Multivariate random forests with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, or the alpha-transformation of Tsagris, Preston and Wood (2011), <doi:10.48550/arXiv.1106.1451>, and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), <doi:10.1093/bioinformatics/btw765>, is applied.

Spatial regression models with compositional responses using the alpha--transformation. Relevant papers include: Tsagris M. (2025), <doi:10.48550/arXiv.2510.12663>, Tsagris M. (2015), <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>, Tsagris M.T., Preston S. and Wood A.T.A. (2011), <doi:10.48550/arXiv.1106.1451>.

Provides functions for the consistent analysis of compositional 
  data (e.g. portions of substances) and positive numbers (e.g. concentrations) 
  in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.

Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions).
 Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>,
 statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>,
 Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al (2019)<DOI:10.1016/j.cmpb.2018.10.020>.
 Algorithms for generating correlated gene expressions are also available.
 Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for
 sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) <DOI:10.3390/biomedicines11030797> and
 factorial survival analyses (Emura et al 2024 Stat Methods Med Res) <DOI:10.1177/09622802231215805>.

Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and quantifying impacts of compound events in hydrology and climatology. Please see Hao Zengchao et al. (2019) <doi:10.1088/1748-9326/ab4df5>.