Description Usage Arguments Details Value References See Also Examples

Suave uses `vegas`

-like importance sampling combined
with a globally adaptive subdivision strategy: Until the requested accuracy is reached,
the region with the largest error at the time is bisected in the dimension in which the
fluctuations of the integrand are reduced most. The number of new samples in each half
is prorated for the fluctuation in that half.

1 2 3 4 5 6 |

`ndim` |
same as |

` ncomp` |
same as |

` integrand` |
same as |

` ...` |
same as |

`lower` |
same as |

`upper` |
same as |

`rel.tol` |
same as |

`abs.tol` |
same as |

`flags` |
same as |

`min.eval` |
same as |

`max.eval` |
same as |

`nnew` |
the number of new integrand evaluations in each subdivision. |

`flatness` |
This parameter determines how
prominently “outliers”, i.e. individual samples with a large
fluctuation,
figure in the total fluctuation,
which in turn determines how a region is split up. As suggested by its name, |

See details in the documentation.

Idem as `cuhre`

T. Hahn (2005) CUBA-a library for multidimensional numerical
integration. *Computer Physics Communications*, **168**, 78-95.

1 2 3 4 5 6 7 8 9 |

```
Suave input parameters:
ndim 3
ncomp 1
rel.tol 0.001
abs.tol 1e-12
smooth 0
pseudo.random 0
final 0
verbose 2
min.eval 0
max.eval 50000
nnew 1000
flatness 50
Iteration 1: 1000 integrand evaluations so far
[1] 0.664916 +- 0.0138647 chisq 0 (0 df)
Iteration 2: 2000 integrand evaluations so far
[1] 0.664343 +- 0.0046336 chisq 0.00737292 (2 df)
Iteration 3: 3000 integrand evaluations so far
[1] 0.664623 +- 0.0026616 chisq 1.09695 (5 df)
Iteration 4: 4000 integrand evaluations so far
[1] 0.664393 +- 0.00118042 chisq 1.60235 (8 df)
Iteration 5: 5000 integrand evaluations so far
[1] 0.664433 +- 0.000802054 chisq 2.52582 (12 df)
Iteration 6: 6000 integrand evaluations so far
[1] 0.664746 +- 0.000612748 chisq 4.38844 (16 df)
integral: 0.6647464 (+-0.00061)
nregions: 6; number of evaluations: 6000; probability: 0.001946077
```

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