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CompositionalML (version 1.0)

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. and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. .

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Version

Install

install.packages('CompositionalML')

Monthly Downloads

153

Version

1.0

License

GPL (>= 2)

Maintainer

Michail Tsagris

Last Published

March 14th, 2024

Functions in CompositionalML (1.0)

alfa-PPR with compositional predictor variables

\(\alpha\)-PPR with compositional predictor variables
Tuning the parameters of the alpha-SVM

Tuning the parameters of the \(\alpha\)-SVM
alpha-RF

\(\alpha\)-RF
alpha-Boruta

\(\alpha\)-Boruta variable selection
alpha-SVM

\(\alpha\)-SVM
CompositionalML-package

Machine Learning with Compositional Data
Tuning the parameters of the alfa-PPR

Tuning the parameters of the\(\alpha\)-PPR
Tuning the parameters of the alpha-RF

Tuning the parameters of the \(\alpha\)-RF