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bgmm
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]
[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 22 Dec. 2024.
Previous versions and publish date:
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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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