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 267 articles , 2 standard articles )

Showing results 41 to 60 of 267.
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  1. Lu, Zhao-Hua; Chow, Sy-Miin; Ram, Nilam; Cole, Pamela M.: Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data (2019)
  2. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  3. Morris, Katherine; Punzo, Antonio; McNicholas, Paul D.; Browne, Ryan P.: Asymmetric clusters and outliers: mixtures of multivariate contaminated shifted asymmetric Laplace distributions (2019)
  4. O’Hagan, Adrian; Murphy, Thomas Brendan; Scrucca, Luca; Gormley, Isobel Claire: Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap (2019)
  5. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  6. Park, Ju-Hyun; Kyung, Minjung: Bayesian curve fitting and clustering with Dirichlet process mixture models for microarray data (2019)
  7. Polonio, Luca; Coricelli, Giorgio: Testing the level of consistency between choices and beliefs in games using eye-tracking (2019)
  8. Rathke, Fabian; Schnörr, Christoph: Fast multivariate log-concave density estimation (2019)
  9. Shi, Xianfeng; Liu, Xuejun; Zhang, Li: PUseqClust: a clustering analysis method for RNA-seq data (2019)
  10. Viroli, Cinzia; McLachlan, Geoffrey J.: Deep Gaussian mixture models (2019)
  11. Young, Derek S.; Chen, Xi; Hewage, Dilrukshi C.; Nilo-Poyanco, Ricardo: Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering (2019)
  12. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  13. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  14. Athreya, Avanti; Fishkind, Donniell E.; Tang, Minh; Priebe, Carey E.; Park, Youngser; Vogelstein, Joshua T.; Levin, Keith; Lyzinski, Vince; Qin, Yichen; Sussman, Daniel L.: Statistical inference on random dot product graphs: a survey (2018)
  15. Brocas, Isabelle; Carrillo, Juan D.; Sachdeva, Ashish: The path to equilibrium in sequential and simultaneous games: a mousetracking study (2018)
  16. Brodinová, Šárka; Zaharieva, Maia; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian: Clustering of imbalanced high-dimensional media data (2018)
  17. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  18. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  19. Gallegos, María Teresa; Ritter, Gunter: Probabilistic clustering via Pareto solutions and significance tests (2018)
  20. García-Escudero, Luis Angel; Gordaliza, Alfonso; Greselin, Francesca; Ingrassia, Salvatore; Mayo-Iscar, Agustín: Eigenvalues and constraints in mixture modeling: geometric and computational issues (2018)

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