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.

Documentation

Manual: normalr.pdf
Vignette: None available.

Maintainer: Kevin Chang <k.chang at auckland.ac.nz>

Author(s): Kevin Chang*, Matthew Courtney*

Install package and any missing dependencies by running this line in your R console:

install.packages("normalr")

Depends R (>= 3.3.0)
Imports MASS, parallel, purrr, magrittr, ddR, shiny
Suggests testthat, covr
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Package normalr
Materials
URL https://github.com/kcha193/normalr
Task Views
Version 0.0.3
Published 2017-01-17
License GPL
BugReports https://github.com/kcha193/normalr/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks normalr check results
Package source normalr_0.0.3.tar.gz