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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 |
| DOI: | 10.32614/CRAN.package.wNNSel |
| Author: | Shahla Faisal |
| Maintainer: | Shahla Faisal <shahla_ramzan at yahoo.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | wNNSel results |
| 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 |
| macOS binaries: | r-release (arm64): wNNSel_0.1.tgz, r-oldrel (arm64): wNNSel_0.1.tgz, r-release (x86_64): wNNSel_0.1.tgz, r-oldrel (x86_64): wNNSel_0.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.