% This function calculates the effective number of parameters in the model.
function [D,nbetas] = calculateEffectiveParameters(Xinmodel,peinmodel,N,T,lambda)
Npe = length(peinmodel);
nbetas=0;
if isnan(lambda)

   % BIC-derived dimension for each change-point.

    D = log(N); % add 1 dim for intercept term.
    for i=1:Npe
        if containsInteractions(peinmodel{i})
            % the ith predictor in model has change-point.
            % log(sum(Xinmodel(:,i)))/log(N) "effective dimensions" for
            % the slope parameter, and 2 "effective dimensions" for the
            % change-point parameter.
            D = D+log(sum(Xinmodel(:,i+1)))+2*log(N);
            nbetas=nbetas+2;
        else
            % the ith predictor in model is simple linear function.
            nbetas=nbetas+1;
            D = D+log(N);
        end 
    end
    D = D/log(N);
else
    % hard-coded dimension (value=lambda) for each change-point.
    D = Npe+1; % start off with 1 dimension for each slope parameter.
    for i=1:Npe
        if containsInteractions(peinmodel{i})
            % the ith predictor in model has change-point.
            D = D+lambda;
            nbetas=nbetas+2;
        else
            % the ith predictor in model is simple linear function.
            % in this case there is nothing to add.
            nbetas=nbetas+1;
        end 
    end
end


D = T*D;