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 .


The CRAN version is here: http://cran.r-project.org/web/packages/bgmm/

Detailed description of this package is available in paper: The R Package bgmm: Mixture Modeling with Uncertain Knowledge Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn JSS Vol. 47, Issue 3, Apr 2012 http://www.jstatsoft.org/v47/i03/

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Reference manual

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install.packages("bgmm")

1.8.3 by Przemyslaw Biecek, 2 years ago


http://bgmm.molgen.mpg.de/


Browse source code at https://github.com/cran/bgmm


Authors: Przemyslaw Biecek \& Ewa Szczurek


Documentation:   PDF Manual  


Task views: Cluster Analysis & Finite Mixture Models


GPL-3 license


Depends on mvtnorm, car, lattice, combinat

Suggests testthat


Imported by ggrasp.


See at CRAN