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.

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Description file content

Package
mgc
Type
Package
Title
Multiscale Graph Correlation
Version
1.0.1
Date
2018-04-12
Maintainer
Eric Bridgeford
Description
Multiscale Graph Correlation (MGC) is a framework developed by Shen et al. (2017) 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.
Depends
R (>= 3.4.0)
Imports
stats, SDMTools, MASS
URL
Suggests
testthat, ggplot2, reshape2, knitr, rmarkdown
License
GPL-2
Encoding
UTF-8
LazyData
true
RoxygenNote
6.0.1
VignetteBuilder
knitr
NeedsCompilation
no
Packaged
2018-04-13 14:09:15 UTC; eric
Author
Eric Bridgeford [aut, cre], Censheng Shen [aut], Shangsi Wang [aut], Joshua Vogelstein [ths]
Repository
CRAN
Date/Publication
2018-04-13 21:17:05 UTC

install.packages('mgc')

1.0.1

6 days ago

https://github.com/neurodata/mgc

Eric Bridgeford

GPL-2

Depends on

R (>= 3.4.0)

Imports

stats, SDMTools, MASS

Suggests

testthat, ggplot2, reshape2, knitr, rmarkdown

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