What is the advantage (if any) of using Cohen's $\omega$ instead of Cramer's V for effect size? I'm wondering why I should or should not use Cohen's $\omega$ (omega) for effect size over Cramer's V for a chi-square goodness-of-fit test?
What would be the advantage or disadvantage of favoring Cohen's $\omega$?
I am using the rcompanion package with the cohenW() and cramerV() functions.

As far as I know, neither Cohen's w nor Cramér’s V is routinely used for a goodness-of-fit test. (Edit: see the comments following this answer.)
Cramér’s V can be modified for use in a goodness-of-fit test.  If the theoretical proportions for the test are all equal, Cramer's V is relatively easy to interpret.  It's more difficult to interpret if the proportions are not equal.
Considering a test of association, Cramér’s V is a popular effect size statistic, and has the advantage of being confined to the range of 0 to 1.
Cohen's w doesn't have this quality, and may have popularity simply because it was presented by Jacob Cohen (Statistical Power Analysis for the Behavioral Sciences, 1988) and is used there in power analysis.
