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 110 articles , 2 standard articles )

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  1. Matthew Shotwell: profdpm: An R Package for MAP Estimation in a Class of Conjugate Product Partition Models (2013) not zbMATH
  2. Subedi, Sanjeena; Punzo, Antonio; Ingrassia, Salvatore; McNicholas, Paul D.: Clustering and classification via cluster-weighted factor analyzers (2013)
  3. Bashir, Shaheena; Carter, E. M.: Robust mixture of linear regression models (2012)
  4. Bermúdez, Lluís; Karlis, Dimitris: A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking (2012)
  5. Bettina Grün; Ioannis Kosmidis; Achim Zeileis: Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned (2012) not zbMATH
  6. Hannah Frick; Carolin Strobl; Friedrich Leisch; Achim Zeileis: Flexible Rasch Mixture Models with Package psychomix (2012) not zbMATH
  7. Heinrich Fritz; Luis García-Escudero; Agustín Mayo-Iscar: tclust: An R Package for a Trimming Approach to Cluster Analysis (2012) not zbMATH
  8. Ingrassia, Salvatore; Minotti, Simona C.; Vittadini, Giorgio: Local statistical modeling via a cluster-weighted approach with elliptical distributions (2012)
  9. Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)
  10. Picard, Nicolas; Bar-Hen, Avner: A criterion based on the Mahalanobis distance for cluster analysis with subsampling (2012)
  11. Chen, Cathy W. S.; Chan, Jennifer S. K.; So, Mike K. P.; Lee, Kevin K. M.: Classification in segmented regression problems (2011)
  12. Fisher, N. I.; Lee, A. J.: Getting the `correct’ answer from survey responses: a simple application of the EM algorithm (2011)
  13. Garrido Lopera, Liliana; Cepeda-Cuervo, Edilberto; Achcar, Jorge Alberto: Heteroscedastic normal-exponential mixture models: Bayesian and classical approaches (2011)
  14. Schlittgen, Rainer: A weighted least-squares approach to clusterwise regression (2011)
  15. Soffritti, Gabriele; Galimberti, Giuliano: Multivariate linear regression with non-normal errors: a solution based on mixture models (2011)
  16. Ingmar Visser; Maarten Speekenbrink: depmixS4: An R Package for Hidden Markov Models (2010) not zbMATH
  17. Städler, Nicolas; Bühlmann, Peter; Van de Geer, Sara: (\ell_1)-penalization for mixture regression models (2010)
  18. Tarpey, Thaddeus; Petkova, Eva: Latent regression analysis (2010)
  19. Cappé, Olivier; Moulines, Eric: On-line expectation-maximization algorithm for latent data models (2009)
  20. Tatiana Benaglia; Didier Chauveau; David Hunter; Derek Young: mixtools: An R Package for Analyzing Mixture Models (2009) not zbMATH