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 65 articles , 2 standard articles )

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  1. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018)
  2. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  3. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  4. Jung, Byoung Cheol; Cheon, Sooyoung; Lim, Hwa Kyung: Mixtures of regression models with incomplete and noisy data (2018)
  5. Tang, Qingguo; Karunamuni, R. J.: Robust variable selection for finite mixture regression models (2018)
  6. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  7. Gao, Xin; Cao, Yurong R.; Ogden, Nicholas; Aubin, Louise; Zhu, Huaiping P.: Mixture Markov regression model with application to mosquito surveillance data analysis (2017)
  8. 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)
  9. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017)
  10. 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)
  11. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  12. Fegatelli, Danilo Alunni; Tardella, Luca: Flexible behavioral capture-recapture modeling (2016)
  13. Ingrassia, Salvatore; Punzo, Antonio: Decision boundaries for mixtures of regressions (2016)
  14. Kohli, Nidhi; Harring, Jeffrey R.; Zopluoglu, Cengiz: A finite mixture of nonlinear random coefficient models for continuous repeated measures data (2016)
  15. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016)
  16. McNicholas, Paul D.: Model-based clustering (2016)
  17. Michael, Semhar; Melnykov, Volodymyr: Finite mixture modeling of Gaussian regression time series with application to dendrochronology (2016)
  18. Miljkovic, Tatjana; Grün, Bettina: Modeling loss data using mixtures of distributions (2016)
  19. Panagiotis Papastamoulis, Magnus Rattray: BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data (2016) arXiv
  20. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)

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