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

Maximization of the likelihood given a mixture of binomial distributions

EM_clustering(Schrod, contamination, prior_weight = NULL,
  clone_priors = NULL, Initializations = 1, nclone_range = 2:5,
  epsilon = 0.01, ncores = 2, model.selection = "BIC",
  optim = "default", keep.all.models = FALSE, FLASH = FALSE)

`Schrod`	List of dataframes, output of the Schrodinger function or the EM algorithm
`contamination`	The fraction of normal cells in the sample
`prior_weight`	If known a list of priors (fraction of mutations in a clone) to be used in the clustering
`clone_priors`	If known a list of priors (cell prevalence) to be used in the clustering
`Initializations`	Maximal number of independant initial condition tests to be tried
`nclone_range`	Number of clusters to look for
`epsilon`	Stop value: maximal admitted value of the difference in cluster position and weights between two optimization steps.
`ncores`	Number of CPUs to be used
`model.selection`	The function to minimize for the model selection: can be "AIC", "BIC", or numeric. In numeric, the function uses a variant of the BIC by multiplication of the k*ln(n) factor. If >1, it will select models with lower complexity.
`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)
`FLASH`	should it use FLASH algorithm to create priors