Description Usage Arguments Value See Also Examples
Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.
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modelName 
A character string indicating the model. The help file for

data 
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. 
z 
A matrix whose 
prior 
Specification of a conjugate prior on the means and variances.
See the help file for 
control 
A list of control parameters for EM. The defaults are set by the call

Vinv 
If the model is to include a noise term, 
warn 
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is set in 
... 
Catches unused arguments in indirect or list calls via 
A list including the following components:
modelName 
A character string identifying the model (same as the input argument). 
n 
The number of observations in the data. 
d 
The dimension of the data. 
G 
The number of mixture components. 
z 
A matrix whose 
parameters 

loglik 
The log likelihood for the data in the mixture model. 
control 
The list of control parameters for EM used. 
prior 
The specification of a conjugate prior on the means and variances used,

Attributes: 

meE
,...,
meVVV
,
em
,
mstep
,
estep
,
priorControl
,
mclustModelNames
,
mclustVariance
,
mclust.options
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