Simulate a neutral landscape model using the Gibbs algorithm introduced in Gaucherel (2008).

nlm_mosaicgibbs(ncol, nrow, resolution = 1, germs, R, patch_classes,
rescale = TRUE)

## Arguments

ncol |
[`numerical(1)` ]
Number of columns forming the raster. |

nrow |
[`numerical(1)` ]
Number of rows forming the raster. |

resolution |
[`numerical(1)` ]
Resolution of the raster. |

germs |
[`numerical(1)` ]
Intensity parameter (non-negative integer). |

R |
[`numerical(1)` ]
Interaction radius (non-negative integer) for the fitting of the spatial point
pattern process - the min. distance between germs in map units. |

patch_classes |
[`numerical(1)` ]
Number of classes for germs. |

rescale |
[`logical(1)` ] If `TRUE` (default), the values
are rescaled between 0-1. |

## Value

RasterLayer

## Details

`nlm_mosaicgibbs`

offers the second option of simulating a neutral landscape model
described in Gaucherel (2008).
The method works in principal like the tessellation method (`nlm_mosaictess`

),
but instead of a random point pattern the algorithm fits a simulated realization of the Strauss
process. The Strauss process starts with a given number of points and
uses a minimization approach to fit a point pattern with a given interaction
parameter (0 - hardcore process; 1 - Poisson process) and interaction radius
(distance of points/germs being apart).

## References

Gaucherel, C. (2008) Neutral models for polygonal landscapes with linear
networks. *Ecological Modelling*, 219, 39 - 48.

## Examples

# simulate polygonal landscapes
mosaicgibbs <- nlm_mosaicgibbs(ncol = 40,
nrow = 30,
germs = 20,
R = 0.02,
patch_classes = 12)

# NOT RUN {
# visualize the NLM
landscapetools::show_landscape(mosaicgibbs)
# }