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.
Michail Tsagris mtsagris@uoc.gr.
Michail Tsagris <mtsagris@uoc.gr>
| Package: | CompositionalRF |
| Type: | Package |
| Version: | 1.3 |
| Date: | 2025-07-10 |
| License: | GPL-2 |
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.
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.