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DirichletRF: "Dirichlet Random Forest"

Implementation of the Dirichlet Random Forest algorithm for compositional response data. Supports maximum likelihood estimation ('MLE') and method-of-moments ('MOM') parameter estimation for the Dirichlet distribution. Provides two prediction strategies; averaging-based predictions (average of responses within terminal nodes) and parameter-based predictions (expected value derived from the estimated Dirichlet parameters within terminal nodes). For more details see Masoumifard, van der Westhuizen, and Gardner-Lubbe (2026, ISBN:9781032903910).

Version: 0.1.0
Imports: Rcpp (≥ 1.0.0), parallel
LinkingTo: Rcpp
Suggests: testthat (≥ 3.0.0)
Published: 2026-03-23
DOI: 10.32614/CRAN.package.DirichletRF
Author: Khaled Masoumifard ORCID iD [aut, cre], Stephan van der Westhuizen ORCID iD [aut], Sugnet Lubbe ORCID iD [aut]
Maintainer: Khaled Masoumifard <masoumifardk at yahoo.com>
License: GPL-3
NeedsCompilation: yes
CRAN checks: DirichletRF results

Documentation:

Reference manual: DirichletRF.html , DirichletRF.pdf

Downloads:

Package source: DirichletRF_0.1.0.tar.gz
Windows binaries: r-devel: DirichletRF_0.1.0.zip, r-release: DirichletRF_0.1.0.zip, r-oldrel: DirichletRF_0.1.0.zip
macOS binaries: r-release (arm64): DirichletRF_0.1.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): DirichletRF_0.1.0.tgz, r-oldrel (x86_64): DirichletRF_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=DirichletRF to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.