pronounced "hippo") is a Python library containing state-of-the-art algorithms for performing multivariate hypothesis tests. It is open-source and released under the MIT license.

Currently available in Python, Development versions in both R (github and CRAN) and MATLAB support only MGC. hyppo was formerly known as mgcpy.

Manuscript reproduction for the Discriminability paper can be found at Discriminability Reproduction.

  1. C. Shen, J. Chung, R. Mehta, T. Xu, and J. T. Vogelstein. Independence Testing for Temporal Data. arxiv, 2024.
  2. C. Shen, S. Panda, and J. T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv, 2023.
  3. C. Shen, S. Panda, and J. T. Vogelstein. The Chi-Square Test of Distance Correlation. Journal of Computational and Graphical Statistics, (ja)0:1–21, 2021.
  4. S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, . Mehta, C. Shen, and J. T. Vogelstein. hyppo: A Multivariate Hypothesis Testing Python Package. arXiv, 2021.
  5. G. Franca, M. Rizzo, and J. T. Vogelstein. Kernel k-Groups via Hartigan's Method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
  6. Y. Lee, C. Shen, C. E. Priebe, and J. T. Vogelstein. Network dependence testing via diffusion maps and distance-based correlations. Biometrika, 2019.
  7. J. T. Vogelstein, E. W. Bridgeford, Q. Wang, C. E. Priebe, M. Maggioni, and C. Shen. Discovering and deciphering relationships across disparate data modalities. eLife, 2019.
  8. C. Shen, C. E. Priebe, and J. T. Vogelstein. From Distance Correlation to Multiscale Graph Correlation. Journal of the American Statistical Association, 2018.
  9. S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. Signal Subgraph Estimation Via Vertex Screening. arXiv, 2018.