CompositionalRF-package: Multivariate Random Forests with Compositional Responses

In CompositionalRF: Multivariate Random Forest with Compositional Responses

Multivariate Random Forests with Compositional Responses

Description

Multivariate random forest with compositional response variables and continuous predictor variables. The 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.

Details

Package:	CompositionalRF
Type:	Package
Version:	1.4
Date:	2025-09-07
License:	GPL-2

Maintainers

Michail Tsagris <[e-mail]>

Author(s)

Michail Tsagris [e-mail].

References

Rahman R., Otridge J. and Pal R. (2017). IntegratedMRF: random forest-based framework for integrating prediction from different data types. Bioinformatics, 33(9): 1407–1410.

Segal M. and Xiao Y. (2011). Multivariate random forests. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1): 80–87.

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

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

Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin.

CompositionalRF documentation built on Sept. 9, 2025, 5:43 p.m.