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...

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!

🎉 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

Improved support for the great {tinytable}📦 from @vincentab.bsky.social coming to the easystats packages! Use the `display()` method for different output formats of your tables - HTML, markdown, or - when `format = "tt"` a `tinytable` object that renders context-dependent. #easystats #rstats

#statstab #386 {bayestestR} Evaluating Evidence and Making Decisions using Bayesian Statistics by @mattansb.msbstats.info Thoughts: Want to start using Bayesian stats? Here is a quick but comprehensive guide in #R #bayesian #bayes #mcmc #easystats #guide mattansb.github.io/bayesian-evi...

bayestestR::describe_posterior() works on rvar columns

Several easystats📦were updated the past weeks, make sure to install them to get the latest features! Here's what's new: - 📦insight, bayestestR: performance improvements for Bayesian models, better support for brms-mixture models 1/2 #easystats #rstats easystats.github.io/easystats/

Yay, we have reached the 30 million downloads mark (and > 10k citations of our packages)! #easystats #rstats (nice metrics, despite not 100% accurate, but still...)

Since we got questions regarding if model predictors also predict class membership or only the mean outcome for each class, we added a short paragraph including a summary table and some example code at the end of the vignette, clarifying the different GMM options: easystats.github.io/modelbased/a...

Unlock hidden patterns in longitudinal data! 🚀 Our new vignette shows how to use brms & easystats to perform Growth Mixture Models, identify unique developmental trajectories, and visualize & interpret your findings with ease. #rstats #brms #easystats easystats.github.io/modelbased/a...

Personally I prefer using datawizard::standardize(), and specifically using it *in the formula*. So mtcars$hp_z <- scale(mtcars$hp) mpg ~ hp_z Becomes mpg ~ standardize(hp) This solves both issues you raise in your post. #rstats @easystats.bsky.social

We're happy to have an accompanying publication for another #rstats #easystats package published! Thanks to @vincentab.bsky.social and @tjmahr.com for reviewing the manuscript!

🆕 Introducing check_group_variation() in the {performance} #Rstats package! 🎉 This function makes it easy to checks if variables vary within or between levels of grouping variables. Perfect for understanding and designing mixed models 🚀 easystats.github.io/performance/... #stats #easystats

One function per week, this time we look closer at random effects variances in mixed models: `performance_reliability()` & `performance_dvour()`. Is the variability in your data due to noise within groups, or actual differences between groups? #easystats #rstats easystats.github.io/performance/...

In case you missed it, we recently updated some of our packages, including many new features (again) in the #rstats #easystats {modelbased} package: easystats.github.io/modelbased/n... The last weeks we were working a lot on improving support and performance for Bayesian models and especially

One function per week (maybe we change it to month?), this time showing how to easily create a table of a sample description using the #rstats #easystats {report} package: easystats.github.io/report/refer... Appropriate summary automatically applied based on variable types, also supports weighting.

Explore the many ways to interpret statistical models in #rstats using the #easystats {modelbased} package. There is a series of five vignettes, demonstrating how to easily answer different research questions. No longer struggle with confusing coefficient tables! easystats.github.io/modelbased/a...

For every (possibly) complex question, there's an clear and easy to communicate solution - if you go for predictions/marginal effects/(pairwise) comparisons/contrasts instead of trying to interpret coefficients. #easystats #rstats

A new version of {modelbased} just hit CRAN, including bug fixes and many new features. modelbased let's you easily compute marginal means, contrasts and pairwise comparisons, and marginal effects (slopes). Find a lot of examples and vignettes online at: easystats.github.io/modelbased/

Centering is not only useful, but sometimes necessary. E.g., to avoid heterogeneity bias, commonly in longitudinal data analysis with variables that vary over time. Special centering is required then. Here's one function per week, `datawizard::demean()`! #rstats easystats.github.io/datawizard/r...

One function per week, this time showing an easy way how to calculate correlations: with `correlation()` from the {correlation} package! Easily apply dozens of different methods, including multilevel and Bayesian correlations! #rstats #easystats easystats.github.io/correlation/

One function per week, this time with `parameters::model_parameters()`. The function returns a comprehensive, consistent ("tidy") output for regression models and many other statistical procedures, including Bayesian and mixed models. #rstats #easystats easystats.github.io/parameters/r...

New tutorial: Causal inference for observational data using propensity scores and g-computation with the {modelbased} package #rstats #easystats easystats.github.io/modelbased/a...

One function per week, this time we show you `check_itemscale()`, which computes various measures of internal consistencies applied to (sub)scales from items that were extracted using `principal_components()`, or supplied as data frame. #easystats #rstats easystats.github.io/performance/...

The #easystats team puts lots of work into writing package documentarion and tutorials, available from the GitHub websites. There's also a collection of other resources related to easystats, which we update regularly: easystats.github.io/easystats/ar... let us know if we missed something! #rstats

One function per week, this week we're excited to show you `model_dashboard()`, *the* one-ring-to-rule-them-all to get a quick and comprehensive overview of your regression model: easystats.github.io/easystats/re... See example code in the screenshot, and video in next post! #rstats #easystats

A new version of the #rstats #easystats {modelbased}📦just hit CRAN! {modelbased} helps you to easily compute marginal means, contrast analysis and model predictions. It's a wrapper around the great {marginaleffects}📦, providing an easy and intuitive syntax! easystats.github.io/modelbased/

We're sorry that we missed to post a function last week, but we're currently in the process of revising our {modelbased}📦 significantly! Look forward to great new features, leveraging the power of {marginaleffects} and {emmeans}, providing an absolutely simple and inuitive user interface! More soon!

One function per week, this week with `data_seek()` from the {datawizard} package. This function helps you finding variables by their names, variable or value labels in data sets Labelled data is also supported. #easystats #rstats easystats.github.io/datawizard/r...

One function per week, this week we show you `p_direction()`, the probability of direction, i.e. is a parameter strictly positive or negative? Cum grano salis, a continuous measure of evidence. easystats.github.io/bayestestR/r... #rstats #easystats

Another #rstats #easystats package is published as "stable" release on CRAN: {datawizard}! The one-stop-solution for so many data wrangling and preparation tasks (reshaping, selecting, filtering, variable standardization, centering, recoding, ...), super light-weight easystats.github.io/datawizard/

This year we'll start and try to post one #rstats #easystats function per week, to promote the different packages from the easystats-project. We'll start with `t_to_d()` from the {effectsize} package, which is one of the many functions to convert effectsizes: easystats.github.io/effectsize/r...

There's possibly no better statistical programming language than #rstats (note the "possibly", this is not going to be the first language1 versus language2 posts in 2025 😎)

Also available for frequentist models: easystats.github.io/parameters/r... #rstats #easystats

These bayes tutorials are the first ones i've actually understood. Looking forward to the rest of the being finished. Go @easystats.bsky.social teams! easystats.github.io/bayestestR/i... #rstats

Significance is not enough! The goal of this package is to provide utilities to work with indices of effect size and standardized parameters, allowing computation and conversion of indices such as Cohen’s d, r, odds-ratios, etc. easystats.github.io/effectsize/ #rstats

A really matured 📦 that reached the 1.0.0 milestone! More #easystats milestone releases to come the next weeks and months... #rstats

One new feature is how `export_table()` can deal with very wide tables. `export_table()` is used by almost every `print()` method throughout easystats packages. Whenever you have text-output in the console or markdown documents, you can be (almost) sure you won't see messy tables anymore...

The first stable #easystats release is there! Our "workhorse" package, which builds the foundation of the easystats-ecosystem, has been released in version 1.0.0 on CRAN. Probably not the package users use most, it's rather doing its work silently in the background... easystats.github.io/insight/

We're thrilled to announce that we’ve begun preparing for the stable (1.0) releases of all packages in our ecosystem. Releases are expected early next year. If you’ve been using any of these packages, we would greatly value your feedback on the API: easystats.github.io/easystats/ #rstats #easystats

Want to learn/teach R? I'm still actively developing my package of learnr tutorials at github.com/profandyfiel... Just added a tutorial on multilevel models and repeated measures.

easyverse is growing :-) #easystats #rstats

And this is how we put it into action! Here you can see the switch from RStudio to Positron by @remi-theriault.com (while @strengejacke.bsky.social is recovering from the evening before...) #rstats #easystats

This is how the #easystats team generates new ideas for their #rstats packages! Hard(ly) working @remi-theriault.com and @strengejacke.bsky.social!

Which of the following information below model output (last paragraph, not that one about uncertainty intervals) do you find useful/helpful and think it's worth printing? It's printed once per session. Should some/all information moved into the docs, or kept in output? #easystats

📚🚨 I posted 11 new chapters of my upcoming book! Model to Meaning: How to Interpret Statistical Results with #marginaleffects for #RStats and Python. These are early drafts and I really need your feedback! Errors, content requests, improvements, etc. marginaleffects.com

Our Introduction to Bayesian Data Analysis for Cognitive Science (with Nicenboim and Schad) is now in production with CRC Press. It will remain freely available here: bruno.nicenboim.me/bayescogsci/