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.

Version: 2.1-0
Imports: methods, pedometrics, Rcpp, sp, SpatialTools
LinkingTo: Rcpp
Suggests: gstat, tcltk, knitr
Published: 2017-06-23
Author: Alessandro Samuel-Rosa [aut, cre], Lucia Helena Cunha dos Anjos [ths], Gustavo de Mattos Vasques [ths], Gerard B M Heuvelink [ths], Edzer Pebesma [ctb], Jon Skoien [ctb], Joshua French [ctb], Pierre Roudier [ctb], Dick Brus [ctb], Murray Lark [ctb]
Maintainer: Alessandro Samuel-Rosa <alessandrosamuelrosa at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: spsann results


Reference manual: spsann.pdf
Vignettes: spsann: Optimization of Sample Configurations Using Spatial Simulated Annealing
Package source: spsann_2.1-0.tar.gz
Windows binaries: r-devel: spsann_2.0-0.zip, r-release: spsann_2.0-0.zip, r-oldrel: spsann_2.1-0.zip
OS X El Capitan binaries: r-release: spsann_2.0-0.tgz
OS X Mavericks binaries: r-oldrel: spsann_2.1-0.tgz
Old sources: spsann archive


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