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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, 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:
R
.margins
package.Stata
or other R
packages.R
package requires relatively few
dependencies.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
.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.
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}, }
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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