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:dragon: Compute and work with indices of effect size and standardized parameters

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Should return something like this: ![image](https://user-images.githubusercontent.com/35330040/183286344-a558a4ee-d98e-451f-bd93-27c2d498f7ef.png) Inputs: - Rules - title (optional) - value name (optional) --- This can then be used to generate the docs / vignettes more easily.

low priority 😴

Currently the cohens d (etc) values we compute for paired designs are mean standardized difference scores (dz). Those are rarely what folks think about when they say "Cohen's d" and...

enhancement 🔥

Some code I wrote a while back for computing SMDs from lm output. Could be useful to report effect sizes in terms of standardized betas and Cohen's ds. Most important...

enhancement 🔥

It would be nice to include an option to add the SE or variance in `cohen_d()`, etc. to facilitate use of effectsize with meta-analyses.

enhancement 🔥

Weighting is already implemented in `standardize.default()` and `standardize_parameters()` (and `eta_squared()` via the fit models). Would be nice to have this also (with `weights = ` arg) in: - [ ]...

enhancement 🔥

https://github.com/melissagwolf/dynamic/issues/3 ```R get_dynamic

enhancement 🔥
low priority 😴

(Originally part of #5 ) https://doi.org/10.1037/1082-989X.8.4.434

enhancement 🔥
help wanted 🔍
low priority 😴

I am thinking about this bit from the documentation: ```r #' @section CI Does Not Contain the Estimate: #' For very large sample sizes, the width of the CI can...

enhancement 🔥
WIP 👷‍♂️

**Describe the bug** I was doing a comparison between the results of `emmeans::eff_size()` and `effectsize::t_to_d()`, and come up with very different results **To Reproduce** ``` r require(effectsize) #> Loading required...

Discussion 🦜

Just saw this preprint that cites performance: https://www.biorxiv.org/content/biorxiv/early/2020/07/26/2020.07.26.221168.full.pdf > Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model...

enhancement 🔥
low priority 😴