VarSelLCM: Variable Selection for Model-Based Clustering using the Integrated Complete-Data Likelihood of a Latent Class Model. Uses a finite mixture model for performing the cluster analysis with variable selection of continuous data by assuming independence between classes. The package deals dataset with missing values by assuming that values are missing at random. The one-dimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by the Maximum Integrated Complete-Data Likelihood criterion. The maximum likelihood inference is done by an EM algorithm for the selected model. This package also performs the imputation of missing values.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Marbac, Matthieu; Sedki, Mohammed; Patin, Tienne: Variable selection for mixed data clustering: application in human population genomics (2020)
- Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
- Crook, Oliver M.; Gatto, Laurent; Kirk, Paul D. W.: Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics (2019)
- Marbac, Matthieu; Vandewalle, Vincent: A tractable multi-partitions clustering (2019)
- Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
- Marbac, Matthieu; Sedki, Mohammed: Variable selection for model-based clustering using the integrated complete-data likelihood (2017)