FlexMix: A general framework for finite mixture models and latent class regression in R

FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per individual, the usual formula interface of the S language is used for convenient model specification, and a modular concept of driver functions allows to interface many different types of regression models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models.

• Publication year

  2004

• Venue

  Journal of Statistical Software

• Publication date

  2004-10-18

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.18637/JSS.V011.I08
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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