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CompositionalML
View on CRAN: Click
here
Download and install CompositionalML package within the R console
Install from CRAN:
install.packages("CompositionalML")
Install from Github:
library("remotes")
install_github("cran/CompositionalML")
Install by package version:
library("remotes")
install_version("CompositionalML", "1.0")
Attach the package and use:
library("CompositionalML")
Maintained by
Michail Tsagris
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.CompositionalML . CompositionalML results . CompositionalML.pdf . CompositionalML_1.0.tar.gz . CompositionalML_1.0.zip . CompositionalML_1.0.zip . CompositionalML_1.0.zip . CompositionalML_1.0.tgz . CompositionalML_1.0.tgz . CompositionalML_1.0.tgz . CompositionalML_1.0.tgz . CompositionalML_1.0.tgz . CompositionalML_1.0.tgz . https://CRAN.R-project.org/package=CompositionalML .
First Published: 2024-03-14
Latest Update: 2024-03-14
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. .
How to cite:
Michail Tsagris (2024). CompositionalML: Machine Learning with Compositional Data. R package version 1.0, https://cran.r-project.org/web/packages/CompositionalML. Accessed 17 Feb. 2025.
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