CompositionalRF-package {CompositionalRF}R Documentation

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: 1.0
Date: 2024-10-01
License: GPL-2

Maintainers

Michail Tsagris <mtsagris@uoc.gr>

Author(s)

Michail Tsagris mtsagris@uoc.gr.

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.

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.


[Package CompositionalRF version 1.0 Index]