Compute the BIC of a given structure

1 2 |

`X` |
the dataset |

`Z` |
binary adjacency matrix of the structure (size p) |

`Bic_null_vect` |
the BIC of the null hypothesis (used for independent variables) |

`Bic_old` |
BIC (vector) associated to Zold |

`methode` |
parameter for OLS (matrix inversion) methode_BIC parameter for OLS (matrix inversion) 1:householderQr, 2:colPivHouseholderQr |

`Zold` |
another structure with some common parts with Z (allows to compute only the differences, to be faster) |

`star` |
boolean defining wether classical BIC or BIC* (over-penalized by a hierarchical uniform assumption to avoid over-learning)is computed |

The vector of the BICs associated to each covariate (conditionnal distribution) according to the sub-regression structure.

1 2 3 4 5 6 7 8 9 10 11 | ```
## Not run:
require(CorReg)
data=mixture_generator(n=15,p=5,valid=0)#dataset generation
Z=data$Z #binary adjacency matrix that describes correlations within the dataset
X=data$X_appr
Bic_null_vect=density_estimation(X=X)$BIC_vect
#Computes the BIC associated to each covariate (optional, BicZ can do it if not given as an input)
#computes the BIC associated to the structure
res=BicZ(X = X,Z = Z,Bic_null_vect=Bic_null_vect)
## End(Not run)
``` |

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