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.

Version: 1.7
Depends: R (≥ 2.0), mvtnorm, car, lattice, combinat
Published: 2014-12-19
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: NA
Materials: NA
In views: Cluster
CRAN checks: bgmm results

Downloads:

Reference manual: bgmm.pdf
Package source: bgmm_1.7.tar.gz
Windows binaries: r-devel: bgmm_1.7.zip, r-release: bgmm_1.7.zip, r-oldrel: bgmm_1.7.zip
OS X Snow Leopard binaries: r-release: bgmm_1.7.tgz, r-oldrel: bgmm_1.7.tgz
OS X Mavericks binaries: r-release: bgmm_1.7.tgz
Old sources: bgmm archive