doi:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.">

mgc: Multiscale Graph Correlation (original) (raw)

Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <doi:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.

Version: 2.0.2
Depends: R (≥ 3.4.0)
Imports: stats, MASS, abind, boot, energy, raster
Suggests: testthat (≥ 2.1.0), ggplot2, reshape2, knitr, rmarkdown
Published: 2020-06-23
DOI: 10.32614/CRAN.package.mgc
Author: Eric Bridgeford [aut, cre], Censheng Shen [aut], Shangsi Wang [aut], Joshua Vogelstein [ths]
Maintainer: Eric Bridgeford
License: GPL-2
URL: https://github.com/neurodata/r-mgc
NeedsCompilation: yes
CRAN checks: mgc results

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