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 111 articles , 2 standard articles )

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  1. Auder, Benjamin; Gassiat, Elisabeth; Loum, Mor Absa: Least squares moment identification of binary regression mixture models (2021)
  2. Bagirov, Adil M.; Taheri, Sona; Cimen, Emre: Incremental DC optimization algorithm for large-scale clusterwise linear regression (2021)
  3. 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
  4. Galimberti, Giuliano; Soffritti, Gabriele: Seemingly unrelated clusterwise linear regression (2020)
  5. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  6. Ingrassia, Salvatore; Punzo, Antonio: Cluster validation for mixtures of regressions via the total sum of squares decomposition (2020)
  7. Joki, Kaisa; Bagirov, Adil M.; Karmitsa, Napsu; Mäkelä, Marko M.; Taheri, Sona: Clusterwise support vector linear regression (2020)
  8. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  9. Murphy, Keefe; Murphy, Thomas Brendan: Gaussian parsimonious clustering models with covariates and a noise component (2020)
  10. Shen, Jieli; Liu, Regina Y.; Xie, Min-ge: (i)Fusion: individualized fusion learning (2020)
  11. 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)
  12. Abdalla, Abdelbaset; Michael, Semhar: Finite mixture of regression models for a stratified sample (2019)
  13. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  14. Akakpo, Rexford M.; Xia, Michelle; Polansky, Alan M.: Frequentist inference in insurance ratemaking models adjusting for misrepresentation (2019)
  15. Flynt, Abby; Dean, Nema: Growth mixture modeling with measurement selection (2019)
  16. Fung, Tsz Chai; Badescu, Andrei L.; Lin, X. Sheldon: A class of mixture of experts models for general insurance: theoretical developments (2019)
  17. 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)
  18. Patterson, Toby: Book review of: W. Zucchini et al., Hidden Markov models for time series: an introduction using R. 2nd ed. (2019)
  19. Wang, Wan-Lun: Mixture of multivariate (t) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values (2019)
  20. Young, Derek S.; Chen, Xi; Hewage, Dilrukshi C.; Nilo-Poyanco, Ricardo: Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering (2019)

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