MASS, codetools, ellipse, gclus, grid, lme4, mgcv (>= 1.6-1),
mlbench, mvtnorm, nnet

MASS, ellipse, grid, lme4, mvtnorm, nnet

MASS, codetools, diptest, ellipse, gclus, grid, lme4, mgcv
(>= 1.6-1), mlbench, mvtnorm, nnet

grid, methods, modeltools (>= 0.2-16), stats, stats4
MASS, codetools, diptest, ellipse, gclus, lme4, mgcv (>=
1.6-1), mlbench, mlogit, multcomp, mvtnorm, nnet, survival
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.

c(person("Bettina", "Gruen", role = c("aut", "cre"),
email = "Bettina.Gruen@jku.at"),
person("Friedrich", "Leisch", role = "aut"),
person("Deepayan", "Sarkar", role = "ctb"))
grid, methods, modeltools (>= 0.2-16), nnet, stats, stats4
codetools, diptest, ellipse, gclus, lme4 (>= 1.0), MASS, mgcv
(>= 1.6-1), mlbench, mlogit, multcomp, mvtnorm, survival
Bettina Gruen [aut, cre],
Friedrich Leisch [aut],
Deepayan Sarkar [ctb]

codetools, diptest, ellipse, gclus, lme4 (>= 1.0), MASS, mgcv
(>= 1.8-0), mlbench, mlogit, multcomp, mvtnorm, survival

c(person("Bettina", "Gruen", role = c("aut", "cre"),
email = "Bettina.Gruen@jku.at"),
person("Friedrich", "Leisch", role = "aut"),
person("Deepayan", "Sarkar", role = "ctb"),
person("Frederic", "Mortier", rol = "ctb"),
person("Nicolas", "Picard", rol = "ctb"))
codetools, diptest, ellipse, gclus, glmnet, lme4 (>= 1.0),
MASS, mgcv (>= 1.8-0), mlbench, mlogit, multcomp, mvtnorm,
survival
Bettina Gruen [aut, cre],
Friedrich Leisch [aut],
Deepayan Sarkar [ctb],
Frederic Mortier [ctb],
Nicolas Picard [ctb]

c(person("Bettina", "Gruen", role = c("aut", "cre"),
email = "Bettina.Gruen@jku.at"),
person("Friedrich", "Leisch", role = "aut"),
person("Deepayan", "Sarkar", role = "ctb"),
person("Frederic", "Mortier", role = "ctb"),
person("Nicolas", "Picard", role = "ctb"))
A general framework for finite mixtures of regression models
using the EM algorithm is implemented. The package 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.
graphics, grid, grDevices, methods, modeltools (>= 0.2-16),
nnet, stats, stats4
actuar, codetools, diptest, ellipse, gclus, glmnet, lme4 (>=
1.0), MASS, mgcv (>= 1.8-0), mlbench, mlogit, multcomp,
mvtnorm, SuppDists, survival