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easystats.github.io
easystats
@easystats.github.io
960 followers 120 following 89 posts
Official channel of {easystats}, a collection of #rstats 📦s with a unifying and consistent framework for statistical modeling, visualization, and reporting.

“Statistics are like sausages. It’s better not to see them being made, unless you use easystats.”
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easystats.github.io
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...
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)
September 1, 2025 at 6:04 AM
easystats.github.io
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.
August 31, 2025 at 8:27 AM
Reposted by easystats
profandyfield.com
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.
profandyfield.com Prof Andy Field @profandyfield.com · Feb 10
Probably no-one except me uses my R tutorials in their teaching, but if you do, I'm re-writing them over the next 6-9 months. My goal is to streamline them based on 5 years of using them in class, but if you have (polite) requests/suggestsions let me have them. www.discovr.rocks/discovr/
discovr: a package of interactive tutorials | discovr
Statistics education
www.discovr.rocks
August 20, 2025 at 10:19 PM
easystats.github.io
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...
Case Study: Measuring and comparing absolute and relative inequalities in R
easystats.github.io
July 28, 2025 at 7:13 AM
Reposted by easystats
jebyrnes.bsky.social
Modelbased for Quick and Beautiful Model Visualization in #rstats imachordata.com/2025/07/25/m... Thanks, @easystats.github.io!
Modelbased for Quick and Beautiful Model Visualization · I'm a Chordata! Urochordata!
imachordata.com
July 25, 2025 at 8:38 PM
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
July 22, 2025 at 3:27 PM
easystats.github.io
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
July 22, 2025 at 7:42 AM
Reposted by easystats
mzloteanu.bsky.social
#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...
mattansb.github.io
July 14, 2025 at 10:14 PM
Reposted by easystats
tjmahr.com
bayestestR::describe_posterior() works on rvar columns
screenshot showing the row dataframe with a column of rvars and the markdown-formatted-table output of describe_posterior() + print_md()
July 14, 2025 at 8:52 PM
easystats.github.io
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/
Framework for Easy Statistical Modeling, Visualization, and Reporting
A meta-package that installs and loads a set of packages from easystats ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, vi...
easystats.github.io
July 10, 2025 at 5:49 PM
easystats.github.io
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...)
July 5, 2025 at 1:46 PM
easystats.github.io
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...
June 25, 2025 at 10:18 AM
easystats.github.io
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...
An Introduction to Growth Mixture Models with brms and easystats
easystats.github.io
June 24, 2025 at 5:33 PM
Reposted by easystats
mattansb.msbstats.info
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
June 4, 2025 at 7:33 PM
easystats.github.io
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!
joss-openjournals.bsky.social Journal of Open Source Software @joss-openjournals.bsky.social · May 30
Just published in JOSS: 'modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions' https://doi.org/10.21105/joss.07969
May 30, 2025 at 4:36 PM
easystats.github.io
🆕 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
mlmRev::egsingle |> performance::check_group_variation( select = c("female", "grade", "math"), by = c("schoolid", "childid"), include_by = TRUE ) #> Check schoolid variation #> #> Variable | Variation | Design #> ------------------------------ #> childid | both | nested #> female | within | crossed #> grade | both | #> math | both | #> #> Check childid variation #> #> Variable | Variation | Design #> ----------------------------- #> schoolid | between | #> female | between | #> grade | both | #> math | both |
May 27, 2025 at 6:48 AM
easystats.github.io
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/...
May 22, 2025 at 8:50 PM
easystats.github.io
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
Changelog
easystats.github.io
May 15, 2025 at 6:10 PM
easystats.github.io
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.
May 15, 2025 at 6:00 PM
easystats.github.io
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...
Contrasts and pairwise comparisons
easystats.github.io
May 12, 2025 at 6:31 AM
easystats.github.io
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
strengejacke.de Daniel 🕹️ @strengejacke.de · May 6
Here's another example. Instead of running multiple models for each "factor contrast" of interest and look at regression coefficients, you can also specify these contrasts directly in {modelbased} or {marginaleffects} (modelbased is just a convenient wrapper around marginaleffects and emmeans)
library(easystats) # sample data data(contrast_example, package = "modelbased") # center predictor of interaction term, for consistent results contrast_example$score <- center(contrast_example$score) # model with default factor-contrasts model1 <- lm(outcome ~ score * tx, data = contrast_example) # create custom contrasts for the "tx" factor treat_vs_none <- c(-2/3, 1/3, 1/3) short_vs_long <- c(0, -1/2, 1/2) contrasts(contrast_example$tx) <- cbind(treat_vs_none, short_vs_long) # model with custom factor-contrasts model2 <- lm(outcome ~ score * tx, data = contrast_example) # see parameter "tx [treat_vs_none]", value 1.94 - we got this results # due to custom contrasts... model_parameters(model2) # instead of re-running the model each time with different contrasts, simply # formulate what you want to do as "comparison" (or "hypothesis" in # {marginaleffects}): we want to compare the average effect of short + long # treatment against no treatment. We find these coefficients in rows 2+3 # (short+long) and row 1 (none). estimate_means(model1, "tx") # we now formulate this as comparison estimate_contrasts(model1, "tx", comparison = "((b2+b3)/2) = b1") # marginaleffects equivalent marginaleffects::avg_predictions( model1, variables = "tx", hypothesis = "((b2+b3)/2) = b1" )
May 6, 2025 at 12:11 PM
easystats.github.io
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/
Estimation of Model-Based Predictions, Contrasts and Means
Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, s...
easystats.github.io
March 10, 2025 at 8:35 PM
easystats.github.io
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...
March 10, 2025 at 10:03 AM
easystats.github.io
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/
March 5, 2025 at 9:42 PM
easystats.github.io
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...
February 28, 2025 at 7:02 AM