Many experiments can be modeled by a factorial design which allows statistical analysis of main factors and their interactions. A plethora of parametric inference procedures have been developed, for instance based on normality and additivity of the effects. However, often, it is not reasonable to assume a parametric model, or even normality, and effects may not be expressed well in terms of location shifts. In these situations, the use of a fully nonparametric model may be advisable. Nevertheless, until very recently, the straightforward application of nonparametric methods in complex designs has been hampered by the lack of a comprehensive R package. This gap has now been closed by the novel R-package rankFD that implements current state of the art nonparametric ranking methods for the analysis of factorial designs. In this paper, we describe its use, along with detailed interpretations of the results.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-029.zip
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For attribution, please cite this work as
Konietschke & Brunner, "rankFD: An R Software Package for Nonparametric Analysis of General Factorial Designs", The R Journal, 2023
BibTeX citation
@article{RJ-2023-029,
author = {Konietschke, Frank and Brunner, Edgar},
title = {rankFD: An R Software Package for Nonparametric Analysis of General Factorial Designs},
journal = {The R Journal},
year = {2023},
note = {https://doi.org/10.32614/RJ-2023-029},
doi = {10.32614/RJ-2023-029},
volume = {15},
issue = {1},
issn = {2073-4859},
pages = {142-158}
}