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

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References in zbMATH (referenced in 194 articles , 2 standard articles )

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  1. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv
  2. O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
  3. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  4. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  5. 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)
  6. Brocas, Isabelle; Carrillo, Juan D.; Sachdeva, Ashish: The path to equilibrium in sequential and simultaneous games: a mousetracking study (2018)
  7. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  8. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  9. Grayling, Michael J.; Mander, Adrian P.; Wason, James M. S.: Blinded and unblinded sample size reestimation in crossover trials balanced for period (2018)
  10. 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
  11. Joshua M. Rosenberg; Patrick N. Beymer; Daniel J. Anderson; Jennifer A. Schmidt: tidyLPA: An R Package to Easily Carry Out LatentProfile Analysis (LPA) Using Open-Source orCommercial Software (2018) not zbMATH
  12. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018) not zbMATH
  13. Mai, Feng; Fry, Michael J.; Ohlmann, Jeffrey W.: Model-based capacitated clustering with posterior regularization (2018)
  14. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018) not zbMATH
  15. 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)
  16. Tang, Minh; Priebe, Carey E.: Limit theorems for eigenvectors of the normalized Laplacian for random graphs (2018)
  17. 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)
  18. Anderson, Craig; Lee, Duncan; Dean, Nema: Spatial clustering of average risks and risk trends in Bayesian disease mapping (2017)
  19. Arias-Castro, Ery; Pu, Xiao: A simple approach to sparse clustering (2017)
  20. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)

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