CRAN/E | rrscale

rrscale

Robust Re-Scaling to Better Recover Latent Effects in Data

Installation

About

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.

Citation rrscale citation info

Key Metrics

Version 1.0
R ≥ 3.5.0
Published 2020-05-26 1438 days ago
Needs compilation? no
License GPL-3
CRAN checks rrscale results

Downloads

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Maintainer

Maintainer

Gregory Hunt

ghunt@wm.edu

Authors

Gregory Hunt

aut / cre

Johann Gagnon-Bartsch

aut

Material

Reference manual
Package source

Vignettes

Ragged RR
Basic Rescaling

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

rrscale archive

Depends

R ≥ 3.5.0

Imports

DEoptim
nloptr
abind

Suggests

knitr
rmarkdown
testthat
ggplot2
reshape2