mgc: Multiscale Graph Correlation

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 <ericwb95 at gmail.com>
License:	GPL-2
URL:	https://github.com/neurodata/r-mgc
NeedsCompilation:	yes
CRAN checks:	mgc results

Documentation:

Reference manual:	mgc.pdf
Vignettes:	discriminability mgc class_sims reg_sims

Downloads:

Package source:	mgc_2.0.2.tar.gz
Windows binaries:	r-devel: mgc_2.0.2.zip, r-release: mgc_2.0.2.zip, r-oldrel: mgc_2.0.2.zip
macOS binaries:	r-release (arm64): mgc_2.0.2.tgz, r-oldrel (arm64): mgc_2.0.2.tgz, r-release (x86_64): mgc_2.0.2.tgz, r-oldrel (x86_64): mgc_2.0.2.tgz
Old sources:	mgc archive

Reverse dependencies:

Reverse imports:

coveR2, mineSweepR

Linking:

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