jamovi

Hi, For regression analysis of explainers (Evaluation of MAR explainers), all variables (complete + incomplete) are used. The basis for imputation is created using the full array of variables you selected. Including incomplete variables in a separate group allows you to form output tables and pseudo...

What is multilevel regression? If it is multinomial logistic, then the standardized indicators are the odds ratios i.e. exp(coefficient), not the beta coefficients.

I can suggest using the new function "Univariate outliers identification and removal" in the jYS module.
3*SD is traditional Z-score method.
But you can use modified Z-score for median estimation for non normality assumption.

Dear Jamoviers! The "Univariate outliers identification and removal" function has been added to the jYS module (ver. 1.0.10). Removing outliers is done at your discretion by creating pseudo-variables. Univariate hints that multivariate will emerge over time. Great thanks you for your atten...

Dear @rabouqal, unfortunately, the UncertainInterval module is very old and has been removed from the CRAN repository. Of course, it can be installed directly from github as remote, but there may be problems. The "grayzone" topic is very interesting and is available in other modules. I wil...

Gabriel Tiraboschi wrote: ↑Wed Jun 25, 2025 3:02 am Thank you so much Yuri! Complete variables sounds better. If you want to add more clarity to your module, I would suggest to write some instructions/documentation and show it as the user loads the module. The module jReshape is a good example.

Thank you for your work

Thanks! It will be done.

Once more minor note:
Jamovi <=2.6.44 sometimes incorrectly calculate = 0 OOB (out-of-bag error) for missRangher method. I didn't find why.
Version 2.7.0 (near future release with R 4.5.0) do it well.

Thank you so much for your reply Yuri! If you allow me one more question: I used this package in R-studio and it doesn't ask me for a "training variable". Could you please clarify why we need a training variable for the Little's test in your module? Thanks Gabriel Training Variables and I...

This is a translation of the R module missr in jamovi
Little’s missing completely at random (MCAR) test
https://cran.r-project.org/web/packages/missr/missr.pdf

Dear translators!
With considerable support from Jonathon, I was able to place the translation files of the jYS module in the Weblate system.
Please participate in translating it into your language.
I have plans for the future to interest authors of other modules in integrating into Weblate.