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 gmail.com>
License:	GPL-3
URL:	http://bgmm.molgen.mpg.de/
NeedsCompilation:	no
Citation:	bgmm citation info
In views:	Cluster
CRAN checks:	bgmm results [issues need fixing before 2026-03-17]

Documentation:

Reference manual:

bgmm.html , bgmm.pdf

Downloads:

Package source:	bgmm_1.8.5.tar.gz
Windows binaries:	r-devel: bgmm_1.8.5.zip, r-release: bgmm_1.8.5.zip, r-oldrel: bgmm_1.8.5.zip
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

Linking:

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