An MP similar to SBT1 that modifies a time-series of catch recommendations
and aims for a stable catch rates.

`GB_slope(x, Data, reps = 100, plot = FALSE, yrsmth = 5, lambda = 1)`

## 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?

- yrsmth
Number of years for evaluating slope in relative abundance
index

- lambda
A gain parameter

## Value

An object of class `Rec-class`

with the `TAC`

slot populated with a numeric vector of length `reps`

## Details

The TAC is calculated as:
$$\textrm{TAC}_y= C_{y-1} \left(1+\lambda I\right)$$
where \(C_{y-1}\) is catch from the previous year, \(\lambda\) is a gain parameter, and \(I\) is
the slope of the linear regression of log Index (`Data@Ind`

) over the last
`yrsmth`

years.

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

Note that this is my interpretation of their approach 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.

## Required Data

See `Data-class`

for information on the `Data`

object

`GB_slope`

: Cat, Ind, Year

## 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

## Examples

```
GB_slope(1, MSEtool::SimulatedData, plot=TRUE)
#> TAC (median)
#> 1775.624
```