flexmix

R package flexmix: Flexible Mixture Modeling , FlexMix implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 109 articles , 2 standard articles )

Showing results 41 to 60 of 109.
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  1. Kohli, Nidhi; Harring, Jeffrey R.; Zopluoglu, Cengiz: A finite mixture of nonlinear random coefficient models for continuous repeated measures data (2016)
  2. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016) not zbMATH
  3. McNicholas, Paul D.: Model-based clustering (2016)
  4. Michael, Semhar; Melnykov, Volodymyr: Finite mixture modeling of Gaussian regression time series with application to dendrochronology (2016)
  5. Miljkovic, Tatjana; Grün, Bettina: Modeling loss data using mixtures of distributions (2016)
  6. Mufudza, Chipo; Erol, Hamza: Poisson mixture regression models for heart disease prediction (2016)
  7. Nguyen, Hien D.; Lloyd-Jones, Luke R.; McLachlan, Geoffrey J.: A universal approximation theorem for mixture-of-experts models (2016)
  8. Nguyen, Hien D.; McLachlan, Geoffrey J.: Laplace mixture of linear experts (2016)
  9. Nguyen, Hien D.; McLachlan, Geoffrey J.: Linear mixed models with marginally symmetric nonparametric random effects (2016)
  10. Panagiotis Papastamoulis, Magnus Rattray: BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data (2016) arXiv
  11. Papastamoulis, Panagiotis; Martin-Magniette, Marie-Laure; Maugis-Rabusseau, Cathy: On the estimation of mixtures of Poisson regression models with large number of components (2016)
  12. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)
  13. Tian, Fang-Bao (ed.); Sui, Yi (ed.); Zhu, Luoding (ed.); Shu, Chang (ed.); Sung, Hyung J. (ed.): Computational methods and models in circulatory and reproductive systems (2016)
  14. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  15. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  16. Anderlucci, Laura; Viroli, Cinzia: Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data (2015)
  17. Cecile Proust-Lima, Viviane Philipps, Benoit Liquet: Estimation of extended mixed models using latent classes and latent processes: the R package lcmm (2015) arXiv
  18. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: The generalized linear mixed cluster-weighted model (2015)
  19. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: Erratum to: “The generalized linear mixed cluster-weighted model” (2015)
  20. 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