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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 |
| 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 |
| Reference manual: | modi.pdf |
| Vignettes: |
Introduction to modi |
| Package source: | modi_0.1.2.tar.gz |
| Windows binaries: | r-devel: modi_0.1.2.zip, r-release: modi_0.1.2.zip, r-oldrel: modi_0.1.2.zip |
| macOS binaries: | r-release (arm64): modi_0.1.2.tgz, r-oldrel (arm64): modi_0.1.2.tgz, r-release (x86_64): modi_0.1.2.tgz, r-oldrel (x86_64): modi_0.1.2.tgz |
| Old sources: | modi archive |
| Reverse imports: | birdscanR |
| Reverse suggests: | semfindr, wbacon |
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