... which you can do by adding additional "layers", if you use the gt-format or tinytable-format.

Not sure about the specific requirements for APA 7 style, but I guess you may need some additionally tweaking of the returned table object.

Wanna dive deeper into the table universe? Check out these links:
👉 easystats.github.io/insight/arti...
👉 vincentarelbundock.github.io/tinytable/

Happy printing, everyone! 🖨️ #rstats #easystats

That "tt" option is now fully rolled out across several #easystats packages, powered by the amazing {tinytable} package. This means you can create tables in a gazillion different output formats! How cool is that? 🤯

And you can totally control the vibe! Use the `format` argument to get "markdown" (for a classic kable look), "html" (for a sleek gt-table), or the new kid on the block, "tt" (for a tinytable masterpiece!).

... and when they print, it's thanks to some behind-the-scenes magic with `insight::format_table()` and `insight::export_table()`! ✨

But there's more! Many #easystats functions also have a `display()` method. Think of it as your personal table stylist, making everything look super user-friendly! 💅

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

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...

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...

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...

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...).

Just dodging is not yet implemented in {tinyplot}, but hopefully coming soon!

As you can see, many plot types already work, just some fine-tuning left to do...

Since `display(format = "tt")` returns a `tinytable` object, you can easily modify the table to meet your needs.

Here's the default HTML rendering.