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 110 articles , 2 standard articles )

Showing results 61 to 80 of 110.
Sorted by year (citations)
  1. Rémi Lebret; Serge Iovleff; Florent Langrognet; Christophe Biernacki; Gilles Celeux; Gérard Govaert: Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library (2015) not zbMATH
  2. Sharon X. Lee, Geoffrey J. McLachlan: EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions (2015) arXiv
  3. Subedi, Sanjeena; Punzo, Antonio; Ingrassia, Salvatore; McNicholas, Paul D.: Cluster-weighted (t)-factor analyzers for robust model-based clustering and dimension reduction (2015)
  4. Arnošt Komárek; Lenka Komárková: Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data (2014) not zbMATH
  5. Arthur White; Thomas Murphy: BayesLCA: An R Package for Bayesian Latent Class Analysis (2014) not zbMATH
  6. Barbiero, Alessandro: An alternative discrete skew Laplace distribution (2014)
  7. Galimberti, Giuliano; Soffritti, Gabriele: A multivariate linear regression analysis using finite mixtures of (t) distributions (2014)
  8. Genge, Ewa: A latent class analysis of the public attitude towards the euro adoption in Poland (2014)
  9. Nummi, Tapio; Hakanen, Tiina; Lipiäinen, Liudmila; Harjunmaa, Ulla; Salo, Matti K.; Saha, Marja-Terttu; Vuorela, Nina: A trajectory analysis of body mass index for Finnish children (2014)
  10. Punzo, Antonio: Flexible mixture modelling with the polynomial Gaussian cluster-weighted model (2014)
  11. Ringle, Christian M.; Sarstedt, Marko; Schlittgen, Rainer: Genetic algorithm segmentation in partial least squares structural equation modeling (2014)
  12. Wang, Shaoli; Yao, Weixin; Huang, Mian: A note on the identifiability of nonparametric and semiparametric mixtures of GLMs (2014)
  13. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  14. Young, Derek S.: Mixtures of regressions with changepoints (2014)
  15. Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels: The discrete Laplace exponential family and estimation of Y-STR haplotype frequencies (2013)
  16. Bagnato, Luca; Punzo, Antonio: Finite mixtures of unimodal beta and gamma densities and the (k)-bumps algorithm (2013)
  17. Bordes, L.; Kojadinovic, I.; Vandekerkhove, P.: Semiparametric estimation of a two-component mixture of linear regressions in which one component is known (2013)
  18. Faria, S.; Gonçalves, F.: Financial data modeling by Poisson mixture regression (2013)
  19. Komárek, Arnošt; Komárková, Lenka: Clustering for multivariate continuous and discrete longitudinal data (2013)
  20. Marcos Prates; Victor Lachos; Celso Barbosa Cabral: mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions (2013) not zbMATH