## risk.iman_conover()


Reorders each component's draws to a target correlation (Iman-Conover).


Usage


``` python
risk.iman_conover(
    pd,
    correlation,
    seed,
)
```


Every component keeps exactly its own draws; only their pairing across simulations changes. The correlation of the result's normal scores is close to `correlation`, and Spearman's rho close to `(6 / pi) asin(correlation / 2)`.


## Parameters


`pd: PredictiveDistribution`  

`correlation: list of list of float`  
One row and column per component.

`seed: int`  


## Returns


`PredictiveDistribution`  


## Examples

``` python
>>> from prospicio.distributions import PredictiveDistribution
>>> from prospicio.risk import iman_conover
>>> rows = [[float(i), float((i * 7919) % 1000)] for i in range(1000)]
>>> pd = PredictiveDistribution(["lob"], [(0,), (1,)], rows)
>>> joined = iman_conover(pd, [[1.0, 0.7], [0.7, 1.0]], seed=3)
>>> sorted(joined.marginal((1,)).draws) == sorted(pd.marginal((1,)).draws)
```

True