EM.algo: Expectation Maximization algorithm
In QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data

Optimization of clone positions and proportion of mutations in each clone.

1 2	EM.algo(Schrod, nclust = NULL, prior_center = NULL, prior_weight = NULL, contamination, epsilon = 10^(-2), optim = "default")

`Schrod`	A list of dataframes (one for each sample), generated by the Patient_schrodinger_cellularities() function.
`nclust`	Number of clones to look for (mandatory if prior_center or prior_weight are null)
`prior_center`	Clone coordinates (from another analysis) to be used
`prior_weight`	Prior on the fraction of mutation in each clone
`contamination`	Numeric vector with the fraction of normal cells contaminating the sample
`epsilon`	Stop value: maximal admitted value of the difference in cluster position and weights between two optimization steps. If NULL, will take 1/(median depth).
`optim`	use L-BFS-G optimization from R ("default"), or from optimx ("optimx")