## Multiscale Graph Correlation

Multiscale Graph Correlation (MGC, pronounced "magic") is a nonparametric approach for independence and k-sample testing. The MGC framework has been extended to include approaches for reliability and reproducibility, including Discriminability.

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.

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

###### Publications

- E. W. Bridgeford, S. Wang, Z. Yang, Z. Wang, T. Xu, C. Craddock, J. Dey, G. Kiar, W. Gray-Roncal, C. Coulantoni, C. Douville, C. E. Priebe, B. Caffo, M. Milham, X. Zuo, (CoRR), and J. T. Vogelstein. Big Data Reproducibility: Applications in Brain Imaging and Genomics.
*bioRxiv*, 2020. - R. Mehta, C. Shen, T. Xu, and J. T. Vogelstein. A Consistent Independence Test for Multivariate Time-Series.
*arxiv*, 2019. - Y. Lee, C. Shen, C. E. Priebe, and J. T. Vogelstein. Network dependence testing via diffusion maps and distance-based correlations.
*Biometrika*, 2019. - J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. Graph Independence Testing.
*arXiv*, 2019. - 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. - C. Shen, C. E. Priebe, and J. T. Vogelstein. From Distance Correlation to Multiscale Graph Correlation.
*Journal of the American Statistical Association*, 2018. - S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. Signal Subgraph Estimation Via Vertex Screening.
*arXiv*, 2018.