Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Woodrow W. Burchett, Amanda R. Ellis, Solomon W. Harrar, Arne C. Bathke
Title: Nonparametric Inference for Multivariate Data: The R Package npmv
Abstract: We introduce the R package npmv that performs nonparametric inference for the comparison of multivariate data samples and provides the results in easy-to-understand, but statistically correct, language. Unlike in classical multivariate analysis of variance, multivariate normality is not required for the data. In fact, the different response variables may even be measured on different scales (binary, ordinal, quantitative). p values are calculated for overall tests (permutation tests and F approximations), and, using multiple testing algorithms which control the familywise error rate, significant subsets of response variables and factor levels are identified. The package may be used for low- or highdimensional data with small or with large sample sizes and many or few factor levels.

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Paper: Nonparametric Inference for Multivariate Data: The R Package npmv     Download PDF (Downloads: 10332)
Supplements:
npmv_2.4.0.tar.gz: R source package Download (Downloads: 186; 13KB)
v76i04.R: R replication code Download (Downloads: 232; 932B)
DOI: 10.18637/jss.v076.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.