data transformation singular value decomposition Philippe Rocca-Serra a data transformation which compute the singular-value decomposition of a rectangular matrix. The singular-value decomposition is very general in the sense that it can be applied to any m × n matrix whereas eigenvalue decomposition can only be applied to certain classes of square matrices. adapted from wikipedia: https://en.wikipedia.org/wiki/Singular-value_decomposition last accessed: 24/08/2018 term request from Ralf Weber and Gavin Lloyd, University of Birmingham numpy.linalg.svd(a, full_matrices=True, compute_uv=True) https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.linalg.svd.html svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE) https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/svd ready for release