easystats

What does a sound data analysis workflow look like? In a first step: - Define your estimand (*quantity and unit of interest*) & research question - Map reality to your model (choose variables wisely) How the {modelbased}📦fits into this post-estimation workflow (second step): 👇 #rstats #easystats

An estimand needs two things: your *quantity* and your *unit* (target population) of interest. In {modelbased}, you control this via the `estimate` argument to say who is your target population: • A specific individual (e.g. conditional) • An average across individuals (marginal/population)

What does a sound data analysis workflow look like? In a first step: - Define your estimand (*quantity and unit of interest*) & research question - Map reality to your model (choose variables wisely) How the {modelbased}📦fits into this post-estimation workflow (second step): 👇 #rstats #easystats

A massive thank you to everyone who contributed to the #easystats project over the last few months! 🚀 Thanks to the amazing R community on GitHub, you've fixed plenty of bugs and rolled out exciting new features. We absolutely appreciate your support and collaboration! #rstats #OpenSource

Big news from the #rstats #easystats universe! 🚀 We’ve just released updates for several of our packages to CRAN. Time for a quick highlight reel of the most important new features. 🧵 (1/4) You find the details on the related package websites (shown below), and on CRAN.

{performance} brings powerful new tools for your model diagnostics: The new `check_priors()` function allows you to conduct prior predictive checks for Bayesian models, helping you better validate your model assumptions. 🔍 (3/4) easystats.github.io/performance/

Big news from the #rstats #easystats universe! 🚀 We’ve just released updates for several of our packages to CRAN. Time for a quick highlight reel of the most important new features. 🧵 (1/4) You find the details on the related package websites (shown below), and on CRAN.

🚀 Big updates are here! A new round of #rstats #easystats releases is now live on CRAN, featuring major performance boosts and new features for parameters, bayestestR, performance, modelbased, and see. Update via CRAN or grab the latest dev versions with: `easystats::install_latest()`

🎨 {see} 0.14.0 levels up your visualizations! - New type = "graph" for factor analysis & PCA plots to map node/edge loadings - Brand new _halo() geoms (geom_point_halo(), etc.) to add sleek contours to points - Cleaned up smoother curves in outlier plots for small datasets

🚀 Big updates are here! A new round of #rstats #easystats releases is now live on CRAN, featuring major performance boosts and new features for parameters, bayestestR, performance, modelbased, and see. Update via CRAN or grab the latest dev versions with: `easystats::install_latest()`

📈 {performance} 0.17.0 makes model evaluation even smoother: - test_likelihoodratio() now includes a -2LL criterion column - check_predictions() for Bayesian models is standardized using {modelbased} under the hood - Expanded troubleshooting docs for check_model()

🚀 Big updates are here! A new round of #rstats #easystats releases is now live on CRAN, featuring major performance boosts and new features for parameters, bayestestR, performance, modelbased, and see. Update via CRAN or grab the latest dev versions with: `easystats::install_latest()`

📊 {parameters} 0.29.1 is out! - Faster model_parameters() for large mgcv::gam() models (use re_test = FALSE) - Better handling of (Intercept) in post-hoc standardizations - Cluster argument in bootstrap_model() for parallel SNOW handling - Support for lavaan.mi & BSDA packages

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...

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? 🤯

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...

... 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! 💅

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.

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...

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.

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...

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.