The hard part about multiple imputation is the imputation, which
mice can do even if for some reason you can't use the additional pooling functionality it provides. A quick look at the AER package suggests that it is mostly data, with examples using many functions like
lm that certainly are compatible with
If you do find a particular analysis function that is not compatible with the pooling functions of
mice, proceed as follows, following the general procedure for multiple imputation:
Do your multiple imputations with
mice and keep each separate complete imputed data set.
Perform your analysis separately on each of the imputed data sets.
Based on your understanding of the nature of the analysis method and the goals of your study, pool the results of the separate analyses appropriately. This can be as simple as taking means and variances of coefficients over the multiple imputation analyses.
This process is much better than relying on single imputations, and may have the side effect of helping you learn more about how the particular analysis method in question works.