I fit a model... now what?
Details
# Abstract
This talk introduces `marginaleffects`, a unified interface to interpret the results of over 60 classes of models in `R`. `marginaleffects` allows users to compute and plot the key quantities of interest in most statistical analyses: predictions, contrasts, marginal effects (slopes), and marginal means. In addition, users can easily conduct hypothesis tests for arbitrary functions of a model's parameters.
`marginaleffects` is fast, low-dependency, actively developed, and well documented. The package website hosts an extensive list of vignettes and case studies which illustrate how to interpret and report the results of a variety of models: Bayesian, GAMs, Mixed-Effects, causal inference, etc.
# Resources
[https://vincentarelbundock.github.io/marginaleffects/](https://urldefense.com/v3/https:/vincentarelbundock.github.io/marginaleffects/;!!BZ50a36bapWJ!olUQhCvdpZCzalqJC8AWnw-cALExxl0pSfA99ot45n7ZlSaiVa4zv0PLvVMPq8OCoihUGe5KVMoezG8INYk7uMGzoLTp$)
[http://arelbundock.com](https://urldefense.com/v3/http:/arelbundock.com;!!BZ50a36bapWJ!olUQhCvdpZCzalqJC8AWnw-cALExxl0pSfA99ot45n7ZlSaiVa4zv0PLvVMPq8OCoihUGe5KVMoezG8INYk7uHzenVH9$)
# Bio
Vincent Arel-Bundock is an Associate Professor of Political Science at the Université de Montréal, where he teaches research methods and political economy. He uploaded his first R package to CRAN in 2009. 13 years later, he is still fielding support emails and bug reports.
