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wNNSel  

Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables
View on CRAN: Click here


Download and install wNNSel package within the R console
Install from CRAN:
install.packages("wNNSel")

Install from Github:
library("remotes")
install_github("cran/wNNSel")

Install by package version:
library("remotes")
install_version("wNNSel", "0.1")



Attach the package and use:
library("wNNSel")
Maintained by
Shahla Faisal
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-11-09
Latest Update: 2017-11-09
Description:
New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.
How to cite:
Shahla Faisal (2017). wNNSel: Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables. R package version 0.1, https://cran.r-project.org/web/packages/wNNSel. Accessed 21 Nov. 2024.
Previous versions and publish date:
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Complete documentation for wNNSel
Functions, R codes and Examples using the wNNSel R package
Some associated functions: artifNA.cv . artifNA . computeMAIE . computeMSIE . computeNRMSE . cv.wNNSel . wNNSel-package . wNNSel.impute . wNNSel . 
Some associated R codes: artifNA.R . artifNA.cv.R . convex.func.R . cv.wNNSel.R . dist.wNNSel.R . errors.R . kernel.weight.R . utils.R . wNNSel-Package.R . wNNSel.R . wNNSel.impute.R .  Full wNNSel package functions and examples
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