function [ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V, Fix, Kix, TTx,RRx,CCx] = ...
    kalman_update_engine(a0,a1,P0,P1,t,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_,base_regime,d_index,M_,...
    dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options, Fi,Ki,kalman_tol,nk)
% [ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V, Fix, Kix, TTx,RRx,CCx] = kalman_update_engine(
%                                       a0,a1,P0,P1,t,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_,base_regime,d_index,M_,
%                                       dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options, Fi,Ki,kalman_tol,nk)
% INPUTS
% - a               [N by 1]                t-1's state estimate
% - a1              [N by 2]                state predictions made at t-1:t
% - P               [N by N]                t-1's covariance of states
% - P1              [N by N by 2]           one-step ahead forecast error variance at t-1:t
% - t               [integer]               period
% - data_index      [cell]                  1*2 cell of column vectors of indices.
% - Z               [N_obs ny N]            Selector matrix
% - vv              [N_obs by 2]            prediction error on observables at t-1:t
% - Y               [N_obs by 2]            observations at t-1:t
% - H               [N_obs by 1]            vector of measurement error
% - Qt              [N_exo by N_exo by 3]   covariance matrix of shocks at t-1:t+1
% - T0              [N by N]                base regime state transition matrix
% - R0              [N by N_exo]            base regime shock impact transition matrix
% - TT              [N by N by 2]           state transition matrix at t-1:t (restrict var list)
% - RR              [N by N_exo by 2]       shock impact matrix at t-1:t (restrict var list)
% - CC              [N by 2]                state space constant state transition matrix at t-1:t (restrict var list)
% - regimes_        [struct]                regime info t:t+1 given information up to t-1
% - base_regime     [struct]                base regime info
% - d_index         [integer]               column vector of indices of observables in t
%  ...
% - Fix             [N_obs by 2]            F_i matrix (univariate algo) covariance of observables given t-1 information
% - Kix             [N by N_obs by 2]       Kalman gain matrix (univariate algo) given t-1 information
%
% Outputs
% - ax              [N by 2]                t-1:t updated state estimate
% - a1x             [N by 2]                state predictions made at t-1:t
% - Px              [N by N by 2]           t-1:t updated covariance of states
% - P1x             [N by N by 2]           one-step ahead forecast error variance at t-1:t
% - vx              [N_obs by 2]            prediction error on observables at t-1:t
% - Tx              [N by N by 2]           state transition matrix at t-1:t (restrict var list)
% - Rx              [N by N_exo by 2]       shock impact matrix at t-1:t (restrict var list)
% - Cx              [N by 2]                state space constant state transition matrix at t-1:t (restrict var list)
% - regx            [structure]             updated regime info at t:t+2
% - info            [integer]               error code
% - M_              [structure]             MATLAB's structure describing the model
% - likx            [double]                likelihood
% - etahat:         [N_exo by 2]            smoothed shocks t-1:t|t
% - alphahat:       [N by 2]                smoothed states t-1:t|t
% - V:              [N by N by 2]           smoothed states covariance t-1:t|t
% - Fix             [N_obs by 2]            F_i matrix (univariate algo): covariance of observables
% - Kix             [N by N_obs by 2]       Kalman gain matrix (univariate algo)
% - TTx             [N by N by 2]           state transition matrix at t-1:t (full var list)
% - TRx             [N by N_exo by 2]       shock impact matrix at t-1:t (full var list)
% - CCx             [N by 2]                state space constant state transition matrix at t-1:t (full var list)

% Copyright © 2023-2026 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

is_multivariate = true;
Fix=[];
Kix=[];
TTx=[];
RRx=[];
CCx=[];
V=[];
if nargin>26
    is_multivariate = false;
end


if is_multivariate
    [ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,struct(),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
    [ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, regx, info, M_, likx, alphahat, etahat,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,struct(),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
if info==0
    likvec = likx;
    regvec = regx(1);
else
    likx = inf;
    likvec=[];
end
info0=info;
if info
    if ~isequal(regimes_(1:2),[base_regime base_regime])
        if is_multivariate
            [ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
        else
            [ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, regx, info, M_, likx, alphahat, etahat,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
        end
    end
    if info==0
        likvec = likx;
        regvec = regx(1);
    else
        likx = inf;
    end
    info1=info;
else
    if ~isequal(regimes_(1:2),[base_regime base_regime])
        if is_multivariate
            [ax1, a1x1, Px1, P1x1, vx1, Tx1, Rx1, Cx1, regx1, info1, M_1, likx1, etahat1, alphahat1, V1] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
        else
            [ax1, a1x1, Px1, P1x1, vx1, Fix1, Kix1, Tx1, Rx1, Cx1, regx1, info1, M_1, likx1, alphahat1, etahat1,TTx1,RRx1,CCx1] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
        end
        if info1==0 && not(isequal(regx1,regx))
            likvec = [likvec likx1];
            regvec = [regvec; regx1(1)];
        end
        if info1==0 && likx1<likx
            ax=ax1;
            a1x=a1x1;
            Px=Px1;
            P1x=P1x1;
            vx=vx1;
            Tx=Tx1;
            Rx=Rx1;
            Cx=Cx1;
            regx=regx1;
            info=info1;
            M_= M_1;
            likx=likx1;
            etahat=etahat1;
            alphahat=alphahat1;
            if is_multivariate
                V=V1;
            else
                Fix = Fix1;
                Kix = Kix1;
                TTx = TTx1;
                RRx = RRx1;
                CCx = CCx1;
            end
        end
    else
        if t>options_.occbin.likelihood.number_of_initial_periods_with_extra_regime_guess
            info1=0;
        else
            % may help in first 2 periods to try some other guess regime, due to
            % larger state uncertainty
            info1=1;
            options_.occbin.likelihood.brute_force_regime_guess   = true;
            options_.occbin.likelihood.loss_function_regime_guess = true;
        end

    end
end

if t<options_.occbin.likelihood.first_period_binding_regime_allowed
    % I do not search further since I started guessing with base regime
    return
end


diffstart=0;
if info==0 && length(regvec)==1 % only check this if ONE solution is found
    if M_.occbin.constraint_nbr==1
        oldstart = regimes_(1).regimestart(end);
        newstart = regx(1).regimestart(end);
        diffstart = newstart-oldstart;
        regname = 'regimestart';
        reg_string = 'regime';
    else
        newstart1 = regx(1).regimestart1(end);
        newstart2 = regx(1).regimestart2(end);
        oldstart1 = regimes_(1).regimestart1(end);
        oldstart2 = regimes_(1).regimestart2(end);
        [diffstart, diffregime] = max([newstart1-oldstart1,newstart2-oldstart2]);
        switch diffregime
            case 1
                regname = 'regimestart1';
                reg_string = 'regime1';
            case 2
                regname = 'regimestart2';
                reg_string = 'regime2';
        end
    end
end
if options_.occbin.filter.use_relaxation && diffstart>options_.occbin.filter.use_relaxation_tol_period
    % suffix R denotes relaxation
    guess_regime = [base_regime base_regime];
    options_.occbin.filter.guess_regime = true;
    guess_regime(1) = regx(1);
    regxR= regx;
    while isequal(guess_regime(1),regxR(1))
        % we reduce length until the converged regime does not change
        if regxR(1).(regname)(end) <= 1 %guard against decrementing the regime start period below 1
            break
        end
        guess_regime(1).(regname)(end) = regxR(1).(regname)(end)-1;
        if guess_regime(1).(regname)(end)==1
            % make sure we enforce base regime
            guess_regime(1).(regname)=guess_regime(1).(regname)(end);
            guess_regime(1).(reg_string)=0;
        else
            if guess_regime(1).(regname)(end-1)==guess_regime(1).(regname)(end)
                guess_regime(1).(regname)(end-1) = guess_regime(1).(regname)(end-1)-1;
            end
        end
        if is_multivariate
            [axR, a1xR, PxR, P1xR, vxR, TxR, RxR, CxR, regxR, infoR, M_R, likxR, etahatR, alphahatR, VR] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
        else
            [axR, a1xR, PxR, P1xR, vxR, FixR, KixR, TxR, RxR, CxR, regxR, infoR, M_R, likxR, alphahatR, etahatR,TTxR,RRxR,CCxR] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
        end
        isnew=true;
        for kr=1:length(likvec)
            % make sure likelihood does not differ by rounding issue
            % but due to different regimes
            if isequal(regxR(1),regvec(kr))
                isnew = false;
            end
        end
        if isnew
            likvec = [likvec likxR];
            regvec = [regvec; regxR(1)];
        end
        if infoR==0 && likxR<likx
            ax=axR;
            a1x=a1xR;
            Px=PxR;
            P1x=P1xR;
            vx=vxR;
            Tx=TxR;
            Rx=RxR;
            Cx=CxR;
            regx=regxR;
            info=infoR;
            likx=likxR;
            M_= M_R;
            etahat=etahatR;
            alphahat=alphahatR;
            if is_multivariate
                V=VR;
            else
                Fix = FixR;
                Kix = KixR;
                TTx = TTxR;
                RRx = RRxR;
                CCx = CCxR;
            end
        end
    end
    options_.occbin.filter.guess_regime = false;
    options_.occbin.likelihood.brute_force_regime_guess   = false;
    options_.occbin.likelihood.loss_function_regime_guess = false;
end

if (options_.occbin.likelihood.brute_force_regime_guess && (info0 && info1)) ...
        || (options_.occbin.likelihood.brute_force_extra_regime_guess && (info0 || info1)) %|| (info==0 &&  ~isequal(regx(1),base_regime))
    %suffix 2 denotes brute force
    guess_regime = [base_regime base_regime];
    options_.occbin.filter.guess_regime = true;

    use_index = 0;
    if M_.occbin.constraint_nbr==1
        for k=1:5
            guess_regime(1).regimestart=[1 5 5+4*k];
            guess_regime(1).regime=[0 1 0];
            if is_multivariate
                [ax2{1}, a1x2{1}, Px2{1}, P1x2{1}, vx2{1}, Tx2{1}, Rx2{1}, Cx2{1}, regx2{1}, info2, M_2{1}, likx2{1}, etahat2{1}, alphahat2{1}, V2{1}] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
            else
                [ax2{1}, a1x2{1}, Px2{1}, P1x2{1}, vx2{1}, Fix2{1}, Kix2{1}, Tx2{1}, Rx2{1}, Cx2{1}, regx2{1}, info2, M_2{1}, likx2{1}, alphahat2{1}, etahat2{1},TTx2{1},RRx2{1},CCx2{1}] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
            end
            if info2==0
                use_index= 1;
                if not(info==0 && isequal(regx2{1},regx))
                    % found a solution, different from previous
                    break
                end
            end

            guess_regime(1).regimestart=[1 1+4*k];
            guess_regime(1).regime=[1 0];
            if is_multivariate
                [ax2{2}, a1x2{2}, Px2{2}, P1x2{2}, vx2{2}, Tx2{2}, Rx2{2}, Cx2{2}, regx2{2}, info2, M_2{2}, likx2{2}, etahat2{2}, alphahat2{2}, V2{2}] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
            else
                [ax2{2}, a1x2{2}, Px2{2}, P1x2{2}, vx2{2}, Fix2{2}, Kix2{2}, Tx2{2}, Rx2{2}, Cx2{2}, regx2{2}, info2, M_2{2}, likx2{2}, alphahat2{2}, etahat2{2},TTx2{2},RRx2{2},CCx2{2}] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
            end
            if info2==0
                use_index = 2;
            end

            if use_index
                % in case the second guess does not find a solution!
                info2=0;
                % a solution was found
                break
            end
        end
    end

    if M_.occbin.constraint_nbr==2
        for jk=0:1 % loop over other regime duration. this loop is shorter for parsimony. one may add an option ...
            for k=1:5 % loop over current regime duration
                gindex = 0;
                for jr=1:2 % loop over current regime 1 or 2
                    if jr==1
                        regstart1 = 'regimestart1';
                        reg1 = 'regime1';
                        regstart2 = 'regimestart2';
                        reg2 = 'regime2';
                    else
                        regstart1 = 'regimestart2';
                        reg1 = 'regime2';
                        regstart2 = 'regimestart1';
                        reg2 = 'regime1';
                    end
                    for kk=1:2 % loop over current regime binding in expectation vs binding in current period
                        if kk==1
                            guess_regime(1).(regstart1)=[1 5 5+4*k];
                            guess_regime(1).(reg1)=[0 1 0];
                        else
                            guess_regime(1).(regstart1)=[1 1+4*k];
                            guess_regime(1).(reg1)=[1 0];
                        end
                        for kj=1:1+1*(jk>0)
                            % loop over other regime slack or binding in current period or binding in
                            % expectation
                            if jk==0
                                % other regime is slack
                                guess_regime(1).(regstart2) = 1;
                                guess_regime(1).(reg2) = 0;
                            else % jk>0
                                if kj==1
                                    % other regime binding in current period
                                    guess_regime(1).(regstart2)=[1 1+4*jk];
                                    guess_regime(1).(reg2) = [1 0];
                                else
                                    % other regime binding in expectation
                                    guess_regime(1).(regstart2)=[1 5 5+4*jk];
                                    guess_regime(1).(reg2) = [0 1 0];
                                end
                            end
                            gindex = gindex+1;
                            if is_multivariate
                                [ax2{gindex}, a1x2{gindex}, Px2{gindex}, P1x2{gindex}, vx2{gindex}, Tx2{gindex}, Rx2{gindex}, Cx2{gindex}, regx2{gindex}, info2, M_2{gindex}, likx2{gindex}, etahat2{gindex}, alphahat2{gindex}, V2{gindex}] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
                            else
                                [ax2{gindex}, a1x2{gindex}, Px2{gindex}, P1x2{gindex}, vx2{gindex}, Fix2{gindex}, Kix2{gindex}, Tx2{gindex}, Rx2{gindex}, Cx2{gindex}, regx2{gindex}, info2, M_2{gindex}, likx2{gindex}, alphahat2{gindex}, etahat2{gindex},TTx2{gindex},RRx2{gindex},CCx2{gindex}] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
                            end
                            if info2==0
                                use_index= gindex;
                                if not(info==0 && isequal(regx2{gindex},regx))
                                    % found a solution, different from previous one
                                     break
                                end
                            end
                        end % loop over other regime slack, binding in expectation or binding in current period

                        if info2==0
                            if not(info==0 && isequal(regx2{gindex},regx))
                                % found a solution, different from previous one
                                break
                            end
                        end

                    end % loop over current regime binding in expectation vs binding in current period

                    if info2==0
                        if not(info==0 && isequal(regx2{gindex},regx))
                            % found a solution, different from previous one
                            break
                        end
                    end

                end % loop over current regime 1 or 2

                if use_index
                    info2=0;
                    break
                end
            end % loop over current regime duration

            if use_index
                break
            end
        end % loop over other regime duration
    end % 2 constraints


    if info2==0
        % so that we DO NOT enter IVF step
        info0=0;
        info1=0;
        isnew=true;
        for kr=1:length(likvec)
            % make sure likelihood does not differ by rounding issue
            % but truly for different regimes
            if isequal(regx2{use_index}(1),regvec(kr))
                isnew = false;
            end
        end
        if isnew
            if isempty(likvec)
                regvec = regx2{use_index}(1);
            else
                regvec = [regvec; regx2{use_index}(1)];
            end
            likvec = [likvec likx2{use_index}];
        end
    end
    if info2==0 && likx2{use_index}<likx
        ax=ax2{use_index};
        a1x=a1x2{use_index};
        Px=Px2{use_index};
        P1x=P1x2{use_index};
        vx=vx2{use_index};
        Tx=Tx2{use_index};
        Rx=Rx2{use_index};
        Cx=Cx2{use_index};
        regx=regx2{use_index};
        info=info2;
        M_= M_2{use_index};
        likx=likx2{use_index};
        etahat=etahat2{use_index};
        alphahat=alphahat2{use_index};
        if is_multivariate
            V=V2{use_index};
        else
            Fix = Fix2{use_index};
            Kix = Kix2{use_index};
            TTx = TTx2{use_index};
            RRx = RRx2{use_index};
            CCx = CCx2{use_index};
        end
    end
    options_.occbin.filter.guess_regime = false;
end
if options_.occbin.likelihood.loss_function_regime_guess && (info0 || info1) %|| (info==0 &&  ~isequal(regx(1),base_regime))
    [~, out] = occbin.findmin(d_index, a0, P1, Qt, Y, Z, occbin_options.opts_simul,M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_);
    if out.error_flag==0
        options_.occbin.filter.guess_regime = true;
        guess_regime=out.regime_history;
        guess_regime = [guess_regime base_regime];
        if is_multivariate
            [ax2, a1x2, Px2, P1x2, vx2, Tx2, Rx2, Cx2, regx2, info2, M_2, likx2, etahat2, alphahat2, V2] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
        else
            [ax2, a1x2, Px2, P1x2, vx2, Fix2, Kix2, Tx2, Rx2, Cx2, regx2, info2, M_2, likx2, alphahat2, etahat2,TTx2,RRx2,CCx2] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
        end
        options_.occbin.filter.guess_regime = false;
        isnew=true;
        for kr=1:length(likvec)
            % make sure likelihood does not differ by rounding issue
            % but due to different regimes
            if isequal(regx2(1),regvec(kr))
                isnew = false;
            end
        end
        if isnew
            if isempty(likvec)
                regvec = regx2(1);
            else
                regvec = [regvec; regx2(1)];
            end
            likvec = [likvec likx2];
        end
        if info2==0 && likx2<likx
            ax=ax2;
            a1x=a1x2;
            Px=Px2;
            P1x=P1x2;
            vx=vx2;
            Tx=Tx2;
            Rx=Rx2;
            Cx=Cx2;
            regx=regx2;
            info=info2;
            likx=likx2;
            M_= M_2;
            etahat=etahat2;
            alphahat=alphahat2;
            if is_multivariate
                V=V2;
            else
                Fix = Fix2;
                Kix = Kix2;
                TTx = TTx2;
                RRx = RRx2;
                CCx = CCx2;
            end
        end
    end
end
if length(likvec)>1
    % sum the likelihood of multiple solutions
    likx = -2*log(sum(exp(-(likvec-min(likvec))./2)))+min(likvec);
end

if info(1)==0
    if isnan(likx)
        info = 323;
    elseif any(any(isnan(ax)))
        info = 324;
    end
end

end
