Description Usage Arguments Details Value See Also Examples

Implements the Gausianization to help relax the assumption of normality.

1 2 |

`x` |
The |

`npn.func` |
The transformation function used in the npn transformation. If |

`npn.thresh` |
The truncation threshold used in nonparanormal transformation, ONLY applicable when |

`verbose` |
If |

The nonparanormal extends Gaussian graphical models to semiparametric Gaussian copula models.Motivated by sparse additive models, the nonparanormal method estimates the Gaussian copula by marginally transforming the variables using smooth functions.Computationally, the estimation of a nonparanormal transformation is very efficient and only requires one pass of the data matrix.

`data` |
A |

`huge`

and `huge-package`

.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# generate nonparanormal data
L = huge.generator(graph = "cluster", g = 5)
L$data = L$data^5
# transform the data using the shrunken ECDF
Q = huge.npn(L$data)
# transform the non-Gaussian data using the truncated ECDF
Q = huge.npn(L$data, npn.func = "truncation")
# transform the non-Gaussian data using the truncated ECDF
Q = huge.npn(L$data, npn.func = "skeptic")
``` |

```
Generating data from the multivariate normal distribution with the cluster graph structure....done.
Conducting the nonparanormal (npn) transformation via shrunkun ECDF....done.
Conducting nonparanormal (npn) transformation via truncated ECDF....done.
Conducting nonparanormal (npn) transformation via skeptic....done.
```

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