bgmm: Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling (original) (raw)
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 |
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