The `marginaleffects`

package for `R`

and `Python`

offers a single point of entry to easily interpret the results of over 100 classes of models, using a simple and consistent user interface. Its benefits include:

*Powerful:*It can compute and plot predictions; comparisons (contrasts, risk ratios, etc.); slopes; and conduct hypothesis and equivalence tests for over 100 different classes of models in`R`

.*Simple:*All functions share a simple and unified interface.*Documented*: Each function is thoroughly documented with abundant examples. The Marginal Effects Zoo website includes 20,000+ words of vignettes and case studies.*Efficient:*Some operations can be up to 1000 times faster and use 30 times less memory than with the`margins`

package.

*Valid:*When possible, numerical results are checked against alternative software like`Stata`

or other`R`

packages.*Thin:*The`R`

package requires relatively few dependencies.*Standards-compliant:*`marginaleffects`

follows “tidy” principles and returns simple data frames that work with all standard`R`

functions. The outputs are easy to program with and feed to other packages like`ggplot2`

or`modelsummary`

.*Extensible:*Adding support for new models is very easy, often requiring less than 10 lines of new code. Please submit feature requests on Github.*Active development*: Bugs are fixed promptly.

To cite `marginaleffects`

in publications please use:

Arel-Bundock V, Greifer N, Heiss A (Forthcoming). “How to Interpret Statistical Models Using marginaleffects in R and Python.” *Journal of Statistical Software*.

A BibTeX entry for LaTeX users is:

```
@Article{,
title = {How to Interpret Statistical Models Using {marginaleffects} in {R} and {Python}},
author = {Vincent Arel-Bundock and Noah Greifer and Andrew Heiss},
year = {Forthcoming},
journal = {Journal of Statistical Software}, }
```