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

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

References in zbMATH (referenced in 26 articles , 1 standard article )

Showing results 1 to 20 of 26.
Sorted by year (citations)

1 2 next

  1. Etienne Côme, Nicolas Jouvin : greed: An R Package for Model-Based Clustering by Greedy Maximization of the Integrated Classification Likelihood (2022) arXiv
  2. Khouja, Rima; Mattei, Pierre-Alexandre; Mourrain, Bernard: Tensor decomposition for learning Gaussian mixtures from moments (2022)
  3. Biernacki, Christophe; Marbac, Matthieu; Vandewalle, Vincent: Gaussian-based visualization of Gaussian and non-Gaussian-based clustering (2021)
  4. Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, Paul D. McNicholas: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package (2021) not zbMATH
  5. Radojičić, Una; Nordhausen, Klaus; Virta, Joni: Large-sample properties of unsupervised estimation of the linear discriminant using projection pursuit (2021)
  6. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  7. Murphy, Keefe; Murphy, Thomas Brendan: Gaussian parsimonious clustering models with covariates and a noise component (2020)
  8. Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
  9. Celeux, Gilles; Maugis-Rabusseau, Cathy; Sedki, Mohammed: Variable selection in model-based clustering and discriminant analysis with a regularization approach (2019)
  10. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  11. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  12. 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
  13. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  14. Śmieja, Marek; Geiger, Bernhard C.: Semi-supervised cross-entropy clustering with information bottleneck constraint (2017)
  15. Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv
  16. Marbac, Matthieu; Biernacki, Christophe; Vandewalle, Vincent: Latent class model with conditional dependency per modes to cluster categorical data (2016)
  17. Michael, Semhar; Melnykov, Volodymyr: An effective strategy for initializing the EM algorithm in finite mixture models (2016)
  18. Punzo, Antonio; McNicholas, Paul D.: Parsimonious mixtures of multivariate contaminated normal distributions (2016)
  19. 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)
  20. Dang, Utkarsh J.; Browne, Ryan P.; McNicholas, Paul D.: Mixtures of multivariate power exponential distributions (2015)

1 2 next