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, .


Reference manual

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1.0 by Gregory Hunt, a year ago

Browse source code at

Authors: Gregory Hunt [aut, cre] , Johann Gagnon-Bartsch [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports DEoptim, nloptr, abind

Suggests knitr, rmarkdown, testthat, ggplot2, reshape2

See at CRAN