mclust

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 124 articles , 1 standard article )

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  1. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  2. Athreya, A.; Priebe, C.E.; Tang, M.; Lyzinski, V.; Marchette, D.J.; Sussman, D.L.: A limit theorem for scaled eigenvectors of random dot product graphs (2016)
  3. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  4. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Seeking relevant information from a statistical model (2016)
  5. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Feature selection for functional data (2016)
  6. Galimberti, Giuliano; Scardovi, Elena; Soffritti, Gabriele: Using mixtures in seemingly unrelated linear regression models with non-normal errors (2016)
  7. Hasnat, Md.Abul; Alata, Olivier; Trémeau, Alain: Model-based hierarchical clustering with Bregman divergences and fishers mixture model: application to depth image analysis (2016)
  8. Kosmidis, Ioannis; Karlis, Dimitris: Model-based clustering using copulas with applications (2016)
  9. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  10. Li, Ying; He, Ye; Zhang, Yu: Analyzing gene expression time-courses based on multi-resolution shape mixture model (2016)
  11. Proïa, Frédéric; Pernet, Alix; Thouroude, Tatiana; Michel, Gilles; Clotault, Jérémy: On the characterization of flowering curves using Gaussian mixture models (2016)
  12. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)
  13. Punzo, Antonio; McNicholas, Paul D.: Parsimonious mixtures of multivariate contaminated normal distributions (2016)
  14. Bertoletti, Marco; Friel, Nial; Rastelli, Riccardo: Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion (2015)
  15. Bouveyron, Charles; C^ome, Etienne; Jacques, Julien: The discriminative functional mixture model for a comparative analysis of bike sharing systems (2015)
  16. Evans, Katie; Love, Tanzy; Thurston, Sally W.: Outlier identification in model-based cluster analysis (2015)
  17. Hennig, Christian; Lin, Chien-Ju: Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters (2015)
  18. Polonio, Luca; Di Guida, Sibilla; Coricelli, Giorgio: Strategic sophistication and attention in games: an eye-tracking study (2015)
  19. Strobl, Carolin; Kopf, Julia; Zeileis, Achim: Rasch trees: a new method for detecting differential item functioning in the Rasch model (2015)
  20. Vanbinst, Kiran; Ceulemans, Eva; Ghesquière, Pol; De Smedt, Bert: Profiles of children’s arithmetic fact development: A model-based clustering approach (2015)

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