How to optimize function parameters?

Hi,

I have a dataset and I want to fit a function to it.
The function is variogram model (http://en.wikipedia.org/wiki/Variogram)
The variogram model is defined by three parameters and I want them to be
automatically optimized for real time data.
I tried to use gafit {gafit} for this, but there are some data
configuration, where optimal results given by gafit() are negative, which is
not correct and cannot be used for further calculating. gafit() does not
enable me to set a range of possible results (at least I did not succeded in
doing so), therefore I am looking for another solution (Powell?s gradient
descent possibly?).

Can anyone give me a hint where to look?

Thanks

Zbynek

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