runHMM: A function to fit unsupervised Hidden Markov model
In snapCGH: Segmentation, normalisation and processing of aCGH data

This function fits an unsupervised Hidden Markov model to a given MAList or SegList

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runHomHMM(input, vr = 0.01,
                maxiter = 100, criteria = "AIC", delta = NA,
                full.output = FALSE, eps = 0.01)

`input`	an object of class `MAList` or `SegList`
`vr`	Gets passed to the function `repeated::hidden` as the `pshape` argument.
`maxiter`	Gets passed to the function `repeated::hidden` as the `iterlim` argument.
`criteria`	Choice of which selection criteria should be used in the algorithm. The choices are either AIC or BIC

`delta`	Delta value used of the BIC is selected. If no value is entered it defaults to 1.
`full.output`	if true the SegList output includes a probability that a clone is in its assigned state and a smoothed value for the clone.
`eps`	parameter controlling the convergence of the EM algorithm.