Your model is only as good as its assumptions. 📊 But what happens when your data breaks the rules? Let’s dive into how to check your model assumptions—and exactly how to fix those pesky violations: 🧵👇
easystats.github.io/performance/...
#rstats #easystats #performance

Changelog

New updates of {performance} and {see} arrived at CRAN, with some nice improvements for `check_model()`. You can now limit data points to boost performance for large models or hide confidence intervals for models with only few data and spuriously large intervals
easystats.github.io/performance/...

Lots of folks interested in outlier detection with @easystats.github.io's {performance} @ #ISCOP2026

#statstab #463 {modelbased} Understanding your models

Thoughts: A deceptively simple case study on how to understand and report your model.

#rstats #modelling #easystats #r #reporting

easystats.github.io/modelbased/a...

See here for an example of their differences. Even though {parameters} prints things as not-tibbles, it still uses data frames behind the scenes and you can do regular dplyr things. {parameters} fits directly in the {tinytable} world too, which is nice andrewheiss.quarto.pub/parameters-v...

Finally got around to removing broom::tidy(), broom::glance(), and broom::augment() from my class examples in favor of parameters::model_parameters(), performance::model_performance() and marginaleffects::predictions() because they're *so nice* for teaching! #rstats #easystats

Ah, no, we used `datawizard::to_factor()` to convert label attributes into factor levels.

One advantage of that data is that it has labelled data, and you can see the automatic labelling feature in later plots.

🎉 Great news for #rstats users! If you love the native R graphics feel of #tinyplot AND you're a fan of the powerful #easystats #modelbased package, this is for you!

Thanks to @gmcd.bsky.social, we significantly enhanced the tinyplot integration.

🔗 Read more: easystats.github.io/modelbased/a...

{report} #rstats package version 0.6.2 is now on CRAN!

MANY bug fixes in this version! Including corrected duplicated text outputs and dramatic speed increases for brmsfit models (which used to refit the model entirely every time).

easystats.github.io/report/

With the @easystats.github.io team

... 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

Example for a colored markdown table, printed to the R console.

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? 🤯

Screenshot of the gt-HTML-table-output

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!).

Screenshot of the default R console table output

... 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! 💅

library(modelbased) data(penguins) model <- lm(body_mass ~ species * island, data = penguins) out <- estimate_means(model, c("species", "island")) # basic text output out # HTML in viewer pane, using the gt-package display(out, format = "html") # tinytable by defaults prints to the viewer pane, too, # but we change the default to markdown for the console here options(tinytable_print_output = "markdown") # nice markdown output in the console, including colored text! display(out, format = "tt", footer = "") |> tinytable::style_tt(i = 1:3, color = "#cc0000") |> tinytable::style_tt(i = 4:6, indent = 2, background = "#009900") |> tinytable::theme_markdown(ansi = TRUE)

Alrighty, {easystats} users! 👋 Ever wonder how those neat tables magically appear in your R console, or even better, in your fancy #rstats Markdown and Quarto docs?

Well, most of the objects you work with in {easystats} are basically tables, i.e. a 2D matrix with columns and rows...

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...

modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions Makowski 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...

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...).

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.

I’m about halfway through this update (first 11 tutorials are done). I think they’re a lot better. Using a consistent @easystats.github.io workflow throughout will - I think - massively reduce the cognitive load for students. Looking forward to road testing in autumn term.

How to summarize the total effect of a categorical variable like education? A new vignette shows how to compute absolute and relative inequality with the #easystats {modelbased}📦in #rstats. Get a single, interpretable number to quantify overall group disparities!
easystats.github.io/modelbased/a...

Modelbased for Quick and Beautiful Model Visualization in #rstats imachordata.com/2025/07/25/m... Thanks, @easystats.github.io!

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...

🎉 Great news, R users! 🎉 We're thrilled to announce that {tinyplot} support is coming to the #rstats #easystats project! Get ready for even more amazing stuff to make your data analysis a breeze! 📊✨
@gmcd.bsky.social @vincentab.bsky.social @zeileis.org

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