copulareg {copulaboost} | R Documentation |

This function fits joint distributions with an R-vine pair copula structure, that is constructed in a specific way so that the conditional density and distribution of the variable y can be computed explicitly.

```
copulareg(
y,
x,
var_type_y,
var_type_x,
distr_x = NULL,
distr_y = NULL,
dvine = FALSE,
family_set = c("gaussian", "clayton", "gumbel")
)
```

`y` |
A vector of n observations of the (univariate) outcome variable y |

`x` |
A (n x p) matrix of n observations of p covariates |

`var_type_y` |
A character that has to be specified as "d" or "c" to indicate whether y is discrete or continuous, respectively. |

`var_type_x` |
A vector of p characters that have to take the value "c" or "d" to indicate whether each margin of the covariates is discrete or continuous. |

`distr_x` |
Internally created object that contains a nested list. The first element of the 'outer' list is a list where each element is a distribution object similar to that created by ecdf, the second element is a function 'transform' that takes a matrix of values of x, and returns the corresponding cumulative distributions F(x). |

`distr_y` |
Similar to distr_x, but for the outcome y. |

`dvine` |
Logical variable indicating whether the function should fit a canonical d-vine to (y, x) with the ordering (x_1, ..., x_p, y). |

`family_set` |
A vector of strings that specifies the set of pair-copula families that the fitting algorithm chooses from. For an overview of which values that can be specified, see the documentation for bicop. |

A copulareg object. Consists of 'model', an rvinecopulib 'vinecop' object for the copula-model for (x_1, ..., x_p, y), a hash table containing all of the conditionals for the model (for the training set), objects distr_x and distr_y that contain the marginal distributions of the covariates and the outcome, and y, the y-values for the training data.

[Package *copulaboost* version 0.1.0 Index]