bgmm

R package bgmm. 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 <<a href=”http://dx.doi.org/10.18637/jss.v047.i03”>doi:10.18637/jss.v047.i03</a>>.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 4 articles , 1 standard article )

Showing results 1 to 4 of 4.
Sorted by year (citations)

  1. Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv
  2. Rémi Lebret; Serge Iovleff; Florent Langrognet; Christophe Biernacki; Gilles Celeux; Gérard Govaert: Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library (2015) not zbMATH
  3. Sharon X. Lee, Geoffrey J. McLachlan: EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions (2015) arXiv
  4. Przemyslaw Biecek; Ewa Szczurek; Martin Vingron; Jerzy Tiuryn: The R Package bgmm: Mixture Modeling with Uncertain Knowledge (2012) not zbMATH