m.step: Maximization step
In QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data

Optimization of clone positions and proportion of mutations in each clone, based on the previously calculated expectation

1 2	m.step(fik, Schrod, previous.weights, previous.centers, contamination, adj.factor, optim = "default")

`fik`	Matrix giving the probability of each mutation to belong to a specific clone
`Schrod`	A list of dataframes (one for each sample), generated by the Patient_schrodinger_cellularities() function.
`previous.weights`	Weights from the previous optimization step (used as priors for this step)
`previous.centers`	Clone coordinates from previous optimization step (used as priors for this step)
`contamination`	Numeric vector with the fraction of normal cells contaminating the sample
`adj.factor`	Factor to compute the probability: makes transition between the cellularity of the clone and the frequency observed
`optim`	use L-BFS-G optimization from R ("default"), or from optimx ("optimx"), or Differential Evolution ("DEoptim")