Package: modi 0.1.2
Beat Hulliger
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>.
Authors:
modi_0.1.2.tar.gz
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modi.pdf |modi.html✨
modi/json (API)
NEWS
| # Install 'modi' in R: |
| install.packages('modi', repos = c('https://martinster.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/martinster/modi/issues
- bushfire - Bushfire scars.
- bushfire.weights - Weights for Bushfire scars.
- bushfirem - Bushfire scars with missing data.
- lival - Living Standards Measurement Survey Albania 2012
- sepe - Sample Environment Protection Expenditure Survey.
Last updated 2 years agofrom:43d1a676a4. Checks:6 OK, 3 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 26 2025 |
| R-4.5-win | NOTE | Mar 26 2025 |
| R-4.5-mac | NOTE | Mar 26 2025 |
| R-4.5-linux | NOTE | Mar 26 2025 |
| R-4.4-win | OK | Mar 26 2025 |
| R-4.4-mac | OK | Mar 26 2025 |
| R-4.4-linux | OK | Mar 26 2025 |
| R-4.3-win | OK | Mar 26 2025 |
| R-4.3-mac | OK | Mar 26 2025 |
Exports:BEMEA.distEAdetEAimpEM.normalERER.normalGIMCDind.dijind.dijsMDmissnz.minplotITPlotMDPOEMpsi.lismisweep.operatorTRCweighted.quantileweighted.varweightsumWinsimp
