ClustImpute: K-Means Clustering with Build-in Missing Data Imputation

This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.

Version:	0.2.4
Imports:	ClusterR, copula, dplyr, magrittr, tidyr, ggplot2, rlang, knitr
Suggests:	ggExtra, rmarkdown, testthat (≥ 2.1.0), Hmisc, tictoc, spelling, corrplot, covr
Published:	2021-05-31
Author:	Oliver Pfaffel
Maintainer:	Oliver Pfaffel <opfaffel at gmail.com>
License:	GPL-3
NeedsCompilation:	no
Language:	en-US
Citation:	ClustImpute citation info
Materials:	README NEWS
In views:	MissingData
CRAN checks:	ClustImpute results

Documentation:

Reference manual:	ClustImpute.pdf
Vignettes:	Example_on_simulated_data Description of the algorithm

Downloads:

Package source:	ClustImpute_0.2.4.tar.gz
Windows binaries:	r-devel: ClustImpute_0.2.4.zip, r-release: ClustImpute_0.2.4.zip, r-oldrel: ClustImpute_0.2.4.zip
macOS binaries:	r-release (arm64): ClustImpute_0.2.4.tgz, r-oldrel (arm64): ClustImpute_0.2.4.tgz, r-release (x86_64): ClustImpute_0.2.4.tgz, r-oldrel (x86_64): ClustImpute_0.2.4.tgz
Old sources:	ClustImpute archive

Reverse dependencies:

Reverse suggests:

FeatureImpCluster

Linking:

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