mclust

R package mclust: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation , Normal Mixture Modeling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.


References in zbMATH (referenced in 267 articles , 2 standard articles )

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  1. Rodríguez, Carlos E.; Núñez-Antonio, Gabriel; Escarela, Gabriel: A Bayesian mixture model for clustering circular data (2020)
  2. Sarkar, Shuchismita; Zhu, Xuwen; Melnykov, Volodymyr; Ingrassia, Salvatore: On parsimonious models for modeling matrix data (2020)
  3. Tonellato, Stefano F.: Bayesian nonparametric clustering as a community detection problem (2020)
  4. Yoder, Jordan; Chen, Li; Pao, Henry; Bridgeford, Eric; Levin, Keith; Fishkind, Donniell E.; Priebe, Carey; Lyzinski, Vince: Vertex nomination: the canonical sampling and the extended spectral nomination schemes (2020)
  5. Baíllo, Amparo; Cárcamo, Javier; Getman, Konstantin: New distance measures for classifying X-ray astronomy data into stellar classes (2019)
  6. Cappozzo, Andrea; Greselin, Francesca: Detecting wine adulterations employing robust mixture of factor analyzers (2019)
  7. Celeux, Gilles; Maugis-Rabusseau, Cathy; Sedki, Mohammed: Variable selection in model-based clustering and discriminant analysis with a regularization approach (2019)
  8. Chacón, José E.: Mixture model modal clustering (2019)
  9. Cipolli, William III; Hanson, Timothy: Supervised learning via smoothed Polya trees (2019)
  10. Comas-Cufí, Marc; Martín-Fernández, Josep A.; Mateu-Figueras, Glòria: Merging the components of a finite mixture using posterior probabilities (2019)
  11. 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)
  12. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv
  13. Dotto, Francesco; Farcomeni, Alessio: Robust inference for parsimonious model-based clustering (2019)
  14. Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes, Carlos Matrán, Agustín Mayo-Íscar: optimalFlow: Optimal-transport approach to flow cytometry gating and population matching (2019) arXiv
  15. Flynt, Abby; Dean, Nema: Growth mixture modeling with measurement selection (2019)
  16. Flynt, Abby; Dean, Nema; Nugent, Rebecca: sARI: a \textitsoftagreement measure for class partitions incorporating assignment probabilities (2019)
  17. Fop, Michael; Murphy, Thomas Brendan; Scrucca, Luca: Model-based clustering with sparse covariance matrices (2019)
  18. Forbes, Florence; Arnaud, Alexis; Lemasson, Benjamin; Barbier, Emmanuel: Component elimination strategies to fit mixtures of multiple scale distributions (2019)
  19. Li, Jia; Seo, Beomseok; Lin, Lin: Optimal transport, mean partition, and uncertainty assessment in cluster analysis (2019)
  20. Loperfido, Nicola: Finite mixtures, projection pursuit and tensor rank: a triangulation (2019)

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