Rmixmod

R package Rmixmod: An Interface for MIXMOD. A collection of functions designed to run supervised and unsupervised classification with MIXture MODelling.

Keywords for this software

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References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  2. Murphy, Keefe; Murphy, Thomas Brendan: Gaussian parsimonious clustering models with covariates and a noise component (2020)
  3. Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
  4. Celeux, Gilles; Maugis-Rabusseau, Cathy; Sedki, Mohammed: Variable selection in model-based clustering and discriminant analysis with a regularization approach (2019)
  5. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  6. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  7. 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
  8. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  9. Śmieja, Marek; Geiger, Bernhard C.: Semi-supervised cross-entropy clustering with information bottleneck constraint (2017)
  10. Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv
  11. Marbac, Matthieu; Biernacki, Christophe; Vandewalle, Vincent: Latent class model with conditional dependency per modes to cluster categorical data (2016)
  12. Michael, Semhar; Melnykov, Volodymyr: An effective strategy for initializing the EM algorithm in finite mixture models (2016)
  13. Punzo, Antonio; McNicholas, Paul D.: Parsimonious mixtures of multivariate contaminated normal distributions (2016)
  14. Baudry, Jean-Patrick; Cardoso, Margarida; Celeux, Gilles; Amorim, Maria José; Ferreira, Ana Sousa: Enhancing the selection of a model-based clustering with external categorical variables (2015)
  15. Dang, Utkarsh J.; Browne, Ryan P.; McNicholas, Paul D.: Mixtures of multivariate power exponential distributions (2015)
  16. Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence; Rau, Andrea: A model selection criterion for model-based clustering of annotated gene expression data (2015)
  17. Marbac, Matthieu; Biernacki, Christophe; Vandewalle, Vincent: Model-based clustering for conditionally correlated categorical data (2015)
  18. Rémi Lebret; Serge Iovleff; Florent Langrognet; Christophe Biernacki; Gilles Celeux; Gérard Govaert: Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library (2015) not zbMATH
  19. Arnošt Komárek; Lenka Komárková: Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data (2014) not zbMATH
  20. Browne, Ryan P.; McNicholas, Paul D.: Estimating common principal components in high dimensions (2014)

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