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Abstract
Analysts often transform their models’ parameter estimates to report more meaningful and interpretable quantities of interest. This article presents a simple conceptual framework to describe a vast array of estimands which are reported under imprecise and inconsistent terminology across disciplines: predictions, marginal predictions, marginal means, marginal effects, conditional effects, slopes, contrasts, risk ratios, etc. We introduce marginaleffects
, an R package which offers a simple and powerful interface to compute all of those quantites, and to conduct hypothesis tests on them. marginaleffects
is lightweight; extensible; it works well in combination with other R packages; and it supports over 70 classes of models, including Generalized Linear, Generalized Additive, Mixed Effects, and Bayesian models.