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 47 articles , 1 standard article )

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  1. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  2. Gao, Xin; Cao, Yurong R.; Ogden, Nicholas; Aubin, Louise; Zhu, Huaiping P.: Mixture Markov regression model with application to mosquito surveillance data analysis (2017)
  3. García-Escudero, L.A.; Gordaliza, A.; Greselin, F.; Ingrassia, S.; Mayo-Iscar, A.: Robust estimation of mixtures of regressions with random covariates, via trimming and constraints (2017)
  4. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017)
  5. Wilderjans, Tom Frans; Vande Gaer, Eva; Kiers, Henk A.L.; Van Mechelen, Iven; Ceulemans, Eva: Principal covariates clusterwise regression (PCCR): accounting for multicollinearity and population heterogeneity in hierarchically organized data (2017)
  6. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  7. Fegatelli, Danilo Alunni; Tardella, Luca: Flexible behavioral capture-recapture modeling (2016)
  8. Ingrassia, Salvatore; Punzo, Antonio: Decision boundaries for mixtures of regressions (2016)
  9. Kohli, Nidhi; Harring, Jeffrey R.; Zopluoglu, Cengiz: A finite mixture of nonlinear random coefficient models for continuous repeated measures data (2016)
  10. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016)
  11. McNicholas, Paul D.: Model-based clustering (2016)
  12. Michael, Semhar; Melnykov, Volodymyr: Finite mixture modeling of Gaussian regression time series with application to dendrochronology (2016)
  13. Miljkovic, Tatjana; Grün, Bettina: Modeling loss data using mixtures of distributions (2016)
  14. Panagiotis Papastamoulis, Magnus Rattray: BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data (2016) arXiv
  15. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)
  16. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  17. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  18. Anderlucci, Laura; Viroli, Cinzia: Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data (2015)
  19. Cecile Proust-Lima, Viviane Philipps, Benoit Liquet: Estimation of extended mixed models using latent classes and latent processes: the R package lcmm (2015) arXiv
  20. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: Erratum to: “The generalized linear mixed cluster-weighted model” (2015)

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