def find_acc_cut_point_deltas(native, loan):
    """
    Calculate cut_point that will give highest accuracy for entropy deltas.
    Use case is for differences between entropies calculated under competing models,
    where the native model typically estimates lower entropies for native words,
    and the loan model typically estimates lower entropies for loan words.

    Parameters
    ----------
    native : [float]
        Delta entropies for native words calculated under native and loan models.
        native = delta = entropy_{native_word|native_model} - entropy_{native_word|loan_model}
    loan : [float]
        Delta entropies for loan words calculated under native and loan models.
        loan = delta = entropy_{loan_word|native_model} - entropy_{loan_word|loan_model}

    Returns
    -------
    cut_point : float
        Critical value to test native versus loan word model deltas.
        Native if delta <= cut_pont else loan.
    """
    native = [(delta, 0) for delta in native]
    loan = [(delta, 1) for delta in loan]
    data = sorted(native + loan, key=lambda row: row[0])
    # All loan deltas > -inf so correct. No native < -inf so incorrect.
    cut_point = float("-inf")
    correct = len(loan)
    correct_max = correct

    for value, source in data:
        # Count native correct if cut_point were set here.
        # Count loan wrong if cut_point were set here.
        correct += 1 if source == 0 else -1
        if correct > correct_max:
            correct_max = correct
            cut_point = value

    return cut_point