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modi: 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) <doi:10.17713/ajs.v45i1.86>.
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Package details |
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| Author | Beat Hulliger [aut, cre], Martin Sterchi [ctb], Tobias Schoch [ctb] |
| Maintainer | Beat Hulliger <beat.hulliger@fhnw.ch> |
| License | MIT + file LICENSE |
| Version | 0.1.2 |
| URL | https://github.com/martinSter/modi |
| Package repository | View on CRAN |
| Installation |
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