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
|
| 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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