Computes (signed) modularity statistic given a network and community structure. Allows the resolution parameter to be set

## Arguments

- network
Matrix or data frame. A symmetric matrix representing a network

- memberships
Numeric (length =

`ncol(network)`

). A numeric vector of integer values corresponding to each node's community membership- resolution
Numeric (length = 1). A parameter that adjusts modularity to prefer smaller (

`resolution`

> 1) or larger (0 <`resolution`

< 1) communities. Defaults to`1`

(standard modularity computation)- signed
Boolean (length = 1). Whether signed or absolute modularity should be computed. The most common modularity metric is defined by positive values only. Gomez et al. (2009) introduced a signed version of modularity that will discount modularity for edges with negative values. This property isn't always desired for psychometric networks. If

`TRUE`

, then this signed modularity metric will be computed. If`FALSE`

, then the absolute value of the edges in the network (using`abs`

) will be used to compute modularity. Defaults to`FALSE`

## References

Gomez, S., Jensen, P., & Arenas, A. (2009).
Analysis of community structure in networks of correlated data.
*Physical Review E*, *80*(1), 016114.

## Examples

```
# Load data
wmt <- wmt2[,7:24]
# Estimate EGA
ega.wmt <- EGA(wmt, model = "glasso")
# Compute standard (absolute values) modularity
modularity(
network = ega.wmt$network,
memberships = ega.wmt$wc,
signed = FALSE
)
#> [1] 0.1697952
# 0.1697952
# Compute signed modularity
modularity(
network = ega.wmt$network,
memberships = ega.wmt$wc,
signed = TRUE
)
#> [1] 0.1701946
# 0.1701946
```