Computes the Ergodicity Information Index

## Usage

```
ergoInfo(
dynEGA.object,
use = c("edge.list", "unweighted", "weighted"),
shuffles = 5000
)
```

## Arguments

- dynEGA.object
A

`dynEGA.ind.pop`

object- use
Character (length = 1). A string indicating what network element will be used to compute the algorithm complexity, the list of edges or the weights of the network. Defaults to

`use = "unweighted"`

. Current options are:`"edge.list"`

--- Calculates the algorithm complexity using the list of edges`"unweighted"`

--- Calculates the algorithm complexity using the binary weights of the encoded prime transformed network. 0 = edge absent and 1 = edge present`"weighted"`

--- Calculates the algorithm complexity using the weights of encoded prime-weight transformed network

- shuffles
Numeric. Number of shuffles used to compute the Kolmogorov complexity. Defaults to

`5000`

## Value

Returns a list containing:

- PrimeWeight
The prime-weight encoding of the individual networks

- PrimeWeight.pop
The prime-weight encoding of the population network

- Kcomp
The Kolmogorov complexity of the prime-weight encoded individual networks

- Kcomp.pop
The Kolmogorov complexity of the prime-weight encoded population network

- complexity
The complexity metric proposed by Santora and Nicosia (2020)

- EII
The Ergodicity Information Index

## References

**Original Implementation**

Golino, H., Nesselroade, J. R., & Christensen, A. P. (2022).
Toward a psychology of individuals: The ergodicity information index and a bottom-up approach for finding generalizations.
*PsyArXiv*.

## Author

Hudson Golino <hfg9s at virginia.edu> and Alexander Christensen <alexpaulchristensen@gmail.com>

## Examples

```
# Obtain data
sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
if (FALSE) {
# Dynamic EGA individual and population structure
dyn.ega1 <- dynEGA.ind.pop(
data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
delta = 1, id = 25, use.derivatives = 1,
ncores = 2, corr = "pearson"
)
# Compute empirical ergodicity information index
eii <- ergoInfo(dyn.ega1)}
```