New version of package spsann (2.1-0) -- optimization of sample configurations using spatial simulated annealing

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New version of package spsann (2.1-0) -- optimization of sample configurations using spatial simulated annealing

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Dear all,

I am happy to announce that a new version of the *spsann* package is now
on CRAN.

The *spsann* package has 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. A solution for solving multi-objective optimization
problems are available as well.

Now you can use *spsann* to augment an existing sample configuration,
that is, add new sampling points to a spatial sample configuration
generated using *spsann* or any other means. To do so, when using one of
the functions from the family of optim...() functions, you must pass to
the function argument points an object of class list containing two
named sub-arguments: fixed, a matrix or data frame with the coordinates
of the existing sample configuration -- kept fixed during the
optimization --, and free, the number of sample points that should be
added to the existing sample configuration -- free to move around during
the optimization.

The latest application of the *spsann* package was for the optimization
of the locations of rain-gauges to increase the accuracy of predictions
of rainfall fields in an area north-east of the city of Manchester,
United Kingdom. More information is available in the paper published by
Alexandre Wadoux
<http://www.wur.nl/en/Persons/Alexandre-AMJC-Alexandre-Wadoux-MSc.htm>
and collaborators which you can download from
https://doi.org/10.1016/j.advwatres.2017.06.005.

With best regards,

--

Alessandro Samuel-Rosa

/Estagiário Pós-doutoral (2016-17)
Universidade Federal de Santa Maria

Visite meu website <http://samuel-rosa.github.io>!
/

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