2.2.0 (minor release)

The new version of the spsann package includes some bug fixes and a few modifications. Users now can
choose how optimCLHS computes objective function values: as in the original paper or as in the FORTRAN
implementation. Users now also must inform the weights passed to optimCLHS as to guarantee that s/he is
aware of what s/he is doing. The same apples to other functions that deal with multi-objective optimization
problems: optimACDC and optimSPAM. Another important modification in the current version of spsann is
the possibility to use a finite set of candidate locations by setting cellsize = 0. This is useful when
optimizing sample points only in the feature space and should reduce the computation time needed to find the
solution.

2.0-0

This is a major release of package _spsann_ that includes several conceptual changes. Despite our efforts, it was not possible to guarantee the compatibility with previous versions. We have decided not to deprecate functions and function arguments because (1) this would require deprecating a lot of code and (2) you should first read the updated package documentation to understand the conceptual changes that we have made before you start using it. This is a summary of the changes:

• A completely new annealing schedule was implemented. The reason for this modification is that the former annealing schedule showed to be inefficient during our tests. The new annealing schedule is the very simple and most-used schedule proposed by Kirkpatrick et al. (1983). We have also replaced the acceptance criterion with the well-known Metropolis criterion. This new implementation showed to be more efficient in our tests than our early implementation. Setting up this new annealing schedule is done using the new function scheduleSPSANN.
• A more elegant solution to jitter the sample points was implemented. It consists of using a finite set of candidate locations that are seen by the algorithm as the centre of grid cells. In the first stage, we select a grid cell with replacement. In the second stage, we select a location within that grid cell using simple random sampling. This guarantees that any location in the sampling region is a candidate location for the jittered sample point.
• Solving multi-objective combinatorial optimization problems (MOCOP) has become easier with the creation of the new function minmaxPareto(). This function computes the Pareto maximum and minimum values of the objective functions that compose the MOCOP needed to scale the objective functions to the same approximate
  range of values.
• The user can now chose to follow the progress of the optimization using a text progress bar in the R console
  or a Tk progress bar widget. A Tk progress bar widget is useful when running _spsann_ in parallel
  processors.
• The output of the optimization is now stored in an object of class OptimizedSampleConfiguration. This
  object contains three slots. The first (points) holds the coordinates of the optimized sample
  configuration. The second, spsann, stores information about the settings used with the spatial simulated
  annealing algorithm. The third, objective, holds the settings used with the chosen objective function.
  Methods were implemented to retrieve information from the new class, as well as producing plots of the
  optimized sample configuration.
• Package documentation was expanded and adapted to cope with the conceptual changes that were made. It also
  includes a vignette that gives a short description of the package and its structure, as well as presents
  a few examples on how to use the package. It is strongly recommended to read the new package documentation
  and the accompanying vignette before you start using the package.
• Finally, bugs were fixed, warning messages were improved, and a faster code was implemented whenever
  possible.

1.0.1

updated documentation