Description Usage Arguments Note References See Also Examples
pdMean
calculates an (approximate) weighted Karcher or Frechet mean of a sample of
(d,d)dimensional HPD matrices intrinsic to a userspecified metric. In the case of the
affineinvariant Riemannian metric as detailed in e.g., \insertCiteB09pdSpecEst[Chapter 6] or
\insertCitePFA05pdSpecEst, the weighted Karcher mean is either approximated via
the fast recursive algorithm in \insertCiteH13pdSpecEst or computed via the slower, but more accurate,
gradient descent algorithm in \insertCiteP06pdSpecEst. By default, the unweighted Karcher mean is computed.
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M 
a (d,d,S)dimensional array corresponding to a sample of (d,d)dimensional HPD matrices of size S. 
w 
an Sdimensional nonnegative weight vector, such that 
metric 
the distance measure, one of 
grad_desc 
if 
maxit 
maximum number of iterations in gradient descent algorithm, only used if

reltol 
optional tolerance parameter in gradient descent algorithm, only used if

The function does not check for positive definiteness of the input matrices, and (depending on the specified metric) may fail if matrices are close to being singular.
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