Dirichlet Random Forest // Implementation of the Dirichlet Random Forest algorithm for compositional response data. Trees are grown using a Dirichlet log-likelihood splitting criterion, with maximum likelihood ('MLE') and method-of-moments ('MOM') parameter estimation. Provides averaging-based predictions (average of responses within terminal nodes), parameter-based predictions (expected value derived from the estimated Dirichlet parameters within terminal nodes), and distributional predictions represented as a weighted distribution over the training responses. Out-of-bag estimation and impurity- and permutation-based variable importance are also supported. For more details see Masoumifard, van der Westhuizen, and Gardner- Lubbe (2026, ISBN:9781032903910).