Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').
| Version: | 1.0 |
| Depends: | mvtnorm, sn |
| Published: | 2015-02-27 |
| Author: | Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari |
| Maintainer: | Alessandro Barbiero <alessandro.barbiero at unimi.it> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| In views: | MissingData |
| CRAN checks: | GenForImp results |
| Reference manual: | GenForImp.pdf |
| Package source: | GenForImp_1.0.tar.gz |
| Windows binaries: | r-devel: GenForImp_1.0.zip, r-release: GenForImp_1.0.zip, r-oldrel: GenForImp_1.0.zip |
| macOS binaries: | r-release: GenForImp_1.0.tgz, r-oldrel: GenForImp_1.0.tgz |
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