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_sc.m
    path of file: ./repos/ExploreASL/External/SPMmodified
    
    the code of the file until where you have to start completion: function [C,h,Ph,F,Fa,Fc,Eh,Ch,hE,hC] = spm_reml_sc(YY,X,Q,N,hE,hC,V)
% ReML estimation of covariance components from y*y' - proper components
% FORMAT [C,h,Ph,F,Fa,Fc,Eh,Ch,hE,hC] = spm_reml_sc(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
% N   - number of samples
%
% hE  - hyperprior expectation in log-space [default = -32]
% hC  - hyperprior covariance  in log-space [default = 256]
% V   - fixed covariance component
%
% C   - (m x m) estimated errors = h(1)*Q{1} + h(2)*Q{2} + ...
% h   - (q x 1) ReML hyperparameters h
% Ph  - (q x q) conditional precision of log(h)
%
% hE  - prior expectation of log scale parameters
% hC  - prior covariances of log scale parameters
% Eh  - posterior expectation of log scale parameters
% Ch  - posterior covariances of log scale parameters
%
% 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 log-normal hyperpriors.
% See also spm_reml for an unconstrained version that allows for negative
% hyperparameters.
%
%__________________________________________________________________________
%
% SPM ReML routines:

    
function [C,h,Ph,F,Fa,Fc,Eh,Ch,hE,hC] = spm_reml_sc(YY,X,Q,N,hE,hC,V)
% ReML estimation of covariance components from y*y' - proper components
% FORMAT [C,h,Ph,F,Fa,Fc,Eh,Ch,hE,hC] = spm_reml_sc(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
% N   - number of samples
%
% hE  - hyperprior expectation in log-space [default = -32]
% hC  - hyperprior covariance  in log-space [default = 256]
% V   - fixed covariance component
%
% C   - (m x m) estimated errors = h(1)*Q{1} + h(2)*Q{2} + ...
% h   - (q x 1) ReML hyperparameters h
% Ph  - (q x q) conditional precision of log(h)
%
% hE  - prior expectation of log scale parameters
% hC  - prior covariances of log scale parameters
% Eh  - posterior expectation of log scale parameters
% Ch  - posterior covariances of log scale parameters
%
% 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 log-normal hyperpriors.
% See also spm_reml for an unconstrained version that allows for negative
% hyperparameters.
%
%__________________________________________________________________________
%
% 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)
%
%__________________________________________________________________________
% Copyright (C) 2007-2017 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_reml_sc.m 7192 2017-10-18 14:59:01Z guillaume $

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