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 95 articles , 2 standard articles )

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  1. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  2. Abdalla, Abdelbaset; Michael, Semhar: Finite mixture of regression models for a stratified sample (2019)
  3. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  4. Akakpo, Rexford M.; Xia, Michelle; Polansky, Alan M.: Frequentist inference in insurance ratemaking models adjusting for misrepresentation (2019)
  5. Flynt, Abby; Dean, Nema: Growth mixture modeling with measurement selection (2019)
  6. Fung, Tsz Chai; Badescu, Andrei L.; Lin, X. Sheldon: A class of mixture of experts models for general insurance: theoretical developments (2019)
  7. O’Hagan, Adrian; Murphy, Thomas Brendan; Scrucca, Luca; Gormley, Isobel Claire: Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap (2019)
  8. Wang, Wan-Lun: Mixture of multivariate (t) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values (2019)
  9. Young, Derek S.; Chen, Xi; Hewage, Dilrukshi C.; Nilo-Poyanco, Ricardo: Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering (2019)
  10. Zeller, Camila Borelli; Cabral, Celso Rômulo Barbosa; Lachos, Víctor Hugo; Benites, Luis: Finite mixture of regression models for censored data based on scale mixtures of normal distributions (2019)
  11. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  12. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  13. Hand, Paul; Joshi, Babhru: A convex program for mixed linear regression with a recovery guarantee for well-separated data (2018)
  14. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  15. Jung, Byoung Cheol; Cheon, Sooyoung; Lim, Hwa Kyung: Mixtures of regression models with incomplete and noisy data (2018)
  16. Lloyd-Jones, Luke R.; Nguyen, Hien D.; McLachlan, Geoffrey J.: A globally convergent algorithm for lasso-penalized mixture of linear regression models (2018)
  17. Mair, Patrick: Modern psychometrics with R (2018)
  18. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018) not zbMATH
  19. Nummi, Tapio; Salonen, Janne; Koskinen, Lasse; Pan, Jianxin: A semiparametric mixture regression model for longitudinal data (2018)
  20. Tang, Qingguo; Karunamuni, R. J.: Robust variable selection for finite mixture regression models (2018)

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