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bgmm  

Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling
View on CRAN: Click here


Download and install bgmm package within the R console
Install from CRAN:
install.packages("bgmm")

Install from Github:
library("remotes")
install_github("cran/bgmm")

Install by package version:
library("remotes")
install_version("bgmm", "1.8.5")



Attach the package and use:
library("bgmm")
Maintained by
Przemyslaw Biecek
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2011-01-31
Latest Update: 2021-10-10
Description:
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 .
How to cite:
Przemyslaw Biecek (2011). bgmm: Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling. R package version 1.8.5, https://cran.r-project.org/web/packages/bgmm. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0 (2011-01-31 21:05), 1.1 (2011-02-11 18:05), 1.2 (2011-02-16 12:36), 1.3 (2011-08-25 22:08), 1.4 (2011-12-07 11:52), 1.5 (2012-03-29 18:45), 1.6 (2013-11-19 07:39), 1.7 (2014-12-19 15:57), 1.8.3 (2017-02-27 11:44), 1.8.4 (2020-03-03 14:00)
Other packages that cited bgmm R package
View bgmm citation profile
Other R packages that bgmm depends, imports, suggests or enhances
Complete documentation for bgmm
Functions, R codes and Examples using the bgmm R package
Some associated functions: CellCycle . DEprobs . Ste12 . bgmm-package . chooseModels . crossval . genotypes . getModelStructure . init.model.params . mModel . mModelList . miRNA . plot.mModel . plot.mModelList . plotGIC . predict.mModel . simulateData . tools . 
Some associated R codes: Full bgmm package functions and examples
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