Calculating MDE for a difference-in-difference clustered randomized trial (in R)

Question

I'm looking to calculate the Minimum Detectable Effect for a potential Difference-in-Differences design where treatment and control are assigned at the cluster level and my outcome at the individual level is dichotomous. To do this I'm working in R and using the clusterPower package, specifically I'm using the cpa.did.binary function. In the help file for this function it notes that d is "The expected absolute difference." I'm interpreting this as being a Minimum Detectable Effect, is that correct? If this is the MDE, is this output the expected difference in logits?

Thanks to anyone that can help. Alternatively, if you have a better package or way of calculating MDE that is also welcome.

# Input
cpa.did.binary(power = .8,
               nclusters = 10,
               nsubjects = 100,
               p = .5,
               d = NA,
               ICC = .04,
               rho_c = 0,
               rho_s = 0)
# Output
>        d 
>0.2086531