I have patient blood data on lots of analytes that are reasonably approximated by normal distributions (after suitable transformations). But depending on patient type, the covariance structure changes; they could change in tightness and direction, as illustrated in the graph below. Is there an imputation algorithm that is best suited for this situation, able to compute separate covariance structures depending on indicator variables? There is a lot of confusing advice out there on which algorithm is best.