# How to deal with missing data in mixed effects (or multi-level) models?

I am curious about strategies for dealing with missing data in mixed effects (or multi-level models). By default, as far as I understand, many software tools use listwise deletion by default, so that cases with missing values for any of the variables are removed from the analysis. This is the case, for example, for the lme4 software in R. Is this typical for other software? Are there tools except for imputation for using all the available data, as in casewise deletion?