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>.
https://anaconda.org/r/r-rrscale/badges/version.svg
https://anaconda.org/r/r-rrscale/badges/latest_release_date.svg
https://anaconda.org/r/r-rrscale/badges/latest_release_relative_date.svg
https://anaconda.org/r/r-rrscale/badges/platforms.svg
https://anaconda.org/r/r-rrscale/badges/license.svg
https://anaconda.org/r/r-rrscale/badges/downloads.svg