bgmm: Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling

Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.

Version: 1.8.5
Depends: R (≥ 2.0), mvtnorm, car, lattice, combinat
Suggests: testthat
Published: 2021-10-10
DOI: 10.32614/CRAN.package.bgmm
Author: Przemyslaw Biecek \& Ewa Szczurek
Maintainer: Przemyslaw Biecek <Przemyslaw.Biecek at>
License: GPL-3
NeedsCompilation: no
Citation: bgmm citation info
In views: Cluster
CRAN checks: bgmm results


Reference manual: bgmm.pdf


Package source: bgmm_1.8.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bgmm_1.8.5.tgz, r-oldrel (arm64): bgmm_1.8.5.tgz, r-release (x86_64): bgmm_1.8.5.tgz, r-oldrel (x86_64): bgmm_1.8.5.tgz
Old sources: bgmm archive

Reverse dependencies:

Reverse imports: ggrasp


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