R package mixture: Mixture Models for Clustering and Classification. An implementation of all 14 Gaussian parsimonious clustering models (GPCMs) for model-based clustering and model-based classification.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
- Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
- Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv
- McNicholas, Paul D.: Model-based clustering (2016)
- Punzo, Antonio; McNicholas, Paul D.: Parsimonious mixtures of multivariate contaminated normal distributions (2016)
- McNicholas, Paul D.; Browne, Ryan P.; Murray, Paula M.: Discussion of `Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan (2013)
- Morris, Katherine; McNicholas, Paul D.: Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions (2013)