You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_reml_A.m
    path of file: ./repos/spm
    
    the code of the file until where you have to start completion: function  [C,h,Ph,F,Fa,Fc] = spm_reml_A(YY,X,Q,N,hE,hC,V)
% ReML estimation of covariance components from y*y' - factored components
% FORMAT [C,h,Ph,F,Fa,Fc] = spm_reml_A(YY,X,Q,N,[hE,hC,V])
%
% YY  - (m x m) sample covariance matrix Y*Y'  {Y = (m x N) data matrix}
% X   - (m x p) design matrix
% Q   - {1 x q} covariance components (factors)
% N   - number of samples
%
% hE  - hyperprior expectation [default = 0]
% hC  - hyperprior covariance  [default = 256]
% V   - fixed covariance component
%
% C   - (m x m) estimated errors: C = A*A': A = h(1)*Q{1} + h(2)*Q{2} + ...
% h   - (q x 1) ReML hyperparameters h
% Ph  - (q x q) conditional precision of h
%
% F   - [-ve] free energy F = log evidence = p(Y|X,Q) = ReML objective
%
% Fa  - accuracy
% Fc  - complexity (F = Fa - Fc)
%
% Performs a Fisher-Scoring ascent on F to find MAP variance parameter
% estimates. NB: uses weakly informative normal hyperpriors on the factors.
%
%__________________________________________________________________________
%
% SPM ReML routines:

    
function  [C,h,Ph,F,Fa,Fc] = spm_reml_A(YY,X,Q,N,hE,hC,V)
% ReML estimation of covariance components from y*y' - factored components
% FORMAT [C,h,Ph,F,Fa,Fc] = spm_reml_A(YY,X,Q,N,[hE,hC,V])
%
% YY  - (m x m) sample covariance matrix Y*Y'  {Y = (m x N) data matrix}
% X   - (m x p) design matrix
% Q   - {1 x q} covariance components (factors)
% N   - number of samples
%
% hE  - hyperprior expectation [default = 0]
% hC  - hyperprior covariance  [default = 256]
% V   - fixed covariance component
%
% C   - (m x m) estimated errors: C = A*A': A = h(1)*Q{1} + h(2)*Q{2} + ...
% h   - (q x 1) ReML hyperparameters h
% Ph  - (q x q) conditional precision of h
%
% F   - [-ve] free energy F = log evidence = p(Y|X,Q) = ReML objective
%
% Fa  - accuracy
% Fc  - complexity (F = Fa - Fc)
%
% Performs a Fisher-Scoring ascent on F to find MAP variance parameter
% estimates. NB: uses weakly informative normal hyperpriors on the factors.
%
%__________________________________________________________________________
%
% SPM ReML routines:
%
%      spm_reml:    no positivity constraints on covariance parameters
%      spm_reml_sc: positivity constraints on covariance parameters
%      spm_sp_reml: for sparse patterns (c.f., ARD)
%
%__________________________________________________________________________

% Karl Friston
% Copyright (C) 2010-2022 Wellcome Centre for Human Neuroimaging


% assume a single sample if not specified
%--------------------------------------------------------------------------
try, N; catch, N  = 1;      end