function oo_ = prior_analysis(type,arg1,arg2,arg3,options_,M_,oo_,estim_params_)
% oo_ = prior_analysis(type,arg1,arg2,arg3,options_,M_,oo_,estim_params_)
% Inputs
% - type            [string]        type of object to be computed
% - arg1                            first input argument of called function
%                                   (usually variable list)
% - arg2                            second input argument of called function
%                                   (usually variable or shock list)
% - arg3                            first input argument of called function
%                                   (nar or FEVD steps)
% - options_        [structure]     Dynare structure defining global options.
% - M_              [structure]     Dynare structure describing the model.
% - oo_             [structure]     Dynare structure where the results are saved.
% - estim_params_   [structure]     structure storing information about estimated
%                   parameters
% Outputs:
% - oo_             [structure]     Dynare structure where the results are saved.

% Copyright © 2009-2025 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.

info = check_prior_analysis_data(type,M_);
SampleSize = options_.prior_mc;
switch info
    case {0,1,2}
        MaxMegaBytes = options_.MaximumNumberOfMegaBytes;
        drsize = size_of_the_reduced_form_model(oo_.dr);
        if drsize*SampleSize>MaxMegaBytes
            drsave=0;
        else
            drsave=1;
        end
        load([M_.dname '/prior/definition.mat']);
        Prior = dprior(estim_params_, M_, options_.prior_trunc);
        prior_sampler(drsave,M_,Prior,options_,oo_,estim_params_);
        oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_);
    case {4,5}
        oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_);
    case 6
        [ivar,vartan] = get_variables_list(options_,M_);
        nvar = length(ivar);
        oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan);
    otherwise
        error('prior_analysis:: Check_prior_analysis_data gave a meaningless output!')
end



function oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan)
narg1 = 8;
narg2 = 10;
if ~(nargin==narg1 || nargin==narg2)
    error('prior_analysis:: Call to function job is buggy!')
end
switch type
  case 'variance'
    if nargin==narg1
        [nvar,vartan] = ...
            dsge_simulated_theoretical_covariance(SampleSize,arg3,M_,options_,oo_,'prior');
    end
    oo_ = covariance_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
                                 vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_,options_);
  case 'decomposition'
    if nargin==narg1
        [~,vartan] = ...
            dsge_simulated_theoretical_variance_decomposition(SampleSize,M_,options_,oo_,'prior');
    end
    oo_ = variance_decomposition_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
                                             M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_,options_);
    if ~all(diag(M_.H)==0)
        if strmatch(arg1,options_.varobs,'exact')
            observable_name_requested_vars=intersect(vartan,options_.varobs,'stable');
            oo_ = variance_decomposition_ME_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
                [M_.exo_names;'ME'],arg2,observable_name_requested_vars,arg1,options_.mh_conf_sig,oo_,options_);
        end
    end
  case 'correlation'
    if nargin==narg1
        [nvar,vartan] = ...
            dsge_simulated_theoretical_correlation(SampleSize,arg3,M_,options_,oo_,'prior');
    end
    oo_ = correlation_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
          vartan,nvar,arg1,arg2,arg3,options_.mh_conf_sig,oo_,M_,options_);
  case 'conditional decomposition'
    if nargin==narg1
        [~,vartan] = ...
            dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,arg3,M_,options_,oo_,'prior');
    end
    oo_ = conditional_variance_decomposition_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
                                                      arg3,M_.exo_names,arg2,vartan,arg1,options_.mh_conf_sig,oo_,options_);
    if ~all(diag(M_.H)==0)
        if strmatch(arg1,options_.varobs,'exact')
            oo_ = conditional_variance_decomposition_ME_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
                                                              arg3,[M_.exo_names;'ME'],arg2,vartan,arg1,options_.mh_conf_sig,oo_,options_);
        end
    end
  otherwise
    disp('Not yet implemented')
end
