CRAN/E | CompositionalML

CompositionalML

Machine Learning with Compositional Data

Installation

About

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.

Key Metrics

Version 1.0
R ≥ 4.0
Published 2024-03-14 32 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks CompositionalML results

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Maintainer

Maintainer

Michail Tsagris

mtsagris@uoc.gr

Authors

Michail Tsagris

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 4.0

Imports

Boruta
Compositional
doParallel
e1071
foreach
graphics
ranger
Rfast
Rfast2
stats