easystats @easystats4u
Official channel of {easystats}, a collection of #rstats 📦s with a unifying and consistent framework for statistical modeling, visualization, and reporting easystats.github.io/easystats/ worldwide Joined January 2020-
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🤯📈 TAKE YOUR STATS SKILLZ TO THE NEXT LEVEL WITH THIS #R PACKAGE 🔥The @easystats4u {modelbased} package (the successor of #ggeffects) is now published in @JOSS_TheOJ joss.theoj.org/papers/10.2110… Check it out for a demystification of marginal means, contrasts and effects #Rlang
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?
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?
{bayestestR} makes it now much easier to process inputs from packages {marginaleffects}, {emmeans}, or random variable types from posterior draws! #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 on bluesky now! Follow us under @easystats.bsky.social #easystats #rstats
NEW to {bayestestR} dev version - support for #marginaleffects! Take it for a test drive: remotes::install_github("easystats/bayestestR") @easystats4u #rstats
A short update on this feature: - Improved documentation (easystats.github.io/performance/re…) - Streamlined text output - Improved plots #rstats #easystats #DAG
A short update on this feature: - Improved documentation (easystats.github.io/performance/re…) - Streamlined text output - Improved plots #rstats #easystats #DAG https://t.co/W9bpgJbH5X
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!
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.
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
Today Tom Geva (from @bengurionu's Statisticas and Data Analysis program) presented the work he's done over the last few months for @easystats4u's correlation package 👏
#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…
And another "small but great" piece in our #easystats collection...
And another "small but great" piece in our #easystats collection...
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
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
🚨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
(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 @easystats4u. smart-workshops.com/rmlm-info
(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 @easystats4u. smart-workshops.com/rstats-info

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