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>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: MASS (≥ 7.3-50), norm (≥ 1.0-9.5), stats, graphics, utils
Suggests: knitr, rmarkdown, testthat
Published: 2018-11-20
Author: Beat Hulliger [aut], Martin Sterchi [cre]
Maintainer: Martin Sterchi <martin.sterchi at fhnw.ch>
BugReports: https://github.com/martinSter/modi/issues
License: MIT + file LICENSE
URL: https://github.com/martinSter/modi
NeedsCompilation: no
Citation: modi citation info
Materials: README
CRAN checks: modi results

Downloads:

Reference manual: modi.pdf
Vignettes: Introduction to modi
Package source: modi_0.1.0.tar.gz
Windows binaries: r-devel: modi_0.1.0.zip, r-devel-gcc8: modi_0.1.0.zip, r-release: modi_0.1.0.zip, r-oldrel: modi_0.1.0.zip
OS X binaries: r-release: modi_0.1.0.tgz, r-oldrel: modi_0.1.0.tgz

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