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It's a Super K-Nearest Neighbor classification method with using kernel density to describe weight of the distance between a training observation and the testing sample.
| Version: | 3.1 |
| Depends: | methods, stats |
| Published: | 2022-06-11 |
| Author: | Yi Ya [aut, cre], Nader Ebrahimi [aut], Yoram Rubin [aut], Jacob Zhang [aut] |
| Maintainer: | Yi Ya <Yi.YA_yaya at hotmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | SKNN results |
| Reference manual: | SKNN.pdf |
| Package source: | SKNN_3.1.tar.gz |
| Windows binaries: | r-devel: SKNN_3.1.zip, r-release: SKNN_3.1.zip, r-oldrel: SKNN_3.1.zip |
| macOS binaries: | r-release (arm64): SKNN_3.1.tgz, r-oldrel (arm64): SKNN_3.1.tgz, r-release (x86_64): SKNN_3.1.tgz, r-oldrel (x86_64): SKNN_3.1.tgz |
| Old sources: | SKNN archive |
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