The bgmm package implements two partially supervised
mixture modeling methods: soft-label and belief-based modeling.
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.
Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
Tiuryn (2012), The R Package bgmm: Mixture Modeling with
Uncertain Knowledge, Journal of Statistical Software.

The bgmm package implements two partially supervised
mixture modeling methods: soft-label and belief-based modeling.
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.

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.

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>.