CRAN/E | normalr

normalr

Normalisation of Multiple Variables in Large-Scale Datasets

Installation

About

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.

Citation normalr citation info
github.com/kcha193/normalr
Bug report File report

Key Metrics

Version 1.0.0
R ≥ 3.3.0
Published 2018-03-30 2380 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks normalr results

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Maintainer

Maintainer

Kevin Chang

Authors

Kevin Chang

aut / cre

Matthew Courtney

aut

Material

README
NEWS
Reference manual
Package source

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

normalr archive

Depends

R ≥ 3.3.0

Imports

MASS
parallel
purrr
magrittr
rlang
shiny

Suggests

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covr