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 180 articles , 2 standard articles )

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  1. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018)
  2. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018)
  3. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  4. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  5. Jeffrey Andrews; Jaymeson Wickins; Nicholas Boers; Paul McNicholas: teigen: An R Package for Model-Based Clustering and Classification via the Multivariate t Distribution (2018)
  6. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018)
  7. Mai, Feng; Fry, Michael J.; Ohlmann, Jeffrey W.: Model-based capacitated clustering with posterior regularization (2018)
  8. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018)
  9. Polanski, Andrzej; Marczyk, Michal; Pietrowska, Monika; Widlak, Piotr; Polanska, Joanna: Initializing the EM algorithm for univariate Gaussian, multi-component, heteroscedastic mixture models by dynamic programming partitions (2018)
  10. Tang, Minh; Priebe, Carey E.: Limit theorems for eigenvectors of the normalized Laplacian for random graphs (2018)
  11. Wallace, Meredith L.; Buysse, Daniel J.; Germain, Anne; Hall, Martica H.; Iyengar, Satish: Variable selection for skewed model-based clustering: application to the identification of novel sleep phenotypes (2018)
  12. Anderson, Craig; Lee, Duncan; Dean, Nema: Spatial clustering of average risks and risk trends in Bayesian disease mapping (2017)
  13. Arias-Castro, Ery; Pu, Xiao: A simple approach to sparse clustering (2017)
  14. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  15. Coretto, Pietro; Hennig, Christian: Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering (2017)
  16. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  17. Djordjilović, Vera; Chiogna, Monica; Vomlel, Jiří: An empirical comparison of popular structure learning algorithms with a view to gene network inference (2017)
  18. Lin, Lin; Li, Jia: Clustering with hidden Markov model on variable blocks (2017)
  19. Lumbreras, Alberto; Velcin, Julien; Guégan, Marie; Jouve, Bertrand: Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models (2017)
  20. Mazo, Gildas: A semiparametric and location-shift copula-based mixture model (2017)

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