Description Usage Arguments Details Value Examples

Call methylation status of cytosines (or bins) with a Hidden Markov Model.

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`data` |
A |

`fit.on.chrom` |
A character vector specifying the chromosomes on which the HMM will be fitted. |

`transDist` |
The decaying constant for the distance-dependent transition matrix. Either a single numeric or a named numeric vector, where the vector names correspond to the transition contexts. Such a vector can be obtained from |

`eps` |
Convergence threshold for the Baum-Welch algorithm. |

`max.time` |
Maximum running time in seconds for the Baum-Welch algorithm. |

`max.iter` |
Maximum number of iterations for the Baum-Welch algorithm. |

`count.cutoff` |
A cutoff for the counts to remove artificially high counts from mapping artifacts. Set to |

`verbosity` |
An integer from 1 to 5 specifying the verbosity of the fitting procedure. Values > 1 are only for debugging. |

`num.threads` |
Number of CPU to use for the computation. Parallelization is implemented on the number of states, which is 2 or 3, so setting |

`initial.params` |
A |

`include.intermediate` |
A logical specifying wheter or not the intermediate component should be included in the HMM. |

`update` |
One of |

`min.reads` |
The minimum number of reads that a position must have to contribute in the Baum-Welch fitting procedure. |

The Hidden Markov model uses a binomial test for the emission densities. Transition probabilities are modeled with a distance dependent decay, specified by the parameter `transDist`

.

A `methimputeBinomialHMM`

object.

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ataudt/methimpute documentation built on May 10, 2019, 2:07 p.m.

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