(4) "Transitioning to R for Statistics" will run June 17-18 and ease the transition to R for those who already know correlations, group comparisons, and multiple regression in another software package. Uses {easystats} tools and donates to easystats. smart-workshops.com/rstats-info

(5) "Transitioning to R for Multilevel Modeling" will run June 18-19 and eases the transition to R for those who already know mixed effects modeling (MLM/HLM/GLMM) in another software package. Uses {easystats} tools and donates to easystats. smart-workshops.com/rmlm-info

🚨New publication in Behavior Research Methods!🚨 We review outlier detection methods and how to achieve them in R using #easystats' {performance} 📦 📄paper: doi.org/10.3758/s13428… 📃preprint: doi.org/10.31234/osf.i… 💻performance: easystats.github.io/performance/re… #rstats

You may think it's just s small addition to one of our packages, but overall, the many small pieces form a marvelous "R Framework for Easy Statistical Modeling, Visualization, and Reporting" #easystats

And another "small but great" piece in our #easystats collection...

#statstab #129 Structural Models (EFA, CFA, SEM, ...) w/ {parameters} Thoughts: Lots of debate about #EFA vs #CFA; very confusing. Once I figure out what to use, this #R package seems to have lots of functionality. #rstats #factoranalysis #r #stats easystats.github.io/parameters/art…

Today Tom Geva (from Ben-Gurion University of the Negev's Statisticas and Data Analysis program) presented the work he's done over the last few months for easystats's correlation package 👏

Are you working with mixed (multilevel) models in #rstats and wondering how to calculate R2? Grab the latest updates of our #easystats {performance} and {insight} packages from CRAN and try out "r2_nakagawa()" (or simply "r2()" for mixed model): easystats.github.io/performance/re… /1

We improved the accuracy for many new model families and validated the results against examples from the paper that has proposed this method: royalsocietypublishing.org/doi/10.1098/rs… "r2_nakagawa()" is probably one of the most accurate functions to return R2 for mixed models in #rstats.

A new feature that *might* be added to our #rstats #easystats packages soon: checking models for correct adjustment by using DAGs! `check_dag()` (working title) makes it so easy to check the causal paths of your model and tells you how to address misspecifications!

A short update on this feature: - Improved documentation (easystats.github.io/performance/re…) - Streamlined text output - Improved plots #rstats #easystats #DAG

NEW to {bayestestR} dev version - support for #marginaleffects! Take it for a test drive: remotes::install_github("easystats/bayestestR") easystats #rstats

We're on bluesky now! Follow us under easystats.bsky.social #easystats #rstats

{bayestestR} makes it now much easier to process inputs from packages {marginaleffects}, {emmeans}, or random variable types from posterior draws! #easystats #rstats

We're working on revisiting and homogenizing outputs from our #easystats packages. This includes consistent coloring of information/warnings/messages, but also: which information is useful in the output, which information should just go into the docs? WDYT?

🤯📈 TAKE YOUR STATS SKILLZ TO THE NEXT LEVEL WITH THIS #R PACKAGE 🔥The easystats {modelbased} package (the successor of #ggeffects) is now published in JOSS joss.theoj.org/papers/10.2110… Check it out for a demystification of marginal means, contrasts and effects #rlang