mgc: Multiscale Graph Correlation

Multiscale Graph Correlation (MGC) is a framework developed by Shen et al. (2017) <arXiv:1609.05148> 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: 1.0.1
Depends: R (≥ 3.4.0)
Imports: stats, SDMTools, MASS
Suggests: testthat, ggplot2, reshape2, knitr, rmarkdown
Published: 2018-04-13
Author: Eric Bridgeford [aut, cre], Censheng Shen [aut], Shangsi Wang [aut], Joshua Vogelstein [ths]
Maintainer: Eric Bridgeford <ericwb95 at gmail.com>
License: GPL-2
URL: https://github.com/neurodata/mgc
NeedsCompilation: no
CRAN checks: mgc results

Downloads:

Reference manual: mgc.pdf
Vignettes: discriminability
mgc
sims
Package source: mgc_1.0.1.tar.gz
Windows binaries: r-devel: mgc_1.0.1.zip, r-release: mgc_1.0.1.zip, r-oldrel: not available
OS X binaries: r-release: mgc_1.0.1.tgz, r-oldrel: not available
Old sources: mgc archive

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