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 234 articles , 2 standard articles )

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  1. Bianchini, Ilaria; Guglielmi, Alessandra; Quintana, Fernando A.: Determinantal point process mixtures via spectral density approach (2020)
  2. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  3. Greco, Luca; Agostinelli, Claudio: Weighted likelihood mixture modeling and model-based clustering (2020)
  4. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  5. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  6. Okan Bulut, Christopher David Desjardins: profileR: An R package for profile analysis (2020) not zbMATH
  7. Rodríguez, Carlos E.; Núñez-Antonio, Gabriel; Escarela, Gabriel: A Bayesian mixture model for clustering circular data (2020)
  8. Sarkar, Shuchismita; Zhu, Xuwen; Melnykov, Volodymyr; Ingrassia, Salvatore: On parsimonious models for modeling matrix data (2020)
  9. Yoder, Jordan; Chen, Li; Pao, Henry; Bridgeford, Eric; Levin, Keith; Fishkind, Donniell E.; Priebe, Carey; Lyzinski, Vince: Vertex nomination: the canonical sampling and the extended spectral nomination schemes (2020)
  10. Chacón, José E.: Mixture model modal clustering (2019)
  11. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv
  12. Dotto, Francesco; Farcomeni, Alessio: Robust inference for parsimonious model-based clustering (2019)
  13. Flynt, Abby; Dean, Nema: Growth mixture modeling with measurement selection (2019)
  14. Flynt, Abby; Dean, Nema; Nugent, Rebecca: sARI: a \textitsoftagreement measure for class partitions incorporating assignment probabilities (2019)
  15. Loperfido, Nicola: Finite mixtures, projection pursuit and tensor rank: a triangulation (2019)
  16. 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)
  17. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  18. 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)
  19. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  20. Park, Ju-Hyun; Kyung, Minjung: Bayesian curve fitting and clustering with Dirichlet process mixture models for microarray data (2019)

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