These functions conduct conditional uniform graph (CUG) or permutation (QAP) tests of any graph-level statistic.

```
test_random(
.data,
FUN,
...,
times = 1000,
strategy = "sequential",
verbose = FALSE
)
test_permutation(
.data,
FUN,
...,
times = 1000,
strategy = "sequential",
verbose = FALSE
)
```

- .data
An object of a

`{manynet}`

-consistent class:matrix (adjacency or incidence) from

`{base}`

Redgelist, a data frame from

`{base}`

R or tibble from`{tibble}`

igraph, from the

`{igraph}`

packagenetwork, from the

`{network}`

packagetbl_graph, from the

`{tidygraph}`

package

- FUN
A graph-level statistic function to test.

- ...
Additional arguments to be passed on to FUN, e.g. the name of the attribute.

- times
Integer indicating number of simulations used for quantile estimation. (Relevant to the null hypothesis test only - the analysis itself is unaffected by this parameter.) Note that, as for all Monte Carlo procedures, convergence is slower for more extreme quantiles. By default,

`times=1000`

. 1,000 - 10,000 repetitions recommended for publication-ready results.- strategy
If

`{furrr}`

is installed, then multiple cores can be used to accelerate the function. By default`"sequential"`

, but if multiple cores available, then`"multisession"`

or`"multicore"`

may be useful. Generally this is useful only when`times`

> 1000. See`{furrr}`

for more.- verbose
Whether the function should report on its progress. By default FALSE. See

`{progressr}`

for more.

`test_random()`

: Returns test results for some measure on an object against a distribution of measures on random networks of the same dimensions`test_permutation()`

: Returns test results for some measure on an object against a distribution of measures on permutations of the original network

Other models:
`play`

,
`regression`

```
marvel_friends <- to_unsigned(ison_marvel_relationships)
marvel_friends <- to_giant(marvel_friends) %>%
to_subgraph(PowerOrigin == "Human")
(cugtest <- test_random(marvel_friends, network_heterophily, attribute = "Attractive",
times = 200))
#>
#> CUG Test Results
#>
#> Observed Value: -0.8571429
#> Pr(X>=Obs): 1
#> Pr(X<=Obs): 0
#>
plot(cugtest)
(qaptest <- test_permutation(marvel_friends,
network_heterophily, attribute = "Attractive",
times = 200))
#>
#> QAP Test Results
#>
#> Observed Value: -0.8571429
#> Pr(X>=Obs): 0.97
#> Pr(X<=Obs): 0.07
#>
plot(qaptest)
```