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 25 articles , 1 standard article )

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  1. 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)
  2. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  3. Ingrassia, Salvatore; Punzo, Antonio: Decision boundaries for mixtures of regressions (2016)
  4. Kohli, Nidhi; Harring, Jeffrey R.; Zopluoglu, Cengiz: A finite mixture of nonlinear random coefficient models for continuous repeated measures data (2016)
  5. Miljkovic, Tatjana; Grün, Bettina: Modeling loss data using mixtures of distributions (2016)
  6. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)
  7. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  8. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)
  9. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: Erratum to: “The generalized linear mixed cluster-weighted model” (2015)
  10. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: The generalized linear mixed cluster-weighted model (2015)
  11. Subedi, Sanjeena; Punzo, Antonio; Ingrassia, Salvatore; McNicholas, Paul D.: Cluster-weighted $t$-factor analyzers for robust model-based clustering and dimension reduction (2015)
  12. Wang, Shaoli; Yao, Weixin; Huang, Mian: A note on the identifiability of nonparametric and semiparametric mixtures of glms (2014)
  13. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  14. Bordes, L.; Kojadinovic, I.; Vandekerkhove, P.: Semiparametric estimation of a two-component mixture of linear regressions in which one component is known (2013)
  15. Komárek, Arnošt; Komárková, Lenka: Clustering for multivariate continuous and discrete longitudinal data (2013)
  16. Subedi, Sanjeena; Punzo, Antonio; Ingrassia, Salvatore; McNicholas, Paul D.: Clustering and classification via cluster-weighted factor analyzers (2013)
  17. Bermúdez, Lluís; Karlis, Dimitris: A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking (2012)
  18. Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)
  19. Garrido Lopera, Liliana; Cepeda-Cuervo, Edilberto; Achcar, Jorge Alberto: Heteroscedastic normal-exponential mixture models: Bayesian and classical approaches (2011)
  20. Soffritti, Gabriele; Galimberti, Giuliano: Multivariate linear regression with non-normal errors: a solution based on mixture models (2011)

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