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 146 articles , 1 standard article )

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  1. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  2. Anderson, Craig; Lee, Duncan; Dean, Nema: Spatial clustering of average risks and risk trends in Bayesian disease mapping (2017)
  3. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  4. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  5. Djordjilović, Vera; Chiogna, Monica; Vomlel, Jiří: An empirical comparison of popular structure learning algorithms with a view to gene network inference (2017)
  6. Lumbreras, Alberto; Velcin, Julien; Guégan, Marie; Jouve, Bertrand: Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models (2017)
  7. Mazo, Gildas: A semiparametric and location-shift copula-based mixture model (2017)
  8. Punzo, Antonio; McNicholas, Paul.D.: Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model (2017)
  9. Saptarshi Chakraborty, Samuel W.K. Wong: BAMBI: An R package for Fitting Bivariate Angular Mixture Models (2017) arXiv
  10. Shaikh, Mateen R.; Beyene, Joseph: Statistical models and computational algorithms for discovering relationships in microbiome data (2017)
  11. Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv
  12. 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)
  13. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  14. Foss, Alex; Markatou, Marianthi; Ray, Bonnie; Heching, Aliza: A semiparametric method for clustering mixed data (2016)
  15. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Seeking relevant information from a statistical model (2016)
  16. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Feature selection for functional data (2016)
  17. Galimberti, Giuliano; Scardovi, Elena; Soffritti, Gabriele: Using mixtures in seemingly unrelated linear regression models with non-normal errors (2016)
  18. Gao, Chen; Zhu, Yunzhang; Shen, Xiaotong; Pan, Wei: Estimation of multiple networks in Gaussian mixture models (2016)
  19. Hasnat, Md.Abul; Alata, Olivier; Trémeau, Alain: Model-based hierarchical clustering with Bregman divergences and fishers mixture model: application to depth image analysis (2016)
  20. Kosmidis, Ioannis; Karlis, Dimitris: Model-based clustering using copulas with applications (2016)

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