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 274 articles , 2 standard articles )

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  1. Batool, Fatima; Hennig, Christian: Clustering with the average silhouette width (2021)
  2. Biernacki, Christophe; Marbac, Matthieu; Vandewalle, Vincent: Gaussian-based visualization of Gaussian and non-Gaussian-based clustering (2021)
  3. Braverman, Amy; Hobbs, Jonathan; Teixeira, Joaquim; Gunson, Michael: Post hoc uncertainty quantification for remote sensing observing systems (2021)
  4. Cappozzo, Andrea; Greselin, Francesca; Murphy, Thomas Brendan: Robust variable selection for model-based learning in presence of adulteration (2021)
  5. Carel, Léna; Alquier, Pierre: Simultaneous dimension reduction and clustering via the NMF-EM algorithm (2021)
  6. Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
  7. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  8. Subedi, Sanjeena; McNicholas, Paul D.: A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting (2021)
  9. Thrun, Michael C.; Ultsch, Alfred: Swarm intelligence for self-organized clustering (2021)
  10. Tsai, Cary Chi-Liang; Cheng, Echo Sihan: Incorporating statistical clustering methods into mortality models to improve forecasting performances (2021)
  11. Agterberg, Joshua; Park, Youngser; Larson, Jonathan; White, Christopher; Priebe, Carey E.; Lyzinski, Vince: Vertex nomination, consistent estimation, and adversarial modification (2020)
  12. Alqahtani, Nada A.; Kalantan, Zakiah I.: Gaussian mixture models based on principal components and applications (2020)
  13. Bianchini, Ilaria; Guglielmi, Alessandra; Quintana, Fernando A.: Determinantal point process mixtures via spectral density approach (2020)
  14. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  15. 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)
  16. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  17. Greco, Luca; Agostinelli, Claudio: Weighted likelihood mixture modeling and model-based clustering (2020)
  18. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  19. Heckens, Anton J.; Krause, Sebastian M.; Guhr, Thomas: Uncovering the dynamics of correlation structures relative to the collective market motion (2020)
  20. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)

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