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 253 articles , 2 standard articles )

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  1. Agterberg, Joshua; Park, Youngser; Larson, Jonathan; White, Christopher; Priebe, Carey E.; Lyzinski, Vince: Vertex nomination, consistent estimation, and adversarial modification (2020)
  2. Bianchini, Ilaria; Guglielmi, Alessandra; Quintana, Fernando A.: Determinantal point process mixtures via spectral density approach (2020)
  3. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  4. Cappozzo, Andrea; Greselin, Francesca; Murphy, Thomas Brendan: A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise (2020)
  5. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  6. Greco, Luca; Agostinelli, Claudio: Weighted likelihood mixture modeling and model-based clustering (2020)
  7. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  8. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  9. Melnykov, Volodymyr; Michael, Semhar: Clustering large datasets by merging (K)-means solutions (2020)
  10. Murphy, Keefe; Murphy, Thomas Brendan: Gaussian parsimonious clustering models with covariates and a noise component (2020)
  11. Nguyen, Hien D.; Forbes, Florence; McLachlan, Geoffrey J.: Mini-batch learning of exponential family finite mixture models (2020)
  12. Okan Bulut, Christopher David Desjardins: profileR: An R package for profile analysis (2020) not zbMATH
  13. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
  14. Rodríguez, Carlos E.; Núñez-Antonio, Gabriel; Escarela, Gabriel: A Bayesian mixture model for clustering circular data (2020)
  15. Sarkar, Shuchismita; Zhu, Xuwen; Melnykov, Volodymyr; Ingrassia, Salvatore: On parsimonious models for modeling matrix data (2020)
  16. 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)
  17. Baíllo, Amparo; Cárcamo, Javier; Getman, Konstantin: New distance measures for classifying X-ray astronomy data into stellar classes (2019)
  18. Cappozzo, Andrea; Greselin, Francesca: Detecting wine adulterations employing robust mixture of factor analyzers (2019)
  19. Celeux, Gilles; Maugis-Rabusseau, Cathy; Sedki, Mohammed: Variable selection in model-based clustering and discriminant analysis with a regularization approach (2019)
  20. Chacón, José E.: Mixture model modal clustering (2019)

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