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: PALA_Stat_multi.m
    path of file: ./repos/PALA/PALA/PALA_addons/PALA_functions
    
    the code of the file until where you have to start completion: function [ErrList_out,FinalPairs_out,MissingPoint_out,WrongLoc_out,Stat_class] = PALA_Stat_multi(myfilepath,Track_in,listAlgo,PData,Threshold_pairing,Threshold_TruePos)
%% function [ErrList_out,FinalPairs_out,MissingPoint_out,WrongLoc_out,Stat_class] = PALA_Stat_multi(myfilepath,Track_in,listAlgo,PData,Threshold_pairing,Threshold_TruePos)
% This function runs the PairingAlgorithm for all blocs of IQ. It requires all tracks and

    
function [ErrList_out,FinalPairs_out,MissingPoint_out,WrongLoc_out,Stat_class] = PALA_Stat_multi(myfilepath,Track_in,listAlgo,PData,Threshold_pairing,Threshold_TruePos)
%% function [ErrList_out,FinalPairs_out,MissingPoint_out,WrongLoc_out,Stat_class] = PALA_Stat_multi(myfilepath,Track_in,listAlgo,PData,Threshold_pairing,Threshold_TruePos)
% This function runs the PairingAlgorithm for all blocs of IQ. It requires all tracks and
% needs to load raw 'ListPos' from simulation.
% INPUTS:
%       - myfilepath: filepath of simulated IQ that contains ListPos
%       - Track_in: list of tracks for each algorithm and bloc of IQ. (non interpolated tracks !)
%       - listAlgo: list of used ULM algorithms
%       - PData: parameters of IQ.
%       - Threshold_pairing: max distance between reel and detected point to consider a pair assignee. (\lambda/2)
%       - Threshold_TruePos: max distance between reel and detected point to consider a true positive localization. (\lambda/4)
% OUTPUTS:
%       - ErrList_out: for each algorithm, list of localization [dz,dx,norm(dz,dx)]
%       - FinalPairs_out: for each algorithm, list of pairs (index in ref, index indetection, RMSE)
%       - MissingPoint_out: for each algorithm, index of missing points in ListPos
%       - WrongLoc_out: for each algorithm, index of wrong detections in MatTrackedLoc_algo
%       - Stat_class: [Npos_in x Npos_loc x T_pos x F_neg x F_pos x Jaccard]
%
% Created by Arthur Chavignon 9/12/2019
%
% DATE 2020.08.10 - VERSION 1.1
% AUTHORS: Arthur Chavignon, Baptiste Heiles, Vincent Hingot. CNRS, Sorbonne Universite, INSERM.
% Laboratoire d'Imagerie Biomedicale, Team PPM. 15 rue de l'Ecole de Medecine, 75006, Paris
% Code Available under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (see https://creativecommons.org/licenses/by-nc-sa/4.0/)
% ACADEMIC REFERENCES TO BE CITED
% Details of the code in the article by Heiles, Chavignon, Hingot, Lopez, Teston and Couture.  
% Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy, Nature Biomedical Engineering, 2021.
% General description of super-resolution in: Couture et al., Ultrasound localization microscopy and super-resolution: A state of the art, IEEE UFFC 2018

ErrList = {}; FinalPairs = {}; MissingPoint = {};WrongLoc = {}; Dist2Mean = {};

parfor hhh = 1:size(Track_in,1) % can be replace by a for loop.
    %% For each bloc of data
    fprintf([num2str(hhh) ' '])
    temp = load([myfilepath '_IQ' num2str(hhh,'%03.0f') '.mat'],'ListPos');

    ErrList_i = {}; FinalPairs_i = {}; MissingPoint_i = {}; WrongLoc_i = {};

    for ialgo = 1:numel(listAlgo)
        MatTrackedLoc_algo = cell2mat(Track_in{hhh,ialgo}); % [z,x,idframe] in lambda
        [Stat_class_i,ErrList_i{ialgo},FinalPairs_i{ialgo},MissingPoint_i{ialgo},WrongLoc_i{ialgo}] = PALA_PairingAlgorithm(temp.ListPos,MatTrackedLoc_algo,PData,Threshold_pairing,Threshold_TruePos);
        % Stat_classification : [Npos_in x Npos_loc x T_pos x F_neg x F_pos]
        Stat_classification{hhh}(:,ialgo) = sum(Stat_class_i,1);

    end