Maintainer: Peng He (phe@ab.mpg.de)

This package is for generating and visualising networks for
characterising the physical attributes and spatial organisations of
habitat components (i.e.Â habitat physical configurations). The network
generating algorithm first determines the `X`

and
`Y`

coordinates of `N`

nodes within a rectangle
with a side length of `L`

and an area of `A`

. Then
it computes the pair-wise Euclidean distance `Dij`

between
node `i`

and `j`

, and then a complete network with
`1/Dij`

as link weights is constructed. Then, the algorithm
removes links from the complete network with the probability as shown in
the function `ahn_prob`

in this package. Such link removals
can make the network disconnected whereas a connected network is wanted.
In such cases, the algorithm rewires one network component to its
spatially nearest neighbouring component and repeat doing this until the
network becomes connected again. Finally, it generates an undirected
network (weighted or unweighted, connected or disconnected, as usersâ€™
choices).

Currently, the package consists of three functions:

`ahn_gen()`

: generating or constructing networks`ahn_prob()`

: plotting the probability curves for removing links from the complete network`ahn_plot()`

: plotting networks generated by`ahn_gen()`

Install `AnimalHabitatNetwork`

from CRAN with:

`install.packages("AnimalHabitatNetwork")`

Below are some examples illustrating the uses of these functions:

```
library(AnimalHabitatNetwork)
# the use of the function ahn_gen()
ahn_gen(N = 30, L = 10, mu = .5, lamda = .75)
<- 10
N <- runif(N, 0, 5)
coord <- sample(LETTERS, N, replace = TRUE)
ql <- sample(1:20, N, replace = TRUE)
qn ahn_gen(N, L = 5, mu = 1, lamda = 5, Weighted = FALSE, X = coord, U = ql, V = qn)
ahn_gen(N, L = 5, mu = 1, lamda = 5, Weighted = TRUE, Connected = FALSE, Y = coord, U = ql, V = qn)
# the use of the function ahn_plot()
ahn_plot(ahn_gen(N = 30, L = 5, mu = .5, lamda = .75))
# the use of the function ahn_prob()
ahn_prob()
ahn_prob(Dij = seq(.05, 10, length.out = 30), mu = c(.1, 2, 5), lamda = c(.0001, .15, .35, 2, 30))
```

```
library(AnimalHabitatNetwork)
# generate networks with approximate structural properties
```