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 |
| 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-release: modi_0.1.0.zip, r-oldrel: modi_0.1.0.zip |
| macOS binaries: | r-release (arm64): modi_0.1.0.tgz, r-oldrel (arm64): modi_0.1.0.tgz, r-release (x86_64): modi_0.1.0.tgz, r-oldrel (x86_64): modi_0.1.0.tgz |
| Reverse imports: | OOI |
| Reverse suggests: | wbacon |
Please use the canonical form https://CRAN.R-project.org/package=modi to link to this page.