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 |
| 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: mgc_1.0.1.zip |
| OS X binaries: | r-release: mgc_1.0.1.tgz, r-oldrel: mgc_1.0.1.tgz |
| Old sources: | mgc archive |
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