The robustness of many of the statistical techniques, such as factor analysis, applied in the social sciences rests upon the assumption of itemlevel normality. However, when dealing with real data, these assumptions are often not met. The BoxCox transformation (Box & Cox, 1964) <http://www.jstor.org/stable/2984418> provides an optimal transformation for nonnormal 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.
Package details 


Author  Kevin Chang [aut, cre], Matthew Courtney [aut] 
Maintainer  Kevin Chang <[email protected]> 
License  GPL 
Version  1.0.0 
URL  https://github.com/kcha193/normalr 
Package repository  View on CRAN 
Installation 
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