Given a number of (2D) configurations, this function uses a combination of transformations (reflections, rotations, translations and scaling) to find a `consensus' configuration which best matches all the component configurations in a least-squares sense.
GPA(X, scale = TRUE)
a two column vector with the coordinates of the group configuration
a list of dissimilarity matrices
boolean flag indicating if the transformation should include the scaling operation
procrustes