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easystats
@easystats.github.io

Okay, so you've crunched your numbers and got some awesome statistical models? Sometimes, just knowing "X predicts Y" isn't enough to really get to the juicy bits. That's where the cool post-hoc stuff comes in – think estimated marginal means, contrasts, pairwise comparisons, or #marginaleffects.

easystats
@easystats.github.io

The {modelbased} R package is here to be your statistical sidekick! It's an #rstats gem that helps you squeeze every last drop of insight from your models. It's got a super user-friendly interface to pull out all those estimands from a huge variety of models (doi.org/10.21105/jos...).

doi.orgmodelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictionsMakowski et al., (2025). modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions. Journal of Open Source Software, 10(1...

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easystats
@easystats.github.io

Even if you're not tackling these super complex questions, {modelbased} is generally just a fantastic tool for really getting your head around your statistical models. Go on, take a peek! You might just fall in love: easystats.github.io/modelbased/ #rstats #easystats #marginaleffects #inference

easystats.github.ioEstimation of Model-Based Predictions, Contrasts and MeansImplements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, s...