function [cleanedTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var)
% removeGroupOutliers removes outliers in y_var within each group defined by fixedVars.
%
%   [cleanedTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var)
%
%   Inputs:
%       dataTable  - Input MATLAB table
%       fixedVars  - Cell array of variable names to group by
%       y_var      - Name of the variable to check for outliers (string or char)
%
%   Outputs:
%       cleanedTable  - Table with outliers removed
%       outliersTable - Table of removed outlier rows

[G, groupKeys] = findgroups(dataTable(:, fixedVars));
keepIdx = true(height(dataTable), 1);
outlierRecords = [];

for groupIdx = 1:height(groupKeys)
    groupRows = (G == groupIdx);
    y_values = dataTable.(y_var)(groupRows);

    % Skip groups with fewer than 3 points
    if sum(groupRows) < 3
        continue;
    end

    % Detect outliers in log10 space (robust for BER, etc.)
    y_log = log10(y_values);
    outlierMask = isoutlier(y_log, 'quartiles', 1);

    if any(outlierMask)
        groupData = dataTable(groupRows, :);
        outlierGroupTable = groupData(outlierMask, :);

        % Optionally, add group key values for traceability
        for k = 1:numel(fixedVars)
            outlierGroupTable.(['Group_', fixedVars{k}]) = repmat(groupKeys{groupIdx, k}, height(outlierGroupTable), 1);
        end

        outlierRecords = [outlierRecords; outlierGroupTable]; %#ok<AGROW>
    end

    % Mark outliers for removal
    groupRowIdx = find(groupRows);
    keepIdx(groupRowIdx(outlierMask)) = false;
end

cleanedTable = dataTable(keepIdx, :);

if isempty(outlierRecords)
    outliersTable = table();
else
    outliersTable = outlierRecords;
end

nRemoved = sum(~keepIdx);
nTotalOriginal = height(dataTable);
fprintf('Removed %d outliers from the data table (%.2f%% of total %d entries).\n', ...
    nRemoved, 100*nRemoved/nTotalOriginal, nTotalOriginal);
end