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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install.packages("modi")

0.1.0 by Martin Sterchi, 3 months ago


https://github.com/martinSter/modi


Report a bug at https://github.com/martinSter/modi/issues


Browse source code at https://github.com/cran/modi


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