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normalr
View on CRAN: Click
here
Download and install normalr package within the R console
Install from CRAN:
install.packages("normalr")
Install from Github:
library("remotes")
install_github("cran/normalr") Install by package version:
library("remotes")
install_version("normalr", "1.0.0") Attach the package and use:
library("normalr")
Maintained by
Kevin Chang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-12-20
Latest Update: 2018-03-30
Description:
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.
How to cite:
Kevin Chang (2016). normalr: Normalisation of Multiple Variables in Large-Scale Datasets. R package version 1.0.0, https://cran.r-project.org/web/packages/normalr. Accessed 07 Oct. 2026.
Previous versions and publish date:
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Complete documentation for normalr
Functions, R codes and Examples using
the normalr R package
Some associated functions: getLambda . normalise . normaliseData . normalrShiny . testData .
Some associated R codes: getLambda.R . normalise.R . normaliseData.R . normalrShiny.R . testData.R . Full normalr package functions and examples
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