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 list of dissimilarity matrices
boolean flag indicating if the transformation should include the scaling operation
a two column vector with the coordinates of the group configuration
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