flexmix
Flexible Mixture Modeling
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, 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.
- Version2.3-19
- R version≥ 2.15.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- flexmix citation info
- Last release03/16/2023
Documentation
- VignetteFinite Mixture Model Diagnostics Using Resampling Methods
- VignetteFlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R
- VignetteFlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters
- VignetteApplications of finite mixtures of regression models
- MaterialNEWS
- In ViewsCluster
- In ViewsEnvironmetrics
- In ViewsPsychometrics
Team
Bettina Gruen
Friedrich Leisch
Deepayan Sarkar
Frederic Mortier
Show author detailsRolesContributorNicolas Picard
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- Depends2 packages
- Imports8 packages
- Suggests15 packages
- Reverse Depends6 packages
- Reverse Imports14 packages
- Reverse Suggests10 packages
- Reverse Enhances1 package