Package: normalp
Version: 0.6.8
Date: 2007-10-01
Title: Package for exponential power distributions (EPD)
Author: Angelo M. Mineo <elio.mineo@dssm.unipa.it>
Maintainer: Angelo M. Mineo <elio.mineo@dssm.unipa.it>
Depends: R (>= 1.5.0)
Description: A collection of utilities refered to
exponential power distributions, also known as General Error Distribution
License: GPL-2
URL: http://www.r-project.org; http://dssm.unipa.it/elio
Packaged: Tue Jan 27 14:10:34 2009; elio

Author: Angelo M. Mineo <angelo.mineo@unipa.it>
Maintainer: Angelo M. Mineo <angelo.mineo@unipa.it>
Description: A collection of utilities referred to exponential power
distributions, also known as General Error Distribution

Title: Routines for Exponential Power Distribution
Description: Collection of utilities referred to Exponential Power distribution,
also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4).

also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4)

Description: A collection of utilities referred to Exponential Power distribution, also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4).
Packaged: 2020-02-13 16:38:13 UTC; luigiaugugliaro

Packaged: 2020-02-14 09:42:29 UTC; luigiaugugliaro

Title: Normalisation of Multiple Variables in Large-Scale Datasets
person("Matthew", "Courtney", email = "matthew.courtney@unimelb.edu.au", role = c("aut")))
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)
<http://www.jstor.org/stable/2984418> 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.
Imports: MASS, parallel, purrr, magrittr, ddR