Robust Re-Scaling to Better Recover Latent Effects in Data
Arc-hyperbolic-sine transformation
Traditional box-cox power transformation. Accepts one real parameter
Exponential of the tranditional box-cox transformation
A generalized box-cox transformation that can handle negative data
Box-cox transformation with a shift of 1 added to the data
Box-cox transformation with the data shifted so that it is positive
Box-cox transformation of shifted variable
Centers the data column-wise
Calculate the geometric mean
List possible transformations
Log of the traditional box-cox transformation
Simple power transformation
Re-scale a data matrix
The completed SVD
Winsorizes the 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>.