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:Beat Hulliger [aut, cre], Martin Sterchi [ctb], Tobias Schoch [ctb]

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NEWS

# Install 'modi' in R:
install.packages('modi', repos = c('https://martinster.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/martinster/modi/issues

Datasets:
  • 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.

On CRAN:

6.00 score 4 stars 1 packages 84 scripts 907 downloads 22 exports 2 dependencies

Last updated 2 years agofrom:43d1a676a4. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winNOTEOct 27 2024
R-4.5-linuxNOTEOct 27 2024
R-4.4-winOKOct 27 2024
R-4.4-macOKOct 27 2024
R-4.3-winOKOct 27 2024
R-4.3-macOKOct 27 2024

Exports:BEMEA.distEAdetEAimpEM.normalERER.normalGIMCDind.dijind.dijsMDmissnz.minplotITPlotMDPOEMpsi.lismisweep.operatorTRCweighted.quantileweighted.varweightsumWinsimp

Dependencies:MASSnorm

Introduction to modi

Rendered frommodi_vignette.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2023-03-13
Started: 2018-07-24