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` XLW C++ example: LPXLFOPER EXCEL_EXPORT xlStats(LPXLFOPER inTargetRange) { EXCEL_BEGIN; XlfOper xlTargetRange(inTargetRange);

    // Temporary variables.
    double averageTmp = 0.0;
    double varianceTmp = 0.0;

    // Iterate over the cells in the incoming matrix.
    for (RW i = 0; i < xlTargetRange.rows(); ++i)
    {
        for (RW j = 0; j < xlTargetRange.columns(); ++j)
        {
            // sums the values.
            double value(xlTargetRange(i,j).AsDouble());
            averageTmp += value;
            // sums the squared values.
            varianceTmp += value * value;
        }
    }
    size_t popSize = xlTargetRange.rows() * xlTargetRange.columns();

    // avoid divide by zero
    if(popSize == 0)
    {
        THROW_XLW("Can't calculate stats on empty range");
    }

    // Initialization of the results Array oper.
    XlfOper result(1, 2);
    // compute average.
    double average = averageTmp / popSize;
    result(0, 0) = average;
    // compute variance
    result(0, 1) = varianceTmp / popSize - average * average;
    return result;
    EXCEL_END;
}

`

in D: ` import std.algorithm:map,sum; import std.range:front;

@Register(ArgumentText("input range to calculate statistics for"),
    HelpTopic("excel-d"),
    FunctionHelp("calculates mean and variance for input array"),
    ArgumentHelp(["input range to calculate statistics for"]))
auto xlStats(double[][] inTargetRange)
{
    auto numCells = (inTargetRange.length > 0) ?
                                 inTargetRange.length * inTargetRange.front.length : 0;
    auto means = inTargetRange.map!(row => row.sum).sum / numCells;
    auto sumSquares = inTargetRange.map!( row => row.map!(cell => cell*cell).sum).sum;
    return [means, sumSquares / numCells - means];
}

`