Supplies a list of values including tolerances for singularity and
convergence assessment, for use functions involving EM within *MCLUST*.

`emControl(eps, tol, itmax, equalPro)`

A named list in which the names are the names of the arguments and the values are the values supplied to the arguments.

- eps
A scalar tolerance associated with deciding when to terminate computations due to computational singularity in covariances. Smaller values of

`eps`

allow computations to proceed nearer to singularity. The default is the relative machine precision`.Machine$double.eps`

, which is approximately \(2e-16\) on IEEE-compliant machines.- tol
A vector of length two giving relative convergence tolerances for the log-likelihood and for parameter convergence in the inner loop for models with iterative M-step ("VEI", "VEE", "EVE", "VVE", "VEV"), respectively. The default is

`c(1.e-5, sqrt(.Machine$double.eps))`

. If only one number is supplied, it is used as the tolerance for the outer iterations and the tolerance for the inner iterations is as in the default.- itmax
A vector of length two giving integer limits on the number of EM iterations and on the number of iterations in the inner loop for models with iterative M-step ("VEI", "VEE", "EVE", "VVE", "VEV"), respectively. The default is

`c(.Machine$integer.max, .Machine$integer.max)`

allowing termination to be completely governed by`tol`

. If only one number is supplied, it is used as the iteration limit for the outer iteration only.- equalPro
Logical variable indicating whether or not the mixing proportions are equal in the model. Default:

`equalPro = FALSE`

.

`emControl`

is provided for assigning values and defaults
for EM within *MCLUST*.

`em`

,
`estep`

,
`me`

,
`mstep`

,
`mclustBIC`

```
irisBIC <- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6))
summary(irisBIC, iris[,-5])
```

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