normalr: Normalisation of Multiple Variables in Large-Scale Datasets

The robustness of many of the statistical techniques, such as factor analysis, applied in the social sciences rests upon the assumption of item-level normality. However, when dealing with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964) <> provides an optimal transformation for non-normal variables. Yet, for large datasets of continuous variables, its application in current software programs is cumbersome with analysts having to take several steps to normalise each variable. We present an R package 'normalr' that enables researchers to make convenient optimal transformations of multiple variables in datasets. This R package enables users to quickly and accurately: (1) anchor all of their variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, (3) apply each unique exponent to its variable, (4) rescale resultant values to within their original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, kurtosis, and other inferential assessments of normality.

Version: 1.0.0
Depends: R (≥ 3.3.0)
Imports: MASS, parallel, purrr, magrittr, rlang, shiny
Suggests: testthat, covr
Published: 2018-03-30
DOI: 10.32614/CRAN.package.normalr
Author: Kevin Chang [aut, cre], Matthew Courtney [aut]
Maintainer: Kevin Chang <k.chang at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
Citation: normalr citation info
Materials: README NEWS
CRAN checks: normalr results


Reference manual: normalr.pdf


Package source: normalr_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): normalr_1.0.0.tgz, r-oldrel (arm64): normalr_1.0.0.tgz, r-release (x86_64): normalr_1.0.0.tgz, r-oldrel (x86_64): normalr_1.0.0.tgz
Old sources: normalr archive


Please use the canonical form to link to this page.