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 191 articles , 2 standard articles )

Showing results 1 to 20 of 191.
Sorted by year (citations)

1 2 3 ... 8 9 10 next

  1. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  2. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  3. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  4. 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)
  5. Brocas, Isabelle; Carrillo, Juan D.; Sachdeva, Ashish: The path to equilibrium in sequential and simultaneous games: a mousetracking study (2018)
  6. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  7. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  8. Grayling, Michael J.; Mander, Adrian P.; Wason, James M. S.: Blinded and unblinded sample size reestimation in crossover trials balanced for period (2018)
  9. Jeffrey Andrews; Jaymeson Wickins; Nicholas Boers; Paul McNicholas: teigen: An R Package for Model-Based Clustering and Classification via the Multivariate t Distribution (2018) not zbMATH
  10. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018) not zbMATH
  11. Mai, Feng; Fry, Michael J.; Ohlmann, Jeffrey W.: Model-based capacitated clustering with posterior regularization (2018)
  12. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018) not zbMATH
  13. Polanski, Andrzej; Marczyk, Michal; Pietrowska, Monika; Widlak, Piotr; Polanska, Joanna: Initializing the EM algorithm for univariate Gaussian, multi-component, heteroscedastic mixture models by dynamic programming partitions (2018)
  14. Tang, Minh; Priebe, Carey E.: Limit theorems for eigenvectors of the normalized Laplacian for random graphs (2018)
  15. Wallace, Meredith L.; Buysse, Daniel J.; Germain, Anne; Hall, Martica H.; Iyengar, Satish: Variable selection for skewed model-based clustering: application to the identification of novel sleep phenotypes (2018)
  16. Anderson, Craig; Lee, Duncan; Dean, Nema: Spatial clustering of average risks and risk trends in Bayesian disease mapping (2017)
  17. Arias-Castro, Ery; Pu, Xiao: A simple approach to sparse clustering (2017)
  18. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  19. Coretto, Pietro; Hennig, Christian: Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering (2017)
  20. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)

1 2 3 ... 8 9 10 next