classifly: Explore Classification Models in High Dimensions

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.

Version: 0.4.1
Imports: class, plyr, stats
Suggests: e1071, MASS, rpart
Published: 2022-05-20
DOI: 10.32614/CRAN.package.classifly
Author: Hadley Wickham
Maintainer: Hadley Wickham <h.wickham at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS ChangeLog
CRAN checks: classifly results


Reference manual: classifly.pdf


Package source: classifly_0.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): classifly_0.4.1.tgz, r-oldrel (arm64): classifly_0.4.1.tgz, r-release (x86_64): classifly_0.4.1.tgz, r-oldrel (x86_64): classifly_0.4.1.tgz
Old sources: classifly archive


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