Maximization of the likelihood given a mixture of binomial distributions

1 2 3 4 | ```
EM_clustering(Schrod, contamination, prior_weight = NULL,
clone_priors = NULL, maxit = 8, nclone_range = 2:5, epsilon = 5 *
(10^(-3)), ncores = 2, model.selection = "BIC", optim = "default",
keep.all.models = 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 |

`maxit` |
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") |

`keep.all.models` |
Should the function output the best model (default; FALSE), or all models tested (if set to true) |

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