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 uoc.gr> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no CRAN checks: CompositionalML results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=CompositionalML to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4