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 97 articles , 2 standard articles )

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  1. Joki, Kaisa; Bagirov, Adil M.; Karmitsa, Napsu; Mäkelä, Marko M.; Taheri, Sona: Clusterwise support vector linear regression (2020)
  2. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  3. Yang, Yu-Chen; Lin, Tsung-I; Castro, Luis M.; Wang, Wan-Lun: Extending finite mixtures of (t) linear mixed-effects models with concomitant covariates (2020)
  4. Abdalla, Abdelbaset; Michael, Semhar: Finite mixture of regression models for a stratified sample (2019)
  5. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  6. Akakpo, Rexford M.; Xia, Michelle; Polansky, Alan M.: Frequentist inference in insurance ratemaking models adjusting for misrepresentation (2019)
  7. Flynt, Abby; Dean, Nema: Growth mixture modeling with measurement selection (2019)
  8. Fung, Tsz Chai; Badescu, Andrei L.; Lin, X. Sheldon: A class of mixture of experts models for general insurance: theoretical developments (2019)
  9. 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)
  10. Wang, Wan-Lun: Mixture of multivariate (t) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values (2019)
  11. Young, Derek S.; Chen, Xi; Hewage, Dilrukshi C.; Nilo-Poyanco, Ricardo: Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering (2019)
  12. 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)
  13. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  14. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  15. Hand, Paul; Joshi, Babhru: A convex program for mixed linear regression with a recovery guarantee for well-separated data (2018)
  16. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  17. Jung, Byoung Cheol; Cheon, Sooyoung; Lim, Hwa Kyung: Mixtures of regression models with incomplete and noisy data (2018)
  18. Lloyd-Jones, Luke R.; Nguyen, Hien D.; McLachlan, Geoffrey J.: A globally convergent algorithm for lasso-penalized mixture of linear regression models (2018)
  19. Mair, Patrick: Modern psychometrics with R (2018)
  20. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018) not zbMATH

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