CompositionalML: Machine Learning with Compositional Data

Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics–Theory and Methods, 52(16): 5535–5567. <doi:10.1080/03610926.2021.2014890>.

Version: 1.0
Depends: R (≥ 4.0)
Imports: Boruta, Compositional, doParallel, e1071, foreach, graphics, ranger, Rfast, Rfast2, stats
Published: 2024-03-14
DOI: 10.32614/CRAN.package.CompositionalML
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: CompositionalML results


Reference manual: CompositionalML.pdf


Package source: CompositionalML_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): CompositionalML_1.0.tgz, r-oldrel (arm64): CompositionalML_1.0.tgz, r-release (x86_64): CompositionalML_1.0.tgz, r-oldrel (x86_64): CompositionalML_1.0.tgz


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