Covariance regularization #321
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updated setup.py to reflect covar package and other updates
updated version number to be PEP compliant
added covar package
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addresses #312 by ensuring positive semi-definite eigenvalues. The choice of shrinkage factor is a bit of a hack because the number of DOFs in this case was approximated as the number of positive eigenvalues. SSA reconstruction works with a tiny bit of eigenvalue filtering (Kaiser criterion in this case), but for a robust
ssa_interp()method we'll need a more general choice of truncation and shrinkage factor for the covariance matrix. Anyway, it plugs the hole, for now.