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easystats

@easystats.github.io

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.”

111 Posts1071 Follower118 Folgt
easystats@easystats.github.io · last mo.

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.

easystats@easystats.github.io · 2mo ago

🚀 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()`

easystats@easystats.github.io · 3mo ago

This is such a great tutorial! If you run mixed models, learning how to tease apart state vs. trait differences is an absolute game-changer. Check out this guide from the #rstats #easystats {modelbased} package. 📊👇

Daniel 🕹️@strengejacke.de · 3mo ago

🚀 New vignette: Uncover "Context Effects" in mixed models. Stop blending within- & between-person effects. Learn to demean variables and formally test state vs. trait differences side-by-side with the #RStats #easystats {modelbased}📦 easystats.github.io/modelbased/a...

Andrew Heiss@andrew.heiss.phd · 7mo ago

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

Rémi Thériault@remi-theriault.com · 9mo ago

{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

Automated Reporting of Results and Statistical Models

The aim of the report package is to bridge the gap between R’s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual repo...

easystats.github.io

easystats@easystats.github.io · 11mo ago

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)
easystats@easystats.github.io · 11mo ago

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.

Prof Andy Field@profandyfield.com · 12mo ago

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.

Prof Andy Field@profandyfield.com · last yr.

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/

easystats@easystats.github.io · last yr.

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

easystats@easystats.github.io · last yr.

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...

An Introduction to Growth Mixture Models with brms and easystats

easystats.github.io

easystats@easystats.github.io · last yr.

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...