This function provides a Monte-Carlo estimate of the power of the permutation tests proposed in this package.

```
power2(
model1 = "gnp",
model2 = "k_regular",
n1 = 20L,
n2 = 20L,
num_vertices = 25L,
model1_params = NULL,
model2_params = NULL,
representation = "adjacency",
distance = "frobenius",
stats = c("flipr:t_ip", "flipr:f_ip"),
B = 1000L,
alpha = 0.05,
test = "exact",
k = 5L,
R = 1000L,
seed = 1234
)
```

- model1
A string specifying the model to be used for generating the first sample. Choices are

`"sbm"`

,`"k_regular"`

,`"gnp"`

,`"smallworld"`

,`"pa"`

,`"poisson"`

and`"binomial"`

. Defaults to`"gnp"`

.- model2
A string specifying the model to be used for generating the second sample. Choices are

`"sbm"`

,`"k_regular"`

,`"gnp"`

,`"smallworld"`

,`"pa"`

,`"poisson"`

and`"binomial"`

. Defaults to`"k_regular"`

.- n1
The size of the first sample. Defaults to

`20L`

.- n2
The size of the second sample. Defaults to

`20L`

.- num_vertices
The number of nodes in the generated graphs. Defaults to

`25L`

.- model1_params
A named list setting the parameters of the first chosen model. Defaults to

`list(p = 1/3)`

.- model2_params
A named list setting the parameters of the second chosen model. Defaults to

`list(k = 8L)`

.- representation
A string specifying the desired type of representation, among:

`"adjacency"`

,`"laplacian"`

and`"modularity"`

. Defaults to`"adjacency"`

.- distance
A string specifying the chosen distance for calculating the test statistic, among:

`"hamming"`

,`"frobenius"`

,`"spectral"`

and`"root-euclidean"`

. Defaults to`"frobenius"`

.- stats
A character vector specifying the chosen test statistic(s), among:

`"original_edge_count"`

,`"generalized_edge_count"`

,`"weighted_edge_count"`

,`"student_euclidean"`

,`"welch_euclidean"`

or any statistics based on inter-point distances available in the**flipr**package:`"flipr:student_ip"`

,`"flipr:fisher_ip"`

,`"flipr:bg_ip"`

,`"flipr:energy_ip"`

,`"flipr:cq_ip"`

. Defaults to`c("flipr:student_ip", "flipr:fisher_ip")`

.- B
The number of permutation or the tolerance. If this number is lower than

`1`

, it is intended as a tolerance. Otherwise, it is intended as the number of required permutations. Defaults to`1000L`

.- alpha
Significance level for hypothesis testing. Defaults to

`0.05`

.- test
A character string specifying the formula to be used to compute the permutation p-value. Choices are

`"estimate"`

,`"upper_bound"`

and`"exact"`

. Defaults to`"exact"`

which provides exact tests.- k
An integer specifying the density of the minimum spanning tree used for the edge count statistics. Defaults to

`5L`

.- R
Number of Monte-Carlo trials used to estimate the power. Defaults to

`1000L`

.- seed
An integer specifying the random generator seed. Defaults to `1234.

A numeric value estimating the power of the test.

Currently, six scenarios of pairs of populations are implemented. Scenario 0 allows to make sure that all our permutation tests are exact.