LDATree: Oblique Classification Trees with Uncorrelated Linear Discriminant Analysis Splits

A classification tree method that uses Uncorrelated Linear Discriminant Analysis (ULDA) for variable selection, split determination, and model fitting in terminal nodes. It automatically handles missing values and offers visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2410.23147>.

Version: 0.2.0
Imports: folda, ggplot2, grDevices, magrittr, stats, utils, visNetwork
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2024-10-31
Author: Siyu Wang ORCID iD [aut, cre, cph]
Maintainer: Siyu Wang <iamwangsiyu at gmail.com>
BugReports: https://github.com/Moran79/LDATree/issues
License: MIT + file LICENSE
URL: https://github.com/Moran79/LDATree, http://iamwangsiyu.com/LDATree/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: LDATree results

Documentation:

Reference manual: LDATree.pdf
Vignettes: Introduction to LDATree (source, R code)

Downloads:

Package source: LDATree_0.2.0.tar.gz
Windows binaries: r-devel: LDATree_0.1.2.zip, r-release: LDATree_0.1.2.zip, r-oldrel: LDATree_0.1.2.zip
macOS binaries: r-release (arm64): LDATree_0.1.2.tgz, r-oldrel (arm64): LDATree_0.1.2.tgz, r-release (x86_64): LDATree_0.1.2.tgz, r-oldrel (x86_64): LDATree_0.1.2.tgz
Old sources: LDATree archive

Linking:

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