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.
- Version0.1.2
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Languageen-GB
- modi citation info
- Last release03/14/2023
Documentation
Team
Beat Hulliger
Tobias Schoch
Show author detailsRolesContributorMartin Sterchi
Show author detailsRolesContributor
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Last 30 days
This package has been downloaded 719 times in the last 30 days. More downloads than an obscure whitepaper, but not enough to bring down any servers. A solid effort! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 20 times.
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Last 365 days
This package has been downloaded 10,000 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Sep 30, 2024 with 92 downloads.
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Dependencies
- Imports2 packages
- Suggests4 packages
- Reverse Imports1 package
- Reverse Suggests2 packages