Description Usage Arguments Value Author(s) References See Also Examples

Forward-backward algorithm called in fit.MSAR.

1 2 3 | ```
Estep.MSAR(data, theta, smth = FALSE,
verbose = FALSE,
covar.emis = covar.emis, covar.trans = covar.trans)
``` |

`data` |
array of univariate or multivariate series with dimension T*N.samples*d. T: number of time steps of each sample, N.samples: number of realisations of the same stationary process, d: dimension. |

`theta` |
model's parameter; object of class MSAR. See also init.theta.MSAR. . |

`smth` |
If smth=FALSE, only the forward step is computed for forecasting probabilities. If smth=TRUE, the smoothing probabilities are computed too. |

`verbose` |
if verbose=TRUE some results are printed at each iteration. |

`covar.emis` |
covariables for emission probabilities. |

`covar.trans` |
covariables for transition probabilities. |

A list including

`loglik` |
log likelihood |

`probS` |
smoothing probabilities: |

`probSS` |
one step smoothing probabilities: |

Valerie Monbet, valerie.monbet@univ-rennes1.fr

Ailliot P., Monbet V., (2012), Markov switching autoregressive models for wind time series. Environmental Modelling & Software, 30, pp 92-101.

fit.MSAR, Mstep.hh.MSAR

1 | ```
#see fit.MSAR
``` |

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