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+=====  FT_CONNECTIVITY_PDC =====
+Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_connectivity_pdc".
+<html><pre>
+  <a href=/reference/ft_connectivity_pdc><font color=green>FT_CONNECTIVITY_PDC</font></a> computes partial directed coherence. This function implements
+  the metrices described in Baccala et al., Biological Cybernetics 2001, 84(6),
+-74. and in Baccala et al., 15th Int.Conf.on DSP 2007, 163-66.
+  The implemented algorithm has been tested against the implementation in the
+  SIFT-toolbox. It yields numerically identical results to what is known there as
+  'nPDC' (for PDC) and 'GPDC' for generalized pdc.
+  Use as
+    [p, v, n] = ft_connectivity_pdc(h, key1, value1, ...)
+  The input argument H should be a spectral transfer matrix organized as
+    Nrpt x Nchan x Nchan x Nfreq (x Ntime),
+  where Nrpt can be 1.
+  Additional optional input arguments come as key-value pairs:
+    'hasjack'  = 0 (default) is a boolean specifying whether the input
+                 contains leave-one-outs, required for correct variance
+                 estimate
+    'feedback' = string, determining verbosity (default = 'none'), see <a href=/reference/ft_progress><font color=green>FT_PROGRESS</font></a>
+    'invfun'   = 'inv' (default) or 'pinv', the function used to invert the
+                 transfer matrix to obtain the fourier transform of the
+                 MVAR coefficients. Use 'pinv' if the data are
+                 poorly-conditioned.
+    'noisecov' = matrix containing the covariance of the residuals of the
+                 MVAR model. If this matrix is defined, the function
+                 returns the generalized partial directed coherence.
+  Output arguments:
+    p = partial directed coherence matrix Nchan x Nchan x Nfreq (x Ntime).
+        If multiple observations in the input, the average is returned.
+    v = variance of p across observations.
+    n = number of observations.
+  Typically, nrpt should be 1 (where the spectral transfer matrix is
+  computed across observations. When nrpt&gt;1 and hasjack is true the input
+  is assumed to contain the leave-one-out estimates of H, thus a more
+  reliable estimate of the relevant quantities.
+  See also <a href=/reference/ft_connectivityanalysis><font color=green>FT_CONNECTIVITYANALYSIS</font></a>
+</pre></html>