rrscale: Robust Re-Scaling to Better Recover Latent Effects in Data (original) (raw)
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 |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Citation: | rrscale citation info |
| CRAN checks: | rrscale results |
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