Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling
Belief-Based Gaussian Mixture Modeling
Selecting a subset of fitted models
k-fold cross-validation for the specified model
Signed probabilities of differential expression
Model structure
Initiation of model parameters
Fitting Gaussian Mixture Model
Fitting Gaussian mixture model or collection of models
Plotting a Graphical Visualization of a Gaussian Model or a List of Mo...
Plotting a graphical visualization of a model or a list of models
Plotting GIC scores
Predictions for fitted Gaussian component model
Dataset generation
Set of supplementary functions for bgmm package
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>.