My instinct is that "relative" may be more appropriate than "absolute". The paper suggests use the "maxResMS" (a.k.a., "maximum variance estimate") times 1e-3 to adjust variance estimates. It seems like it would be more statistically valid under a permutation test to treat all permutations (including the "real" permutation) equally, in the sense that the maxResMS should be calculated for each permutation (dependent on that permutation's data) instead of being derived from the "real" distribution and then applied to all permutations.

I'm not quite clear about what you mean my correlating the tests... any variance adjustment will correlate them to some extent, absolute or relative. I'm okay with stat_fun returning results that are permutation dependent, in the sense that the mean and variance estimates must depend on the distribution of a given permutation's data -- this is what makes me think that fixing any adjustment across all permutations is slightly odd. In a toy case where under the standard permutation (real conditions), the mean and variance estimates happen to be 1000 times higher than under one of the permutations, would it make sense to basically set all the variances in that test permutation to at least twice their original value...?

In any case, I shot an email to Ridgway to ask, we'll see if he responds. Maybe I'll ask Hari, too... I'd like to know which way is more "correct" or appropriate for a given scenario.