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: |
0.1.2 |
| 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 |
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