Multiscale Graph Correlation

Multiscale Graph Correlation (MGC, pronounced "magic") is a nonparametric approach for independence and k-sample testing.

Currently available in Python, with many other standard independence and k-sample tests included. Development versions in both R (github and CRAN) and MATLAB support only MGC.

Publications
  1. J. Xiong et al. Graph Independence Testing. arXiv, 2019.
  2. C. Shen et al. Discovering and Deciphering Relationships Across Disparate Data Modalities. eLife, 2019.
  3. C. Shen, C. E. Priebe and J. T. Vogelstein. From Distance Correlation to Multiscale Graph Correlation. Journal of the American Statistical Association, 2018.
  4. Y. Lee, C. Shen and J. T. Vogelstein. Network Dependence Testing via Diffusion Maps and Distance-Based Correlations. Biometrika, 2018.
  5. C. Shen and J. T. Vogelstein. The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing. arXiv, 2018.
  6. S. Wang et al. Signal Subgraph Estimation Via Vertex Screening. arXiv, 2018.
  7. G. Franca, M. L. Rizzo and J. T. Vogelstein. Kernel k-Groups via Hartigan's Method. arXiv, 2017.