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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 234 articles , 2 standard articles )

Showing results 1 to 20 of 234.
Sorted by year (citations)

1 2 3 ... 10 11 12 next

  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)

1 2 3 ... 10 11 12 next