README.qmd

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The `marginaleffects` package for `R` and `Python` offers a single point of entry to easily interpret the results of [over 100 classes of models,](https://marginaleffects.com/bonus/supported_models.html) using a simple and consistent user interface. 

This package comes with a free full-length online book, with extensive tutorials: [https://marginaleffects.com](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](https://marginaleffects.com/bonus/performance.html) 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](https://marginaleffects.com/bonus/supported_models.html) 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`](https://marginaleffects.com/bonus/plot.html) or [`modelsummary`.](https://marginaleffects.com/bonus/tables.html)
- *Extensible:* Adding support for new models is very easy, often requiring less than 10 lines of new code. Please submit [feature requests on Github.](https://github.com/vincentarelbundock/marginaleffects/issues)
- *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},
  }