Algorithms for multivariate outlier detection when missing values occur.
Algorithms are based on Mahalanobis distance or data depth. Imputation is based
on the multivariate normal model or uses nearest neighbour donors. The algorithms
take sample designs, in particular weighting, into account. The methods are
described in Bill and Hulliger (2016)