Description Usage Arguments Value Examples

Initialization of bidirectional hidden Markov models

1 |

`obs` |
The observations. A list of one or more entries containing the observation matrix ( |

`dStates` |
The number of directed states. |

`uStates` |
The number of undirected states. |

`method` |
Emission distribution of the model. One out of c("NegativeBinomial", "PoissonLogNormal", "NegativeMultinomial", "ZINegativeBinomial", "Poisson", "Bernoulli", "Gaussian", "IndependentGaussian") |

`dirFlags` |
The flag sequence is needed when a bdHMM is fitted on undirected data (e.g.) ChIP only. It is a |

`directedObs` |
Integer vector defining the directionality (or strand-specificity) of the data tracks. Undirected (non-strand-specific) data tracks (e.g. ChIP) are indicated by '0'. Directed (strand-specific) data tracks are indicated by increasing pairs of integers. For instance c(0,0,0,1,1,2,2): The first three data tracks are undirected, followed by two pairs of directed measurements. |

`sizeFactors` |
Library size factors for Emissions PoissonLogNormal or NegativeBinomial as a length(obs) x ncol(obs[[1]]) matrix. |

`sharedCov` |
If TRUE, (co-)variance of (Independent)Gaussian is shared over states. Only applicable to 'Gaussian' or 'IndependentGaussian' emissions. Default: FALSE. |

A HMM object.

1 2 | ```
data(example)
bdHMM_ex = initBdHMM(observations, dStates=3, method="Gaussian")
``` |

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