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 79 articles , 2 standard articles )

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  1. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  2. Akakpo, Rexford M.; Xia, Michelle; Polansky, Alan M.: Frequentist inference in insurance ratemaking models adjusting for misrepresentation (2019)
  3. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  4. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  5. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  6. Jung, Byoung Cheol; Cheon, Sooyoung; Lim, Hwa Kyung: Mixtures of regression models with incomplete and noisy data (2018)
  7. Lloyd-Jones, Luke R.; Nguyen, Hien D.; McLachlan, Geoffrey J.: A globally convergent algorithm for lasso-penalized mixture of linear regression models (2018)
  8. Mair, Patrick: Modern psychometrics with R (2018)
  9. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018) not zbMATH
  10. Tang, Qingguo; Karunamuni, R. J.: Robust variable selection for finite mixture regression models (2018)
  11. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  12. Gao, Xin; Cao, Yurong R.; Ogden, Nicholas; Aubin, Louise; Zhu, Huaiping P.: Mixture Markov regression model with application to mosquito surveillance data analysis (2017)
  13. 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)
  14. Maruotti, Antonello; Punzo, Antonio: Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers (2017)
  15. Mauricio Sarrias and Ricardo Daziano: Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package (2017) not zbMATH
  16. 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)
  17. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  18. Ciarleglio, Adam; Todd Ogden, R.: Wavelet-based scalar-on-function finite mixture regression models (2016)
  19. Fegatelli, Danilo Alunni; Tardella, Luca: Flexible behavioral capture-recapture modeling (2016)
  20. Ingrassia, Salvatore; Punzo, Antonio: Decision boundaries for mixtures of regressions (2016)

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