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easystats @easystats.github.io · 17/09/2026

Fitted the model? Great, that was just step one. 🛠️ Learn how to extract the actual answers to your research questions (predictions, contrasts, slopes) using the {modelbased} modelisation-approach. Read the new vignette here: easystats.github.io/modelbased/a... #RStats #easystats

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easystats @easystats.github.io · 01/09/2025

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? 🤯

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

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easystats @easystats.github.io · 01/09/2025

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!).

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easystats @easystats.github.io · 01/09/2025

... 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! 💅

Screenshot of the default R console table output

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easystats @easystats.github.io · 01/09/2025

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)

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easystats @easystats.github.io · 22/07/2025

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

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easystats @easystats.github.io · 22/07/2025

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

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easystats @easystats.github.io · 22/07/2025

🎉 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

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easystats @easystats.github.io · 22/07/2025

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

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easystats @easystats.github.io · 22/07/2025

Here's the default HTML rendering.

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easystats @easystats.github.io · 22/07/2025

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

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easystats @easystats.github.io · 05/07/2025

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...)

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easystats @easystats.github.io · 31/05/2025

Page 3 in the paper (or the docs: easystats.github.io/modelbased/r...)

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easystats @easystats.github.io · 27/05/2025

This function can also be used to detect any predictors that might cause heterogeneity bias - variable that vary both within and between groups, that can be treated with datawizard::demean() easystats.github.io/datawizard/r...

performance::check_group_variation(iris, select = "Sepal.Width", by = "Species")
#> Check Species variation
#>
#> Variable | Variation | Design
#> --------------------------------
#> Sepal.Width | both |

datawizard::demean(iris, select = "Sepal.Width", by = "Species") |>
dplyr::glimpse()
#> Rows: 150
#> Columns: 7
#> $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
#> $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
#> $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
#> $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
#> $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…
#> $ Sepal.Width_between <dbl> 3.428, 3.428, 3.428, 3.428, 3.428, 3.428, 3.428, 3.428, 3…
#> $ Sepal.Width_within <dbl> 0.072, -0.428, -0.228, -0.328, 0.172, 0.472, -0.028, -0.0…

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easystats @easystats.github.io · 27/05/2025

🆕 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 |

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easystats @easystats.github.io · 22/05/2025

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/...

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easystats @easystats.github.io · 15/05/2025

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.

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easystats @easystats.github.io · 10/03/2025

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...

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easystats @easystats.github.io · 05/03/2025

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/

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easystats @easystats.github.io · 28/02/2025

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...

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easystats @easystats.github.io · 16/02/2025

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/...

library(performance)

# example from `?stats::prcomp`
C <- chol(S <- toeplitz(0.9^(0:15)))
set.seed(17)
X <- matrix(rnorm(1600), 100, 16)
Z <- X %*% C

# run PCA, extract three components
pca <- parameters::principal_components(
as.data.frame(Z),
rotation = "varimax",
n = 3
)
# look how scales built from the three components perform
check_itemscale(pca)

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easystats @easystats.github.io · 06/02/2025

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

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easystats @easystats.github.io · 06/02/2025

Plotting gives you nice figures out of the box!

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easystats @easystats.github.io · 06/02/2025

The output is clear and informative, see following examples:

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easystats @easystats.github.io · 27/01/2025

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...

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easystats @easystats.github.io · 17/01/2025

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

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easystats @easystats.github.io · 10/01/2025

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/

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easystats @easystats.github.io · 06/01/2025

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...

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easystats @easystats.github.io · 26/11/2024

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...

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easystats @easystats.github.io · 26/11/2024

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/

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easystats @easystats.github.io · 20/11/2024

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

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easystats @easystats.github.io · 16/11/2024

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

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easystats @easystats.github.io · 16/11/2024

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

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easystats @easystats.github.io · 27/10/2024

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

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