Generate a random point pattern, a simulated realisation of the Gauss-Poisson Process.

```
rGaussPoisson(kappa, r, p2, win = owin(c(0,1),c(0,1)),
…, nsim=1, drop=TRUE)
```

kappa

Intensity of the Poisson process of cluster centres. A single positive number, a function, or a pixel image.

r

Diameter of each cluster that consists of exactly 2 points.

p2

Probability that a cluster contains exactly 2 points.

win

Window in which to simulate the pattern.
An object of class `"owin"`

or something acceptable to `as.owin`

.

…

Ignored.

nsim

Number of simulated realisations to be generated.

drop

Logical. If `nsim=1`

and `drop=TRUE`

(the default), the
result will be a point pattern, rather than a list
containing a point pattern.

A point pattern (an object of class `"ppp"`

)
if `nsim=1`

, or a list of point patterns if `nsim > 1`

.

Additionally, some intermediate results of the simulation are
returned as attributes of the point pattern.
See `rNeymanScott`

.

This algorithm generates a realisation of the Gauss-Poisson
point process inside the window `win`

.
The process is constructed by first
generating a Poisson point process of parent points
with intensity `kappa`

. Then each parent point is either retained
(with probability `1 - p2`

)
or replaced by a pair of points at a fixed distance `r`

apart
(with probability `p2`

). In the case of clusters of 2 points,
the line joining the two points has uniform random orientation.

In this implementation, parent points are not restricted to lie in the window; the parent process is effectively the uniform Poisson process on the infinite plane.

# NOT RUN { pp <- rGaussPoisson(30, 0.07, 0.5) # }