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.

This package comes with a free full-length online book, with extensive tutorials: https://marginaleffects.com

The package’s 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 use:

Arel-Bundock V, Greifer N, Heiss A (2024). “How to Interpret Statistical Models Using marginaleffects for R and Python.” Journal of Statistical Software, 111(9), 1-32.

A BibTeX entry for LaTeX users is

@Article{, title = {How to Interpret Statistical Models Using {marginaleffects} for {R} and {Python}}, author = {Vincent Arel-Bundock and Noah Greifer and Andrew Heiss}, journal = {Journal of Statistical Software}, year = {2024}, volume = {111}, number = {9}, pages = {1–32}, doi = {10.18637/jss.v111.i09}, }