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rrscale  

Robust Re-Scaling to Better Recover Latent Effects in Data
View on CRAN: Click here


Download and install rrscale package within the R console
Install from CRAN:
install.packages("rrscale")

Install from Github:
library("remotes")
install_github("cran/rrscale")

Install by package version:
library("remotes")
install_version("rrscale", "1.0")



Attach the package and use:
library("rrscale")
Maintained by
Gregory Hunt
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-03-13
Latest Update: 2020-05-26
Description:
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, .
How to cite:
Gregory Hunt (2019). rrscale: Robust Re-Scaling to Better Recover Latent Effects in Data. R package version 1.0, https://cran.r-project.org/web/packages/rrscale. Accessed 02 Nov. 2026.
Previous versions and publish date:
(2026-07-09 06:53), 0.1.3 (2019-03-13 15:00)
Other packages that cited rrscale R package
View rrscale citation profile
Other R packages that rrscale depends, imports, suggests or enhances
Complete documentation for rrscale
Functions, R codes and Examples using the rrscale R package
Some associated functions: asinh . box_cox . box_cox_exp . box_cox_negative . box_cox_plus1 . box_cox_plusmin . box_cox_shift . center . gm_mean . list_transformations . log_box_cox . power . rrscale . svdc . winsor . 
Some associated R codes: rrscale.R . subClosure.R . svd_na.R . transformations.R . util.R .  Full rrscale package functions and examples
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