Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner congenial with three prototypical multilevel modeling analyses: (1) a random intercept model, (2) a random slope model, and (3) a cross-level interaction model. Following these analysis examples, I review and discuss both software packages.