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: 1.8.4
Depends: R (≥ 2.0), mvtnorm, car, lattice, combinat
Suggests: testthat
Published: 2020-03-03
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_1.8.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: bgmm_1.8.4.tgz, r-oldrel: bgmm_1.8.4.tgz
Old sources: bgmm archive

Reverse dependencies:

Reverse depends: joda
Reverse imports: ggrasp


Please use the canonical form to link to this page.