A common problem faced by statistical offices is that data may be missing from collected data sets. The typical way to overcome this problem is to impute the missing data. The problem of imputing missing data is complicated by the fact that statistical data often have to satisfy certain edit rules and that values of variables sometimes have to sum up to known totals. Standard imputation methods for numerical data as described in the literature generally do not take such edit rules and totals into account. In the present paper we describe algorithms for imputation of missing numerical data that do take edit restrictions into account and that ensure that sums are calibrated to known totals.