Non linear regression with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), <doi:10.1093/bioinformatics/btw765>, is applied.
Version: | 1.1 |
Depends: | R (≥ 4.0) |
Imports: | Compositional, doParallel, foreach, RcppParallel, Rcpp, Rfast, stats |
LinkingTo: | Rcpp, RcppParallel |
Published: | 2025-05-07 |
DOI: | 10.32614/CRAN.package.CompositionalRF |
Author: | Michail Tsagris [aut, cre], Christos Adam [aut] |
Maintainer: | Michail Tsagris <mtsagris at uoc.gr> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
SystemRequirements: | GNU make |
CRAN checks: | CompositionalRF results |
Reference manual: | CompositionalRF.pdf |
Package source: | CompositionalRF_1.1.tar.gz |
Windows binaries: | r-devel: CompositionalRF_1.0.zip, r-release: CompositionalRF_1.0.zip, r-oldrel: CompositionalRF_1.0.zip |
macOS binaries: | r-release (arm64): CompositionalRF_1.1.tgz, r-oldrel (arm64): CompositionalRF_1.1.tgz, r-release (x86_64): CompositionalRF_1.1.tgz, r-oldrel (x86_64): CompositionalRF_1.1.tgz |
Old sources: | CompositionalRF archive |
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