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>.

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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