Dear Youri,
I hope this message finds you well.
Thank you again for your outstanding work on the jYS module in Jamovi.
I would like to suggest adding a new feature to your module, inspired by the R package UncertainInterval (https://github.com/HansLandsheer/UncertainInterval). This package allows for the definition of a "gray zone" around test scores—a three-zone approach (positive, uncertain, or negative)—which is particularly useful for interpreting test results, especially in a medical diagnostic context.
Integrating this feature into jYS could offer several significant advantages:
Explicit identification of uncertainty: By clearly distinguishing “inconclusive” scores, it enhances analytical rigor and reduces the risk of misinterpretation near threshold values.
Improved clinical decision-making: A three-zone approach (positive/uncertain/negative) helps avoid overly simplistic binary classifications.
Thank you again for your attention and openness to innovation.
Kind regards,
jYS module into Jamovi library
Re: jYS module into Jamovi library
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 will think about solving this issue.
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 will think about solving this issue.
Re: jYS module into Jamovi library
Thank you Dear Youri
Re: jYS module into Jamovi library
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 attention to my work!
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 attention to my work!