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: 0.1.2 Depends: R (≥ 3.5.0) Imports: MASS (≥ 7.3-50), norm (≥ 1.0-9.5), stats, graphics, utils Suggests: knitr, rmarkdown, survey, testthat Published: 2023-03-14 DOI: 10.32614/CRAN.package.modi Author: Beat Hulliger [aut, cre], Martin Sterchi [ctb], Tobias Schoch [ctb] Maintainer: Beat Hulliger <beat.hulliger at fhnw.ch> BugReports: https://github.com/martinSter/modi/issues License: MIT + file LICENSE URL: https://github.com/martinSter/modi NeedsCompilation: no Language: en-GB Citation: modi citation info Materials: README, NEWS In views: MissingData CRAN checks: modi results [issues need fixing before 2025-09-03] Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=modi to link to this page.
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