wNNSel: Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

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>.

Version: 0.1
Imports: stats
Published: 2017-11-09
Author: Shahla Faisal
Maintainer: Shahla Faisal <shahla_ramzan at yahoo.com>
License: GPL-2
NeedsCompilation: no
CRAN checks: wNNSel results

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Reference manual: wNNSel.pdf
Package source: wNNSel_0.1.tar.gz
Windows binaries: r-devel: wNNSel_0.1.zip, r-release: wNNSel_0.1.zip, r-oldrel: wNNSel_0.1.zip
OS X El Capitan binaries: r-release: wNNSel_0.1.tgz
OS X Mavericks binaries: r-oldrel: wNNSel_0.1.tgz

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