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 |
DOI: | 10.32614/CRAN.package.rrscale |
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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