- 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.Aug 31, 2025 08:27
- 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...).
- True to the #easystats vibe, {modelbased} keeps things simple, flexible, and easy-peasy so you can truly unleash the power of your models without pulling your hair out. Ever wondered about cause and effect in observational data without needing a time machine? easystats.github.io/modelbased/a...
- Got a thing for social and health inequalities? easystats.github.io/modelbased/a... Or maybe you're into the nitty-gritty of intersectional analysis? easystats.github.io/modelbased/a...
- Dealing with interrupted time series where a sudden event just messed with everything? easystats.github.io/modelbased/a... Curious about disparities, different trajectories of hidden groups, and what makes them tick? easystats.github.io/modelbased/a...
- 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
