CompositionalML
Machine Learning with Compositional Data
Description
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>.
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package dependencies
Packages required but not available: 'Compositional', 'Rfast2' See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’ manual.
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ERROR 13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Mar 9, 2026
package dependencies
Packages required but not available: 'Compositional', 'Rfast2' See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’ manual.