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

Optimization of clone positions and proportion of mutations in each clone followed by filtering on most likely possibility for each mutation and a re-optimization.

1 2	FullEM(Schrod, nclust, prior_center, prior_weight = NULL, contamination, epsilon = 5 * 10^(-3), 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`	Stopping condition for the algorithm: what is the minimal tolerated difference of position or weighted between two steps
`optim`	use L-BFS-G optimization from R ("default"), or from optimx ("optimx"), or Differential Evolution ("DEoptim")