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ClustImpute
View on CRAN: Click
here
Download and install ClustImpute package within the R console
Install from CRAN:
install.packages("ClustImpute")
Install from Github:
library("remotes")
install_github("cran/ClustImpute")
Install by package version:
library("remotes")
install_version("ClustImpute", "0.2.4")
Attach the package and use:
library("ClustImpute")
Maintained by
Oliver Pfaffel
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-08-01
Latest Update: 2021-05-31
Description:
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.
How to cite:
Oliver Pfaffel (2019). ClustImpute: K-Means Clustering with Build-in Missing Data Imputation. R package version 0.2.4, https://cran.r-project.org/web/packages/ClustImpute. Accessed 06 Nov. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for ClustImpute
Functions, R codes and Examples using
the ClustImpute R package
Some associated functions: ClustImpute . check_replace_dups . default_wf . miss_sim . pipe . plot . predict . print . var_reduction .
Some associated R codes: ClustImpute.R . Missing_simulation.R . utils-pipe.R . Full ClustImpute package functions and examples
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