Recent Publication in Journal of Statistical Software

 

 

 

Nonparametric Inference for Multivariate Data: The R package npmv

Amanda R. Ellis                            Woodrow W. Burchett

University of Kentucky                           University of Kentucky

Solomon W. Harrar                      Arne C. Bathke

University of Kentucky                          University of Kentucky

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 high- dimensional data with small or with large sample sizes and many or few factor levels. 

Find the full publication HERE

 

 

 

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