Uses a multivariate array of time series to estimate Fisher information following the approach of Karunanithi et al. (2010).

## Arguments

- data
A numeric matrix of individual time series across the columns. These could be different species, populations or measurements. The first column must be an equally spaced time vector.

- sost
A 1 x n matrix where n is a length equal to the number of time series in

`data`

. Each value is the 'size of state' tolerable for that time series and typically is represented by the standard deviation of the time series during a reference period.- winsize
Numeric value. Defines the window size of the rolling window as a percentage of the time series length.

- winspace
Numeric value. The number of data points to roll the window over in each iteration. Must be less than

`winsize`

.- TL
Numeric value. The 'tightening level' or percentage of points shared between states that allows the algorithm to classify data points as the same state.

## Value

A list containing three objects:

- FI
A dataframe of Fisher information estimates and the last time point contributing to each window.

- midt_win
A numeric vector of the time index at the centre of the window for that associated value in

`FI`

.- t_win
A n x m numeric matrix where the length of n is the winspace and length of m is the number of window shifts made. Values are consequently the timepoint indices that contribute to that window.

## Examples

```
#Load the multivariate simulated
#dataset `simTransComms`
data(simTransComms)
#Estimate the size-of-states for each
#time series in the first community.
#This is typically suggested
#to be the standard deviation of a
#reference period or the entire time
#series
eg.sost <- t(apply(simTransComms$community1[,3:7], MARGIN = 2, FUN = sd))
#transpose required to ensure a 1 x n matrix
egFI <- FI(data = simTransComms$community1[1:50,2:7],
sost = eg.sost,
winsize = 10,
winspace = 1,
TL = 90)
```