Calculate mahalanobis distance (null and alternative MSEs) and statistical power for all MPs in an MSE

```
PRBcalc(
MSE_null,
MSE_alt,
tsd = c("Cat", "Cat", "Cat", "Ind", "ML"),
stat = c("slp", "AAV", "mu", "slp", "slp"),
dnam = c("C_S", "C_V", "C_M", "I_S", "ML_S"),
res = 6,
alpha = 0.05,
plotCC = FALSE,
removedat = FALSE,
removethresh = 0.025
)
```

## Arguments

- MSE_null
An object of class MSE representing the null hypothesis

- MSE_alt
An object of class MSE representing the alternative hypothesis

- tsd
Character string of data types: Cat = catch, Ind = relative abundance index, ML = mean length in catches

- stat
Character string defining the quantity to be calculated for each data type, slp = slope(log(x)), AAV = average annual variability, mu = mean(log(x))

- dnam
Character string of names for the quantities calculated

- res
Integer, the resolution (time blocking) for the calculation of PPD

- alpha
Probability of incorrectly rejecting the null operating model when it is valid

- plotCC
Logical, should the PPD cross correlations be plotted?

- removedat
Logical, should data not contributing to the mahalanobis distance be removed?

- removethresh
Positive fraction: the cumulative percentage of removed data (removedat=TRUE) that contribute to the mahalanobis distance

## Value

A list object with two hierarchies of indexing, first by MP, second has two positions as described in Probs: (1) mahalanobis distance, (2) a matrix of type 1 error
(first row) and statistical power (second row), by time block.

## References

Carruthers, T.R, and Hordyk, A.R. In press. Using management strategy evaluation to establish indicators of changing fisheries.
Canadian Journal of Fisheries and Aquatic Science.