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

Documentation:

Reference manual:	modi.pdf
Vignettes:	Introduction to modi

Downloads:

Package source:	modi_0.1.2.tar.gz
Windows binaries:	r-devel: modi_0.1.2.zip, r-release: modi_0.1.2.zip, r-oldrel: modi_0.1.2.zip
macOS binaries:	r-release (arm64): modi_0.1.2.tgz, r-oldrel (arm64): modi_0.1.2.tgz, r-release (x86_64): modi_0.1.2.tgz, r-oldrel (x86_64): modi_0.1.2.tgz
Old sources:	modi archive

Reverse dependencies:

Reverse imports:	birdscanR
Reverse suggests:	semfindr, wbacon

Linking:

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