easystats
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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- Reposted by easystatsFinally 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
- 🎉 Great news for #rstats users! If you love the native R graphics feel of #tinyplot AND you're a fan of the powerful #easystats #modelbased package, this is for you! Thanks to @gmcd.bsky.social, we significantly enhanced the tinyplot integration. 🔗 Read more: easystats.github.io/modelbased/a...
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- 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.
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- 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...
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- 🎉 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
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- 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...
- 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...
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- 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/
