This package is considered a duplicate. The official version of this package is found at:https://vincentarelbundock.r-universe.dev/marginaleffects

Package: marginaleffects 0.25.1

Vincent Arel-Bundock

marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests

Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.

Authors:Vincent Arel-Bundock [aut, cre, cph], Noah Greifer [ctb], Etienne Bacher [ctb], Grant McDermott [ctb], Andrew Heiss [ctb]

marginaleffects_0.25.1.tar.gz
marginaleffects_0.25.1.tar.gz(r-4.5-noble)marginaleffects_0.25.1.tar.gz(r-4.4-noble)
marginaleffects_0.25.1.tgz(r-4.4-emscripten)marginaleffects_0.25.1.tgz(r-4.3-emscripten)
marginaleffects.pdf |marginaleffects.html✨
marginaleffects/json (API)
NEWS

# Install 'marginaleffects' in R:

install.packages('marginaleffects', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vincentarelbundock/marginaleffects/issues14 issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

Conda:r-marginaleffects-0.25.1(2025-03-31)

cpp

6.39 score 1 stars 10 packages 21k downloads 26 exports 9 dependencies

Last updated 2 days agofrom:ee433201af. Checks:3 OK. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 29 2025
R-4.5-linux-x86_64	OK	Mar 29 2025
R-4.4-linux-x86_64	OK	Mar 29 2025

Exports:avg_comparisons avg_predictions avg_slopes comparisons datagrid expect_margins expect_predictions expect_slopes get_coef get_dataset get_draws get_group_names get_model_matrix get_predict get_vcov glance hypotheses inferences plot_comparisons plot_predictions plot_slopes posterior_draws predictions set_coef slopes tidy

Dependencies:backports checkmate data.table Formula generics insight Rcpp RcppEigen rlang

Model to Meaning

Rendered frommodel_to_meaning.Rmdusingknitr::rmarkdownon Mar 29 2025.

Last update: 2025-03-29
Started: 2025-02-01

Citation

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.

Corresponding BibTeX entry:

  @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},
  }

Readme and manuals

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}, }

Help Manual

Help page	Topics
Comparisons Between Predictions Made With Different Regressor Values	avg_comparisons comparisons
Data grids	datagrid
Download and Read Datasets from 'marginaleffects' or Rdatasets	get_dataset
Extract Posterior Draws or Bootstrap Resamples from 'marginaleffects' Objects	get_draws
(Non-)Linear Tests for Null Hypotheses, Joint Hypotheses, Equivalence, Non Superiority, and Non Inferiority	hypotheses
(EXPERIMENTAL) Bootstrap, Conformal, and Simulation-Based Inference	inferences
Plot Conditional or Marginal Comparisons	plot_comparisons
Plot Conditional or Marginal Predictions	plot_predictions
Plot Conditional or Marginal Slopes	plot_slopes
Predictions	avg_predictions predictions
Print 'marginaleffects' objects	print.marginaleffects
Slopes (aka Partial derivatives, Marginal Effects, or Trends)	avg_slopes slopes