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rrscale: Robust Re-Scaling to Better Recover Latent Effects in Data
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.
Getting started
Browse package contents
Package details |
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| Author | Gregory Hunt [aut, cre], Johann Gagnon-Bartsch [aut] |
| Maintainer | Gregory Hunt <ghunt@wm.edu> |
| License | GPL-3 |
| Version | 1.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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