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)

Keywords for this software

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References in zbMATH (referenced in 122 articles , 2 standard articles )

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  1. Diani, Cecilia; Galimberti, Giuliano; Soffritti, Gabriele: Multivariate cluster-weighted models based on seemingly unrelated linear regression (2022)
  2. Greco, Luca: Robust fitting of mixtures of GLMs by weighted likelihood (2022)
  3. Youjiao Yu: mixR: An R package for Finite Mixture Modeling for Both Raw and Binned Data (2022) not zbMATH
  4. Auder, Benjamin; Gassiat, Elisabeth; Loum, Mor Absa: Least squares moment identification of binary regression mixture models (2021)
  5. Bagirov, Adil M.; Taheri, Sona; Cimen, Emre: Incremental DC optimization algorithm for large-scale clusterwise linear regression (2021)
  6. Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, Paul D. McNicholas: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package (2021) not zbMATH
  7. Delong, Łukasz; Lindholm, Mathias; Wüthrich, Mario V.: Gamma mixture density networks and their application to modelling insurance claim amounts (2021)
  8. Galimberti, Giuliano; Nuzzi, Lorenzo; Soffritti, Gabriele: Covariance matrix estimation of the maximum likelihood estimator in multivariate clusterwise linear regression (2021)
  9. Morvan, Marie; Devijver, Emilie; Giacofci, Madison; Monbet, Valérie: Prediction of the Nash through penalized mixture of logistic regression models (2021)
  10. Tomarchio, Salvatore D.; McNicholas, Paul D.; Punzo, Antonio: Matrix normal cluster-weighted models (2021)
  11. Galimberti, Giuliano; Soffritti, Gabriele: Seemingly unrelated clusterwise linear regression (2020)
  12. Gardner, John: Identification and estimation of average causal effects when treatment status is ignorable within unobserved strata (2020)
  13. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  14. Ingrassia, Salvatore; Punzo, Antonio: Cluster validation for mixtures of regressions via the total sum of squares decomposition (2020)
  15. Joki, Kaisa; Bagirov, Adil M.; Karmitsa, Napsu; Mäkelä, Marko M.; Taheri, Sona: Clusterwise support vector linear regression (2020)
  16. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  17. Murphy, Keefe; Murphy, Thomas Brendan: Gaussian parsimonious clustering models with covariates and a noise component (2020)
  18. Shen, Jieli; Liu, Regina Y.; Xie, Min-ge: (i)Fusion: individualized fusion learning (2020)
  19. 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)
  20. Abdalla, Abdelbaset; Michael, Semhar: Finite mixture of regression models for a stratified sample (2019)

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