parallelEM: 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. Then gives out the possibility with maximal likelihood Relies on foreach

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parallelEM(Schrod, nclust, epsilon, contamination, prior_center = NULL,
  prior_weight = NULL, Initializations = 1, optim = "default",
  keep.all.models = FALSE)

`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)
`epsilon`	Stopping condition for the algorithm: what is the minimal tolerated difference of position or weighted between two steps
`contamination`	Numeric vector with the fraction of normal cells contaminating the sample
`prior_center`	Clone coordinates (from another analysis) to be used
`prior_weight`	Prior on the fraction of mutation in each clone
`Initializations`	Maximal number of independant initial condition tests to be tried
`optim`	use L-BFS-G optimization from R ("default"), or from optimx ("optimx"), or Differential Evolution ("DEoptim")
`keep.all.models`	Should the function output the best model (default; FALSE), or all models tested (if set to true)