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 38 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. 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)
  3. 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)
  4. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  5. Ingrassia, Salvatore; Punzo, Antonio: Decision boundaries for mixtures of regressions (2016)
  6. Kohli, Nidhi; Harring, Jeffrey R.; Zopluoglu, Cengiz: A finite mixture of nonlinear random coefficient models for continuous repeated measures data (2016)
  7. McNicholas, Paul D.: Model-based clustering (2016)
  8. Michael, Semhar; Melnykov, Volodymyr: Finite mixture modeling of Gaussian regression time series with application to dendrochronology (2016)
  9. Miljkovic, Tatjana; Grün, Bettina: Modeling loss data using mixtures of distributions (2016)
  10. Panagiotis Papastamoulis, Magnus Rattray: BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data (2016) arXiv
  11. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)
  12. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  13. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  14. Cecile Proust-Lima, Viviane Philipps, Benoit Liquet: Estimation of extended mixed models using latent classes and latent processes: the R package lcmm (2015) arXiv
  15. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: The generalized linear mixed cluster-weighted model (2015)
  16. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: Erratum to: “The generalized linear mixed cluster-weighted model” (2015)
  17. Sharon X. Lee, Geoffrey J. McLachlan: EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions (2015) arXiv
  18. Subedi, Sanjeena; Punzo, Antonio; Ingrassia, Salvatore; McNicholas, Paul D.: Cluster-weighted $t$-factor analyzers for robust model-based clustering and dimension reduction (2015)
  19. Ringle, Christian M.; Sarstedt, Marko; Schlittgen, Rainer: Genetic algorithm segmentation in partial least squares structural equation modeling (2014)
  20. Wang, Shaoli; Yao, Weixin; Huang, Mian: A note on the identifiability of nonparametric and semiparametric mixtures of glms (2014)

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