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.
Version: | 2.2.0 |
Imports: | methods, pedometrics, Rcpp, sp, SpatialTools |
LinkingTo: | Rcpp |
Suggests: | gstat, tcltk, knitr |
Published: | 2019-04-29 |
Author: | Alessandro Samuel-Rosa
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Maintainer: | Alessandro Samuel-Rosa <alessandrosamuelrosa at gmail.com> |
BugReports: | https://github.com/samuel-rosa/spsann/issues/ |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://github.com/samuel-rosa/spsann/ |
NeedsCompilation: | yes |
Language: | en-GB |
Materials: | README NEWS |
In views: | Spatial |
CRAN checks: | spsann results |
Reference manual: | spsann.pdf |
Vignettes: |
spsann: Optimization of Sample Configurations Using Spatial Simulated Annealing |
Package source: | spsann_2.2.0.tar.gz |
Windows binaries: | r-devel: spsann_2.2.0.zip, r-release: spsann_2.2.0.zip, r-oldrel: spsann_2.2.0.zip |
macOS binaries: | r-release: spsann_2.2.0.tgz, r-oldrel: spsann_2.2.0.tgz |
Old sources: | spsann archive |
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