Multivariate Outlier Detection and Imputation for Incomplete Survey Data

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) .


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0.1.0 by Martin Sterchi, 3 months ago

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Authors: Beat Hulliger [aut] , Martin Sterchi [cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports MASS, norm, stats, graphics, utils

Suggests knitr, rmarkdown, testthat

See at CRAN