pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

Aims to utilize model-based clustering (unsupervised) for high dimensional and ultra large data, especially in a distributed manner. The code employs 'pbdMPI' to perform a expectation-gathering-maximization algorithm for finite mixture Gaussian models. The unstructured dispersion matrices are assumed in the Gaussian models. The implementation is default in the single program multiple data programming model. The code can be executed through 'pbdMPI' and MPI' implementations such as 'OpenMPI' and 'MPICH'. See the High Performance Statistical Computing website <> for more information, documents and examples.

Version: 0.2-1
Depends: R (≥ 3.0.0), pbdMPI (≥ 0.4-2)
Imports: methods, MASS
Enhances: MixSim
Published: 2021-02-11
DOI: 10.32614/CRAN.package.pmclust
Author: Wei-Chen Chen [aut, cre], George Ostrouchov [aut]
Maintainer: Wei-Chen Chen <wccsnow at>
MailingList: Please send questions and comments to
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: pmclust citation info
Materials: README ChangeLog
CRAN checks: pmclust results


Reference manual: pmclust.pdf
Vignettes: pmclust-guide


Package source: pmclust_0.2-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available
Old sources: pmclust archive


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