global sensitivity analysis in matlab

main.m
% Define the input parameters and their respective probability distributions
distributions = {'normal', 'uniform', 'beta'};
parameters = {'param1', 'param2', 'param3'};
paramspec = [{1,2}; {0,1}; {1,1.5,2}];

% Define the model (in this case, a simple function)
model = @(x) x(:,1).^2 + x(:,2).^3 + x(:,3).^4;

% Generate a set of input parameter samples using LHS
nsamples = 1000;
samples = lhsdesign(nsamples, numel(parameters));
for i = 1:numel(parameters)
    switch distributions{i}
        case 'normal'
            samples(:,i) = norminv(samples(:,i), 0, paramspec{i}(1));
        case 'uniform'
            samples(:,i) = unifinv(samples(:,i), paramspec{i}(1), paramspec{i}(2));
        case 'beta'
            samples(:,i) = betainv(samples(:,i), paramspec{i}(1), paramspec{i}(2), paramspec{i}(3));
    end
end

% Evaluate the model for each sample
outputs = model(samples);

% Compute the sensitivity indices using the Sobol method
[ST, T] = sensSobol(outputs, samples);
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