svmodt: Linear SVM-Based Recursive Decision Trees

Implements Support Vector Machine Oblique Decision Trees (SVMODT). Recursively builds classification trees using linear Support Vector Machines (SVM) hyperplanes at each node instead of axis-parallel splits, creating oblique decision boundaries. Features include multiple feature selection methods, dynamic feature subset strategies, class weight support for imbalanced datasets, pruning and feature penalization.

Version:	0.1.0
Depends:	R (≥ 3.5)
Imports:	rlang, e1071, FSelectorRcpp, ggplot2
Suggests:	knitr, rmarkdown, bookdown, testthat (≥ 3.0.0), rpart, rsample, gridExtra, tidyr, kableExtra, palmerpenguins, dplyr
Published:	2026-06-30
DOI:	10.32614/CRAN.package.svmodt (may not be active yet)
Author:	Aneesh Agarwal [aut, cre, cph], Jack Jewson [aut, ths], Erik Sverdrup [aut, ths]
Maintainer:	Aneesh Agarwal <aaga0022 at student.monash.edu>
BugReports:	https://github.com/AneeshAgarwala/svmodt/issues
License:	GPL (≥ 3)
URL:	https://github.com/AneeshAgarwala/svmodt
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	svmodt results

Documentation:

Reference manual:	svmodt.html , svmodt.pdf
Vignettes:	Constructing Oblique Trees with svmodt (source, R code)

Downloads:

Package source:	svmodt_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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