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Is your feature request related to a problem? Please describe.
No, not a problem just out of convenience.
Describe the solution you'd like
I think the plots concurve provides are very easy to interpret and visually pleasing. However, the curves can only be created if you have the original data. For example, for the functions for compare means you have to have two "vectors" of data x & y (I assume if paired the function calculates the difference scores). I think it would be helpful if there was an option/function that produces the consonance plots from the summary statistics (preferably the mean difference and standard error). With this option, data presented in the scientific literature (where the reader most likely does not have access to the existing data) could still be visualized with consonance plots. It would also provide more flexibility if the analyses individuals are performing aren't supported by the functions in the package (e.g., emmeans).
Describe alternatives you've considered
None at this time.
Additional context
I can start giving it look and help write the function if needed.
Great work on this package Andrew and Zad!
The text was updated successfully, but these errors were encountered:
Thanks Aaron, I just merged the pull requests, do you think you could possibly make an example to use for curve_rev with the "default" setting, especially for likelihoods? Then we could also do quick comparisons with the consonance function
Is your feature request related to a problem? Please describe.
No, not a problem just out of convenience.
Describe the solution you'd like
I think the plots concurve provides are very easy to interpret and visually pleasing. However, the curves can only be created if you have the original data. For example, for the functions for compare means you have to have two "vectors" of data x & y (I assume if paired the function calculates the difference scores). I think it would be helpful if there was an option/function that produces the consonance plots from the summary statistics (preferably the mean difference and standard error). With this option, data presented in the scientific literature (where the reader most likely does not have access to the existing data) could still be visualized with consonance plots. It would also provide more flexibility if the analyses individuals are performing aren't supported by the functions in the package (e.g., emmeans).
Describe alternatives you've considered
None at this time.
Additional context
I can start giving it look and help write the function if needed.
Great work on this package Andrew and Zad!
The text was updated successfully, but these errors were encountered: