spsann: Optimization of Sample Configurations using Spatial Simulated Annealing

Methods to optimize spatial 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.

Install the latest version of this package by entering the following in R:
AuthorAlessandro 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]
Date of publication2016-03-15 06:33:00
MaintainerAlessandro Samuel-Rosa <alessandrosamuelrosa@gmail.com>
LicenseGPL (>= 2)

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ACDC Man page
CLHS Man page
CORR Man page
countPPL Man page
DIST Man page
minmaxPareto Man page
MKV Man page
MSSD Man page
objACDC Man page
objCLHS Man page
objCORR Man page
objDIST Man page
objMKV Man page
objMSSD Man page
objPPL Man page
objSPAN Man page
objSPSANN Man page
optimACDC Man page
optimCLHS Man page
optimCORR Man page
optimDIST Man page
optimMKV Man page
optimMSSD Man page
optimPPL Man page
optimSPAN Man page
optimUSER Man page
plot Man page
plot.OptimizedSampleConfiguration Man page
PPL Man page
scheduleSPSANN Man page
SPAN Man page
spJitter Man page
spsann Man page
spsann-package Man page
SPSANNtools Man page
USER Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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