1. Happy to share that ver. 1.1.1 of the dySEM 📦 for #dyadic SEM in #rstats is officially available on CRAN: cran.r-project.org/web/packages.... Huge credit goes to @omarjcamanto.bsky.social ; Omar has led a large overhaul of the “outputters” based on feedback from our workshop at #IARR2024 - ThreadSky

johnsakaluk.bsky.social • 49 days ago

1. Happy to share that ver. 1.1.1 of the dySEM 📦 for #dyadic SEM in #rstats is officially available on CRAN: https://cran.r-project.org/web/packages/dySEM/index.html.

Huge credit goes to @omarjcamanto.bsky.social ; Omar has led a large overhaul of the “outputters” based on feedback from our workshop at #IARR2024

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johnsakaluk.bsky.social•49 days ago

2. What’s changed?

We felt some functions were too tasked with doing too many different things, with increasingly busy argumentation.

We also wanted to ensure “outputters” returned model results in a way to ensure users could use output however they want (e.g., tables, plots, etc.)

johnsakaluk.bsky.social•49 days ago

3. We have therefore deprecated outputModel()—which was asked to do too much—and split its functionality into two more specific available functions: outputParamTab() and outputParamFig(). The former is suited for generating tables of output, the latter for path diagrams.

johnsakaluk.bsky.social•49 days ago

4. All tabling outputters will henceforth return tibbles, by default. A younger me thought it was sensible to return formatted tables (e.g., with {gt}). Current me recognizes people can do that if they want, but they should have autonomy to use {ggplot}, or a different tabling package, etc.

johnsakaluk.bsky.social•49 days ago

5. We also have two new outputters that we are excited about: outputInvarCompTab(), and outputConstraintTab().

outputInvarCompTab() facilitates (IMO) an improved dyadic invariance testing sequence.

outputConstraintTab() will help you get more analytic detail out of dyadic invariance testing.

johnsakaluk.bsky.social•49 days ago

6. outputInvarCompTab() allows traditional frequentist nested model comparisons in dyadic invariance testing, but sequenced from most parsimonious model (residual + intercept + loading invariant) to least (merely configurally invariant). A quick explanation of “why?”:

johnsakaluk.bsky.social•49 days ago

7. By default, {lavaan} + anova() (i.e., lavTestLRT()) tests invariance model sequencing such that least parsimonious model (configural) is baseline—a feature without override. I increasingly think in some contexts there is good reason to start with most constrained dyadic invariance model first.

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