rubenarslan/marginaleffects

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. doi:10.18637/jss.v111.i09
https://doi.org/10.18637/jss.v111.i09.

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}, }