Marc, thanks, you are right.
I generated a von mises distribution with the mu and kappa estimated from my angles, say x, i.e.:
[mu kappa] = circ_vmpar(x)
and then
vonmis = circ_randvm(mu,kappa,length(x))

Then I use the kuiper test to see whether the two distribution x and vonmis differ significantly (the difference can be in any property, such as mean, location and dispersion):
[H,pValue] = circ_kuipertest(x, vonmis)

However I was wondering if it is possible to have more accurate p-value estimates in the Kuiper test, as already asked by another user before.

Dear Mark,
thanks for your tip, however I'm not really convinced.
Both the circ_ktest and the circ_kuipertest are not described in the pdf:
http://www.jstatsoft.org/v31/i10/paper

Anyway, circ_ktest is a parametric two-sample test to determine whether two concentration parameters are different.
The circ_kuipertest is a two-sample test which allow to test whether two input samples differ significantly. The difference can be in any property, such as mean location and dispersion. It is a circular analogue of the Kolmogorov-Smirnov test.

I do not understand how these tests could help me with a goodness-of-fit test for the Von Mises-Fisher distribution, but probably is my limit.

Hi guys, I'm new to circular statistics and I've downloaded this package.

Given some vectors, I'd like to test if they are distributed following a Von Mises-Fisher distribution.
Do you know what instructions of the package I should use?
Can you help?