This function returns the precision matrix and the expectation associated to the data matrix X using `desp`

and choosing the tuning parameters *lambda* and *gamma* by `v`

-fold cross-validation that uses a robust loss function. The expression of the loss function is provided in the companion vignette.

1 |

`X` |
The data matrix. |

`v` |
The number of folds. |

`lambda.range` |
The range of the penalization parameter |

`gamma.range` |
The range of the penalization parameter |

`settings` |
A list including all the parameters needed for the estimation. Please refer to the documentation of the function |

`desp.cv`

returns an object with S3 class "desp.cv" containing the estimated parameters along with the selected values of the tuning parameters, with components:

`Omega` |
The precision matrix. |

`mu` |
The expectation vector. |

`Theta` |
The matrix corresponding to outliers. |

`lambda` |
The selected lambda. |

`gamma` |
The selected gamma. |

Arnak Dalalyan and Samuel Balmand.

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