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.
| Version: |
2.3-19 |
| Depends: |
R (≥ 2.15.0), lattice |
| Imports: |
graphics, grid, grDevices, methods, modeltools (≥ 0.2-16), nnet, stats, stats4, utils |
| Suggests: |
actuar, codetools, diptest, Ecdat, ellipse, gclus, glmnet, lme4 (≥ 1.1), MASS, mgcv (≥ 1.8-0), mlbench, multcomp, mvtnorm, SuppDists, survival |
| Published: |
2023-03-16 |
| DOI: |
10.32614/CRAN.package.flexmix |
| Author: |
Bettina Gruen 
[aut, cre],
Friedrich Leisch
[aut],
Deepayan Sarkar
[ctb],
Frederic Mortier [ctb],
Nicolas Picard
[ctb] |
| Maintainer: |
Bettina Gruen <Bettina.Gruen at R-project.org> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Citation: |
flexmix citation info |
| Materials: |
NEWS |
| In views: |
Cluster, Environmetrics, Psychometrics |
| CRAN checks: |
flexmix results |