Dirichlet Random Forest R package DirichletRF version 0.2.0 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). Software Imports: Rcpp (>= 1.0.0), parallel LinkingTo: Rcpp Suggests: testthat (>= 3.0.0) Khaled Masoumifard <masoumifardk@yahoo.com> Comprehensive R Archive Network (CRAN) Khaled Masoumifard [aut, cre] (ORCID: <https://orcid.org/0000-0003-1921-2145>), Stephan van der Westhuizen [aut] (ORCID: <https://orcid.org/0000-0001-9469-8427>), Sugnet Lubbe [aut] (ORCID: <https://orcid.org/0000-0003-2762-9944>) GPL-3 2026-07-23 application/tgz https://CRAN.R-project.org/package=DirichletRF doi:10.32614/CRAN.package.DirichletRF