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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>.
| Version: | 1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | DEoptim, nloptr, abind |
| Suggests: | knitr, rmarkdown, testthat, ggplot2, reshape2 |
| Published: | 2020-05-26 |
| Author: | Gregory Hunt [aut, cre], Johann Gagnon-Bartsch [aut] |
| Maintainer: | Gregory Hunt <ghunt at wm.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Citation: | rrscale citation info |
| CRAN checks: | rrscale results |
| Reference manual: | rrscale.pdf |
| Vignettes: |
Ragged RR Basic Rescaling |
| Package source: | rrscale_1.0.tar.gz |
| Windows binaries: | r-devel: rrscale_1.0.zip, r-release: rrscale_1.0.zip, r-oldrel: rrscale_1.0.zip |
| macOS binaries: | r-release (arm64): rrscale_1.0.tgz, r-oldrel (arm64): rrscale_1.0.tgz, r-release (x86_64): rrscale_1.0.tgz, r-oldrel (x86_64): rrscale_1.0.tgz |
| Old sources: | rrscale archive |
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