By Sam Berens (

Click here to download release R02.00

Highlights of this release

Estimates a mixture model for circularly distributed data. Components within the model can include a uniform distribution and arbitrarily many target/fixed position von Mises distributions.

Estimates a circular mixture model using Expectation-Maximization (EM).

Estimates a mixture model for circularly distributed accuracy data using a hard clustering method. At present, this implementation is only setup to fit one target distribution and one uniform component.

Computes various statistics for a sample of angles in radians ("Thetas"). If optional "Weights" argument is supplied, angles are weighted to have varying influences on the statistics.

Computes the information content (I) of a von Mises distribution with a prior weight of P and a concentration parameter of K.

Converts the von Mises concentration parameter (K) to a resultant (mean) vector length R.

Returns kernel density estimates for a sample of circularly distributed data in "P", evaluated at the angles in "Theta". "K" is a kappa value for the von Mises distribution. Here, it defines the kernel bandwidth of the that spreads the density function around each point in "P".