A simple MP that aims for a reference catch (as a proxy for MSY)
subject to imperfect information.

`GB_CC(x, Data, reps = 100, plot = FALSE)`

## Arguments

- x
A position in the data object

- Data
A data object

- reps
The number of stochastic samples of the MP recommendation(s)

- plot
Logical. Show the plot?

## Value

An object of class `Rec-class`

with the `TAC`

slot populated with a numeric vector of length `reps`

## Details

Note that this is my interpretation of their MP and is now stochastic.
Currently it is generalized and is not 'tuned' to more detailed assessment
data which might explain why in some cases it leads to stock declines.

The TAC is calculated as:
$$\textrm{TAC} = C_\textrm{ref}$$
where \(C_\textrm{ref}\) is a reference catch assumed to be a proxy for MSY.
In the MSE \(C_\textrm{ref}\) is the calculated MSY subject to observation error
defined in `Obs@CV_Cref`

.

The TAC is subject to the following conditions:

if next TAC > 1.2 last catch, then TAC = 1.2 last catch

if next TAC < 0.8 last catch, then TAC = 0.8 last catch

## Required Data

See `Data-class`

for information on the `Data`

object

`GB_CC`

: Cref

## References

Geromont, H.F. and Butterworth, D.S. 2014. Complex assessment or
simple management procedures for efficient fisheries management: a
comparative study. ICES J. Mar. Sci. doi:10.1093/icesjms/fsu017

## See also

Other Constant Catch MPs:
`CC1()`

## Examples

```
GB_CC(1, MSEtool::SimulatedData, plot=TRUE)
#> TAC (median)
#> 1668.429
```