Optimization of Sample Configurations using Spatial Simulated Annealing

Methods to optimize sample configurations using spatial simulated annealing. Multiple objective functions are implemented for various purposes, such as variogram estimation, spatial trend estimation and spatial interpolation. A general purpose spatial simulated annealing function enables the user to define his/her own objective function. Solutions for augmenting existing sample configurations and solving multi-objective optimization problems are available as well.

Optimization of Sample Configurations using Spatial Simulated Annealing

Installation

The spsann package was created to help you creating optimum spatial sample configurations using the power of spatial simulated annealing. It can be downloaded from CRAN using:

install.packages(pkgs = "spsann")

The development version of spsann, available at GitHub, can be downloaded -- using the devtools package -- with:

if (!require(devtools)) {
  install.packages(pkgs = "devtools")
}
devtools::install_github("samuel-rosa/spsann")

How to collaborate

We use the fork & pull collaborative development model. This means that you are free to make a parallel copy of this repository, change the source code as you see fit, and then push the changes to your personal copy of this repository. All this without requiring any authorization. If the changes you've made to your personal copy of this repository are interesting and you're willing to share them with us, then just have them pulled -- pull request -- to this repository. After reviewing the changes, we will decide whether they can be merged -- merge -- with the source code of this repository.