Goal  Function 

Predictions  predictions() 
avg_predictions() 

plot_predictions() 

Comparisons  comparisons() 
avg_comparisons() 

plot_comparisons() 

Slopes  slopes() 
avg_slopes() 

plot_slopes() 

Marginal Means  marginal_means() 
Grids  datagrid() 
datagridcf() 

Hypothesis & Equivalence  hypotheses() 
Bayes, Bootstrap, Simulation  posterior_draws() 
inferences() 
The Marginal Effects Zoo (0.15.1)
đźš¨đźš¨đźš¨ This November, the marginaleffects
author (Vincent) will be giving an online seminar called: â€śInterpreting and Communicating Statistical Results with R.â€ť If you want to become a marginaleffects
expert and support the development of the package, sign up at Code Horizons! đźš¨đźš¨đźš¨
1 Why?
Interpreting the parameters estimated by complex statistical models is often challenging. Many applied researchers are keen to report simple quantities that carry clear scientific meaning but, in doing so, they face three primary obstacles:
 Intuitive estimandsâ€”and their standard errorsâ€”are often tedious to compute.
 The terminology to describe these estimands is not standardized, and varies tremendously across disciplines.
 Modeling packages in
R
andPython
produce inconsistent objects which require users to write custom (and errorprone) code to interpret statistical results.
The â€śMarginal Effects Zooâ€ť book and the marginaleffects
packages for R
and Python
are designed to help analysts overcome these challenges. The free online book provides a unified framework to describe and compute a wide range of estimands. The marginaleffects
package implements this framework and offers a consistent interface to interpret the estimates from over 86 classes of statistical models.
2 What?
The marginaleffects
package allows R
and Python
users to compute and plot three principal quantities of interest: (1) predictions, (2) comparisons (contrasts, risk ratios, odds, lift, etc.), and (3) slopes. In addition, the package includes a convenience function to compute a fourth estimand, â€śmarginal meansâ€ť, which is a special case of averaged predictions. marginaleffects
can also average (or â€śmarginalizeâ€ť) unitlevel (or â€śconditionalâ€ť) estimates of all those quantities. Finally, marginaleffects
can also conduct hypothesis and eequivalence tests on coefficient estimates and on any of the quantities generated by the package.
The outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or factor levels. a.k.a. Fitted values, adjusted predictions.
predictions()
,avg_predictions()
,plot_predictions()
.
Compare the predictions made by a model for different regressor values (e.g., college graduates vs. others): contrasts, differences, risk ratios, odds, lift, etc.
comparisons()
,avg_comparisons()
,plot_comparisons()
.
Partial derivative of the regression equation with respect to a regressor of interest. a.k.a. Marginal effects, trends.
slopes()
,avg_slopes()
,plot_slopes()
.
Predictions of a model, averaged across a â€śreference gridâ€ť of categorical predictors.
marginalmeans()
.
Hypothesis and Equivalence Tests:
Hypothesis and equivalence tests can be conducted on linear or nonlinear functions of model coefficients, or on any of the quantities computed by the
marginaleffects
packages (predictions, slopes, comparisons, marginal means, etc.). Uncertainy estimates can be obtained via the delta method (with or without robust standard errors), bootstrap, or simulation.
3 Benefits
The advantages of marginaleffects
include:

Powerful: It can compute predictions, comparisons (contrasts, risk ratios, etc.), slopes, and conduct hypothesis tests for 86 different classes of models in
R
andPython
.  Simple: All functions share a simple and unified interface.
 Documented: Each function is thoroughly documented with abundant examples. The website includes 20,000+ words of vignettes and case studies.

Efficient: Some operations are orders of magnitude faster than with the
margins
package, and the memory footprint is much smaller.  Thin: Few dependencies.

Standardscompliant:
marginaleffects
follows â€śtidyâ€ť principles and returns objects that work with standard functions likesummary()
,head()
,tidy()
, andglance()
. These objects are easy to program with and feed to other packages likemodelsummary
. 
Valid: When possible, numerical results are checked against alternative software like
Stata
or otherR
packages. Unfortunately, it is not possible to test every model type, so users are still strongly encouraged to crosscheck their results.  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.
4 License and Citation
The marginaleffects
package is licensed under the GNU General Public License v3.0. The content of this website/book is licensed under the Creative Commons AttributionNonCommercial 4.0 International (CC BYNC 4.0).
Warning in citation("marginaleffects"): no date field in DESCRIPTION file of package 'marginaleffects'
To cite package 'marginaleffects' in publications use:
ArelBundock V (2023). _marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests_. R package version 0.15.1, <https://marginaleffects.com/>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis
Tests},
author = {Vincent ArelBundock},
year = {2023},
note = {R package version 0.15.1},
url = {https://marginaleffects.com/},
}