Package: bgmm 1.8.6

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 <doi:10.18637/jss.v047.i03>.

Authors:Przemyslaw Biecek [cre], Ewa Szczurek [aut]

bgmm_1.8.6.tar.gz
bgmm_1.8.6.zip(r-4.7)bgmm_1.8.6.zip(r-4.6)bgmm_1.8.6.zip(r-4.5)
bgmm_1.8.6.tgz(r-4.6-any)bgmm_1.8.6.tgz(r-4.5-any)
bgmm_1.8.6.tar.gz(r-4.7-any)bgmm_1.8.6.tar.gz(r-4.6-any)
bgmm_1.8.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bgmm/json (API)

# Install 'bgmm' in R:
install.packages('bgmm', repos = c('https://pbiecek.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pbiecek/bgmm/issues

Datasets:
  • CellCycleBeliefs - Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases
  • CellCycleCenters - Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases
  • CellCycleClass - Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases
  • CellCycleData - Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases
  • genotypes - Fluorescence signals corresponding to a given allele for 333 SNPs
  • miR124Data - MiRNA transfection data for miR1 and miR124 target genes
  • miR1Data - MiRNA transfection data for miR1 and miR124 target genes
  • miRNABeliefs - MiRNA transfection data for miR1 and miR124 target genes
  • miRNAClass - MiRNA transfection data for miR1 and miR124 target genes
  • Ste12Beliefs - Ste12 knockout data under pheromone treatment versus wild type; Examples of Ste12 targets; Binding p-values of Ste12 to those targets.
  • Ste12Binding - Ste12 knockout data under pheromone treatment versus wild type; Examples of Ste12 targets; Binding p-values of Ste12 to those targets.
  • Ste12Data - Ste12 knockout data under pheromone treatment versus wild type; Examples of Ste12 targets; Binding p-values of Ste12 to those targets.

On CRAN:

Conda:

4.18 score 2 stars 1 packages 51 scripts 98 downloads 1 mentions 27 exports 71 dependencies

Last updated from:071228c4c9. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK141
source / vignettesOK179
linux-release-x86_64OK138
macos-release-arm64OK75
macos-oldrel-arm64OK108
windows-develOK86
windows-releaseOK92
windows-oldrelOK81
wasm-releaseOK113

Exports:beliefbeliefListchooseModelschooseOptimalcrossvalDEprobsdeterminant.numericgetDFgetGICgetModelStructureinit.model.paramsinit.model.params.knownsloglikelihood.mModelmapmModelListplot.mModelplot.mModelListplotGICpredict.mModelsemisupervisedsemisupervisedListsimulateDatasoftsoftListsupervisedunsupervisedunsupervisedList

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecombinatcowplotcpp11DerivdoBydplyrfarverforecastFormulafracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Belief-Based Gaussian Mixture Modelingbgmm-package bgmm
Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phasesCellCycle CellCycleBeliefs CellCycleCenters CellCycleClass CellCycleData
Selecting a subset of fitted modelschooseModels chooseOptimal
k-fold cross-validation for the specified modelcrossval
Signed probabilities of differential expressionDEprobs
Fluorescence signals corresponding to a given allele for 333 SNPsgenotypes
Model structuregetModelStructure
Initiation of model parametersinit.model.params init.model.params.knowns
miRNA transfection data for miR1 and miR124 target genesmiR124Data miR1Data miRNA miRNABeliefs miRNAClass
Fitting Gaussian Mixture Modelbelief mModel semisupervised soft supervised unsupervised
Fitting Gaussian mixture model or collection of modelsbeliefList mModelList semisupervisedList softList supervisedList unsupervisedList
Plotting a Graphical Visualization of a Gaussian Model or a List of Modelsplot.mModel
Plotting a graphical visualization of a model or a list of modelsplot.mModelList
Plotting GIC scoresgetDF getGIC plotGIC
Predictions for fitted Gaussian component modelpredict.mModel
Dataset generationsimulateData
Ste12 knockout data under pheromone treatment versus wild type; Examples of Ste12 targets; Binding p-values of Ste12 to those targets.Ste12 Ste12Beliefs Ste12Binding Ste12Data
Set of supplementary functions for bgmm packagedeterminant.numeric loglikelihood.mModel map