modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions

Abstract

Beyond the challenge of keeping up to date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. Here, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the {performance} package of the easystats ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude by reviewing the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency. A preprint of this paper is available at: https://doi.org/10.31234/osf.io/bu6nt.

Rémi Thériault

Postdoctoral Fellow (Social Psychology)

My research interests include social identities, implicit cognition, and prosociality.