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: GraphicalLassoPath.m
    path of file: ./repos/gift/GroupICAT/icatb/toolbox/Graphical_Lasso
    
    the code of the file until where you have to start completion: function [wList, thetaList, lambdaList, errors] = GraphicalLassoPath(pop, lambdaList, approximate, verbose, penalDiag, tolThreshold, maxIter)
% [wList, thetaList, lambdaList, errors] = GraphicalLassoPath(pop,
% lambdaList, approximate, verbose, penalDiag, tolThreshold, maxIter)
%
% Computes a list of regularized estimates of covariance matrix and its
% inverse along the regularization path
% Inputs:
%  - pop: the set of samples to be used for covariance estimation in an NxP
%  matrix where N is the number of samples and P is the number of variables
%  - lambdaList(o*): a vector containing different lambda values to be used

    
function [wList, thetaList, lambdaList, errors] = GraphicalLassoPath(pop, lambdaList, approximate, verbose, penalDiag, tolThreshold, maxIter)
% [wList, thetaList, lambdaList, errors] = GraphicalLassoPath(pop,
% lambdaList, approximate, verbose, penalDiag, tolThreshold, maxIter)
%
% Computes a list of regularized estimates of covariance matrix and its
% inverse along the regularization path
% Inputs:
%  - pop: the set of samples to be used for covariance estimation in an NxP
%  matrix where N is the number of samples and P is the number of variables
%  - lambdaList(o*): a vector containing different lambda values to be used
%  along the regularization path. If not specified a list of 10 increasing
%  lambda values will be generated using the entries of empirical
%  covariance matrix
%  - approximate(o): a flag indicating whether to use approximate
%  estimation (Meinhausen-Buhlmann approximation)
%  - verbose(o): a flag indicating whether to output algorithm process
%  - penalDiag(o): a flag indicating whether to penalize diagonal elements
%  of the covariance matrix
%  - tolThreshold(o): the amount of tolerance acceptable for covariance
%  matrix elements before terminating the algorithm
%  - maxIter(o): maximum number of iteration to perform in the algorithm
% *: o indicates optional arguments
% Outputs:
%  - wList: the list of estimations obtained for covariance matrix along
%  the regularization path
%  - thetaList: the list of estimations obtained for inverse covariance
%  matrix along the regularization path
%  - lambdaList: actual lambda values used along the regularization path
%  - errors: a list of error flags indicating the validity of each solution
%  along the regularization path
%
% Code by: Hossein Karshenas (hkarshenas@fi.upm.es)
% Date: 10 Feb 2011

if nargin < 1
    error('Too few input parameters.');
end