bgmm: Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling
Version 1.8.3

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 .

AuthorPrzemyslaw Biecek \& Ewa Szczurek
Date of publication2017-02-27 11:44:36
MaintainerPrzemyslaw Biecek <Przemyslaw.Biecek@gmail.com>
LicenseGPL-3
Version1.8.3
URL http://bgmm.molgen.mpg.de/
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("bgmm")

Getting started

Package overview

Popular man pages

bgmm-package: Belief-Based Gaussian Mixture Modeling
chooseModels: Selecting a subset of fitted models
genotypes: Fluorescence signals corresponding to a given allele for 333...
getModelStructure: Model structure
plotGIC: Plotting GIC scores
plot.mModel: Plotting a Graphical Visualization of a Gaussian Model or a...
tools: Set of supplementary functions for bgmm package
See all...

All man pages Function index File listing

Man pages

bgmm-package: Belief-Based Gaussian Mixture Modeling
CellCycle: Data for clustering of 384 cell cycle genes into five...
chooseModels: Selecting a subset of fitted models
crossval: k-fold cross-validation for the specified model
DEprobs: Signed probabilities of differential expression
genotypes: Fluorescence signals corresponding to a given allele for 333...
getModelStructure: Model structure
init.model.params: Initiation of model parameters
miRNA: miRNA transfection data for miR1 and miR124 target genes
mModel: Fitting Gaussian Mixture Model
mModelList: Fitting Gaussian mixture model or collection of models
plotGIC: Plotting GIC scores
plot.mModel: Plotting a Graphical Visualization of a Gaussian Model or a...
plot.mModelList: Plotting a graphical visualization of a model or a list of...
predict.mModel: Predictions for fitted Gaussian component model
simulateData: Dataset generation
Ste12: Ste12 knockout data under pheromone treatment versus wild...
tools: Set of supplementary functions for bgmm package

Functions

Files

inst
inst/CITATION
tests
tests/testthat.R
tests/testthat
tests/testthat/test_bgmm.R
NAMESPACE
demo
demo/00Index
demo/bgmm.r
data
data/CellCycle.RData
data/genotypes.RData
data/Ste12.RData
data/miRNA.RData
R
R/soft.e.step.r
R/bgmm.m.step.r
R/semiList.r
R/clusterAssigment.r
R/get.simple.beliefs.r
R/soft.r
R/getModelSettings.r
R/plot.bgmm.r
R/bgmm.e.step.r
R/bgmm.r
R/plot.semiList.r
R/getSimulatedKdimensionalData.r
R/DEprobs.r
MD5
DESCRIPTION
man
man/plot.mModel.Rd
man/chooseModels.Rd
man/plot.mModelList.Rd
man/tools.Rd
man/getModelStructure.Rd
man/miRNA.Rd
man/init.model.params.Rd
man/plotGIC.Rd
man/mModel.Rd
man/genotypes.Rd
man/bgmm-package.Rd
man/predict.mModel.Rd
man/Ste12.Rd
man/mModelList.Rd
man/DEprobs.Rd
man/simulateData.Rd
man/crossval.Rd
man/CellCycle.Rd
bgmm documentation built on May 19, 2017, 1:24 p.m.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.