Functions comment edited to conform to FileExchange style.

Author: asymppdc

README.md

@@ -1,55 +1,54 @@
-%% A_to_f
-%        Calculates A(f), A in the frequency domain.
+function AL = A_to_f(A, nf)
+%A_to_f  Calculates A(f), A in the frequency domain.
 %
-%% Syntax:
+% Syntax:
 %       AL = A_to_f(A, nf)
 %
-%% Input arguments:
+% Input arguments:
 %         A     - (nChannels x nChannels x p) Recurrence matrix,
 %                  where nChannels is the number of signals, and p the model
 %                  order.
 %         nf    - frequency resolution
 %
-%% Output argument:
+% Output argument:
 %         AL    - (nf, nChannels, nChannels) A(f)
 
 % (C) Koichi Sameshima & Luiz A. Baccalá, 2022. 
 % See file license.txt in installation directory for licensing terms.
 
-function AL = A_to_f(A, nf)
 
-[nChannels, ~, p] = size(A); % Dummy variable ~ is equivalent to nChannels
-Jimag = sqrt(-1);
+   [nChannels, ~, p] = size(A); % Dummy variable ~ is equivalent to nChannels
+   Jimag = sqrt(-1);
 
-% Variable 'exponents' is an array of FFT exponents, on all frequency range for
-% each lag.
-exponents = reshape((-Jimag*pi*kron(0:(nf-1),(1:p))/nf),p,nf).';
+   % Variable 'exponents' is an array of FFT exponents, on all frequency
+   % range for each lag.
+   exponents = reshape((-Jimag*pi*kron(0:(nf-1),(1:p))/nf),p,nf).';
 
-% Af multiplies the exp(ar) by the matrix A, for all frequencies, the reshape
-% and transpose functions are tricks to make the vector calculation possible.
+   % Af multiplies the exp(ar) by the matrix A, for all frequencies, the reshape
+   % and transpose functions are tricks to make the vector calculation possible.
 
-Areshaped = reshape(A, nChannels,nChannels,1,p);
+   Areshaped = reshape(A, nChannels,nChannels,1,p);
 
-Af = zeros(nChannels,nChannels,nf,p);
-for kk = 1:nf
-   Af(:,:,kk,:) = Areshaped;
-end
+   Af = zeros(nChannels,nChannels,nf,p);
+   for kk = 1:nf
+      Af(:,:,kk,:) = Areshaped;
+   end
 
-for i = 1:nChannels
-   for k = 1:nChannels
-      Af(i,k,:,:) = reshape(Af(i,k,:,:),nf,p).*exp(exponents);
+   for i = 1:nChannels
+      for k = 1:nChannels
+         Af(i,k,:,:) = reshape(Af(i,k,:,:),nf,p).*exp(exponents);
+      end
    end
-end
 
-Af = permute(Af, [3,1,2,4]);
+   Af = permute(Af, [3,1,2,4]);
 
-AL=zeros(nf,nChannels,nChannels);
+   AL=zeros(nf,nChannels,nChannels);
 
-for kk = 1:nf
-   temp = zeros(nChannels,nChannels);
-   for k = 1:p
-      temp = temp+reshape(Af(kk,:,:,k),nChannels,nChannels);
+   for kk = 1:nf
+      temp = zeros(nChannels,nChannels);
+      for k = 1:p
+         temp = temp+reshape(Af(kk,:,:,k),nChannels,nChannels);
+      end
+      temp = eye(nChannels)-temp;
+      AL(kk,:,:) = reshape(temp,1,nChannels,nChannels);
    end
-   temp = eye(nChannels)-temp;
-   AL(kk,:,:) = reshape(temp,1,nChannels,nChannels);
-end

core/A_to_f.m

@@ -1,23 +1,23 @@
-%% ARFITCAPS 
-%        Capsule function to call the arfit.m routine, part of 
-%        "ARfit: Multivariate Autoregressive Model Fitting" package.
+function [pf,A,ef] = arfitcaps(u,IP)
+%ARFITCAPS  Capsule function to call the arfit.m routine, part of 
+%           "ARfit: Multivariate Autoregressive Model Fitting" package.
 %
-%% Syntax:
+% Syntax:
 %      [pf,A,ef] = ARFITCAPS(u,IP)
 %
-%% Input Arguments
+% Input Arguments:
 %   u:          time series
 %   IP:         VAR model order
 %
-%% Output Arguments 
+% Output Arguments: 
 %   pf:         covariance matrix provided by ARFIT routine
 %   A:          AR estimate matrix provided by ARFIT routine
 %   ef:         forward residuals provided by ARRES routine
 %
-%% Description:
-% ARFITCAPS is capsule that calls arfit.m and arres.m routines, part of 
-% Autoregressive Model Fitting" package, which implements algorithms 
-% as described in the following articles:
+% Description:
+%   ARFITCAPS is capsule that calls arfit.m and arres.m routines, part of 
+%   Autoregressive Model Fitting" package, which implements algorithms 
+%   as described in the following articles:
 %
 %    [1] Neumaier A & Schneider T, 2001. Estimation of parameters and
 %         eigenmodes of multivariate autoregressive models. ACM Trans Math
@@ -33,10 +33,11 @@
 %  not tested)
 %        https://www.mathworks.com/matlabcentral/fileexchange/174-arfit,
 %
-%  and, before using it, verify the license terms, it seems to be a copyrighted
+%  and, before using it, verify the license terms. It seems to be a copyrighted
 %  material by the Association for Computing Machinery, Inc.
 %
-%%  Note: As described by the authors, acf.m in ARfit needs Signal Processing 
+%  Notes: 
+%  As described by the authors, acf.m in ARfit needs Signal Processing 
 %  Toolbox (TM), as it requires XCORR, a cross-correlation function estimator.
 %
 %  ARfit availability was checked on August 13, 2015, and August 27, 2021. KS
@@ -45,40 +46,37 @@
 %       www.gps.caltech.edu/~tapio/arfit/index.html, 
 %  which is now obsolete.
 %
-%% See also: ARFIT, MVAR, MCARNS, MCARVM, CMLSM
+% See also: ARFIT, MVAR, MCARNS, MCARVM, CMLSM
 
 % (C) Koichi Sameshima & Luiz A. Baccalá, 2022. 
 % See file license.txt in installation directory for licensing terms.
 
-%%
-
-function [pf,A,ef] = arfitcaps(u,IP)
-
-if ~exist('arfit.m','file')
-   help arfitcaps
-   error('ARfit.m not found. Get the ARfit package from Tapio Schneider''s web site.')
-end;
+   % Check for the existence of ARfit Package
+   if ~exist('arfit.m','file')
+      help arfitcaps
+      error('ARfit.m not found. Get the ARfit package from Tapio Schneider''s web site.')
+   end;
 
-v = u';
-[w, Au, C, sbc, fpe, th] = arfit(v,IP,IP);
-pf = C;
+   v = u';
+   [w, Au, C, sbc, fpe, th] = arfit(v,IP,IP);
+   pf = C;
 
-if IP >= 20
-   [siglev,res] = arres(w,Au,v,IP+1);
-else
-   [siglev,res] = arres(w,Au,v);
-end;
+   if IP >= 20
+      [siglev,res] = arres(w,Au,v,IP+1);
+   else
+      [siglev,res] = arres(w,Au,v);
+   end;
 
-% Variable 'siglev' is not used.
+   % Variable 'siglev' is not used.
 
-ef = res'; 
-A = zeros(length(w),length(w),IP);
-for i = 1:IP
-   A(:,:,i) = Au(:,(i-1)*length(w)+1:i*length(w));
-   wu = ceil(length(ef)*rand(size(w)));
-   if length(ef)<length(v)
-      ef = [ef ef(:,wu(1))];
-   else
-      ef = ef(:,1:length(v));
+   ef = res';
+   A = zeros(length(w),length(w),IP);
+   for i = 1:IP
+      A(:,:,i) = Au(:,(i-1)*length(w)+1:i*length(w));
+      wu = ceil(length(ef)*rand(size(w)));
+      if length(ef)<length(v)
+         ef = [ef ef(:,wu(1))];
+      else
+         ef = ef(:,1:length(v));
+      end
    end
-end

core/arfitcaps.m

@@ -1,13 +1,13 @@
-%% ASYMP_DTF
-%        Compute DTF connectivity measures magnitude, from series j-->i, for
-%        any of three of metrics --- Euclidean, diagonal and information ---
-%        as well as asymptotic statistics from vector autoregressive (VAR)
-%        coefficients in the frequency domain.
+function c = asymp_dtf(u,A,pf,nFreqs,metric,alpha)
+%ASYMP_DTF   Compute DTF connectivity measures magnitude, from series j-->i, for
+%            any of three of metrics --- Euclidean, diagonal and information ---
+%            as well as asymptotic statistics from vector autoregressive (VAR)
+%            coefficients in the frequency domain.
 %
-%% Syntax:
+% Syntax:
 %        c = ASYMP_DTF(u,A,pf,nFreqs,metric,alpha)
 %
-%% Input Arguments:
+% Input Arguments:
 %        u      - multiple row vectors time series
 %        A      - AR estimate matrix obtained via MVAR routine
 %        pf     - covariance matrix provided via MVAR routine
@@ -18,7 +18,7 @@
 %        alpha  - significance level
 %                 if alpha is zero, statistical analysis won't be performed
 %
-%% Output Arguments:
+% Output Arguments:
 %        c struct variable with following fields:
 %        |-- .dtf       - complex DTF estimates
 %        |-- .dtf2      - |DTF|^2 estimates
@@ -37,7 +37,7 @@
 %    or
 %        c.{dtf,dtf2,pvalues,th,ci1,ci2,metric,alpha,p,patdenr,patdfr,SS,coh2}
 %
-%% Description:
+% Description:
 %   Compute all three types of DTF --- Granger influentiability measure and
 %   their allied statistical measures of asymptotic statistics for metric
 %   option:
@@ -46,10 +46,10 @@
 %        * 'diag' - Directed Coherence (DC) or gDTF (generalized);
 %        * 'info' - information DTF.
 %
-%% Example:
+% Example:
 % 
-% Annual sunspot numbers  and the melanoma cases (10^5) in the State of
-% Connecticuts, USA, from 1936 to 1972, given by
+%  Annual sunspot numbers  and the melanoma cases (10^5) in the State of
+%  Connecticuts, USA, from 1936 to 1972, given by
 %
 %   u = [ 40 115 100  80  60  40  23  10  10  25  75 145 130 130  80  65  20 ...
 %         10   5  10  60 190 180 175 120  50  35  20  10  15  30  60 105 105 ...
@@ -87,7 +87,7 @@
 %   xplot(c,flgPrinting,fs,fs/2,chLabels,flgColor,flgScale,flgMax, ...
 %                                                              flgSignifColor);
 %
-%% References:
+% References:
 %   [1] M.J. Kaminski and K.J. Blinowska. A new method of the description of the
 %   information flow in the brain structures. Biol Cybern 65:203--210,1991.
 %   <https://doi.org/10.1007/bf00198091>
@@ -97,14 +97,11 @@
 %   Bio-Med Eng 63:2450--2460, 2016. 
 %   <https://doi.org/10.1109/TBME.2016.2550199>
 %
-%% See also: DTF_ALG, ASYMP_PDC, MVAR, MCARNS, MCARVM, CMLSM, ARFIT
+% See also: DTF_ALG, ASYMP_PDC, MVAR, MCARNS, MCARVM, CMLSM, ARFIT
 
 % (C) Koichi Sameshima & Luiz A. Baccalá, 2022. 
 % See file license.txt in installation directory for licensing terms.
 
-%%
-
-function c = asymp_dtf(u,A,pf,nFreqs,metric,alpha)
 
 if ~(nargin == 6)
    error('ASYMP_DTF requires six input arguments.')