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nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

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  • Noguchi, Kimihiro
  • Gel, Yulia R.
  • Brunner, Edgar
  • Konietschke, Frank

Abstract

Longitudinal data from factorial experiments frequently arise in various fields of study, ranging from medicine and biology to public policy and sociology. In most practical situations, the distribution of observed data is unknown and there may exist a number of atypical measurements and outliers. Hence, use of parametric and semi-parametric procedures that impose restrictive distributional assumptions on observed longitudinal samples becomes questionable. This, in turn, has led to a substantial demand for statistical procedures that enable us to accurately and reliably analyze longitudinal measurements in factorial experiments with minimal conditions on available data, and robust nonparametric methodology offering such a possibility becomes of particular practical importance. In this article, we introduce a new R package nparLD which provides statisticians and researchers from other disciplines an easy and user-friendly access to the most up-to-date robust rank-based methods for the analysis of longitudinal data in factorial settings. We illustrate the implemented procedures by case studies from dentistry, biology, and medicine.

Suggested Citation

  • Noguchi, Kimihiro & Gel, Yulia R. & Brunner, Edgar & Konietschke, Frank, 2012. "nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i12).
    • Handle: RePEc:jss:jstsof:v:050:i12
    DOI: http://hdl.handle.net/10.18637/jss.v050.i12
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    References listed on IDEAS

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    1. Konietschke, F. & Bathke, A.C. & Hothorn, L.A. & Brunner, E., 2010. "Testing and estimation of purely nonparametric effects in repeated measures designs," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1895-1905, August.
    2. A. Schörgendorfer & L. V. Madden & A. C. Bathke, 2011. "Choosing appropriate covariance matrices in a nonparametric analysis of factorials in block designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(4), pages 833-850, January.
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    Cited by:

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    2. Wyłupek, Grzegorz, 2023. "A nonparametric test for paired data," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    3. Hasler Mario, 2013. "Multiple Contrasts for Repeated Measures," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-13, July.
    4. Burchett, Woodrow W. & Ellis, Amanda R. & Harrar, Solomon W. & Bathke, Arne C., 2017. "Nonparametric Inference for Multivariate Data: The R Package npmv," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i04).
    5. Ted Maldonado & James R M Goen & Michael J Imburgio & Sydney M Eakin & Jessica A Bernard, 2019. "Single session high definition transcranial direct current stimulation to the cerebellum does not impact higher cognitive function," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
    6. Friedrich, Sarah & Konietschke, Frank & Pauly, Markus, 2017. "A wild bootstrap approach for nonparametric repeated measurements," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 38-52.
    7. Umlauft, Maria & Placzek, Marius & Konietschke, Frank & Pauly, Markus, 2019. "Wild bootstrapping rank-based procedures: Multiple testing in nonparametric factorial repeated measures designs," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 176-192.
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    10. Violette Chiara & Felipe Ramon Portugal & Raphael Jeanson, 2019. "Social intolerance is a consequence, not a cause, of dispersal in spiders," PLOS Biology, Public Library of Science, vol. 17(7), pages 1-27, July.
    11. Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
    12. Francisco Barbosa Escobar & Carlos Velasco & Kosuke Motoki & Derek Victor Byrne & Qian Janice Wang, 2021. "The temperature of emotions," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-28, June.

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