spsann: 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.

Package details

AuthorAlessandro Samuel-Rosa [aut, cre] (<https://orcid.org/0000-0003-0877-1320>), Lucia Helena Cunha dos Anjos [ths] (<https://orcid.org/0000-0003-0063-3521>), Gustavo de Mattos Vasques [ths], Gerard B M Heuvelink [ths] (<https://orcid.org/0000-0003-0959-9358>), Dick Brus [ctb] (<https://orcid.org/0000-0003-2194-4783>), Richard Murray Lark [ctb] (<https://orcid.org/0000-0003-2571-8521>), Edzer Pebesma [ctb] (<https://orcid.org/0000-0001-8049-7069>), Jon Skoien [ctb], Joshua French [ctb], Pierre Roudier [ctb]
MaintainerAlessandro Samuel-Rosa <alessandrosamuelrosa@gmail.com>
LicenseGPL (>= 2)
URL https://github.com/samuel-rosa/spsann/
Package repositoryView on CRAN
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spsann documentation built on May 2, 2019, 1:36 p.m.