box_cox

<ul>
<li>T the transformation with arguments Y, the data, lambda the parameter, and boolean inverse to calculate inverse transformation</li>
<li>T_deriv the transformation with arguments Y, the data, lambda the parameter.</li>
</ul>

datasets

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>.

Gregory Hunt

rrscale

Robust Re-Scaling to Better Recover Latent Effects in Data

Johann Gagnon-Bartsch

box_cox function

An object of class <code>list</code> of length 2.

box_cox: Traditional box-cox power transformation. Accepts one real parameter

Description

Usage

Arguments

Format