def METHOD_NAME(
    problem: Dict,
    X: np.ndarray,
    Y: np.ndarray,
    num_resamples: int = 100,
    conf_level: float = 0.95,
    print_to_console: bool = False,
    seed: int = None,
    y_resamples: int = None,
    method: str = "all",
) -> Dict:
    """Perform Delta Moment-Independent Analysis on model outputs.
    Returns a dictionary with keys 'delta', 'delta_conf', 'S1', and 'S1_conf'
    (first-order sobol indices), where each entry is a list of size D
    (the number of parameters) containing the indices in the same order as the
    parameter file.
    Notes
    -----
    Compatible with:
        all samplers
    Examples
    --------
        >>> X = latin.sample(problem, 1000)
        >>> Y = Ishigami.evaluate(X)
        >>> Si = delta.analyze(problem, X, Y, print_to_console=True)
    Parameters
    ----------
    problem : dict
        The problem definition
    X: numpy.matrix
        A NumPy matrix containing the model inputs
    Y : numpy.array
        A NumPy array containing the model outputs
    num_resamples : int
        The number of resamples when computing confidence intervals (default 100)
    conf_level : float
        The confidence interval level (default 0.95)
    print_to_console : bool
        Print results directly to console (default False)
    y_resamples : int, optional
        Number of samples to use when resampling (bootstrap) (default None)
    method : {"all", "delta", "sobol"}, optional
        Whether to compute "delta", "sobol" or both ("all") indices (default "all")
    References
    ----------
    1. Borgonovo, E. (2007). "A new uncertainty importance measure."
           Reliability Engineering & System Safety, 92(6):771-784,
           doi:10.1016/j.ress.2006.04.015.
    2. Plischke, E., E. Borgonovo, and C. L. Smith (2013). "Global
           sensitivity measures from given data." European Journal of
           Operational Research, 226(3):536-550, doi:10.1016/j.ejor.2012.11.047.
    """
    if seed:
        np.random.seed(seed)
    D = problem["num_vars"]
    if y_resamples is None:
        y_resamples = Y.size
    if not y_resamples <= Y.size:
        raise ValueError(
            "y_resamples must be less than or equal to the total number of samples"
        )
    if not 0 < conf_level < 1:
        raise RuntimeError("Confidence level must be between 0-1.")
    # equal frequency partition
    exp = 2.0 / (7.0 + np.tanh((1500.0 - y_resamples) / 500.0))
    M = int(np.round(min(int(np.ceil(y_resamples**exp)), 48)))
    m = np.linspace(0, y_resamples, M + 1)
    Ygrid = np.linspace(np.min(Y), np.max(Y), 100)
    keys = ("delta", "delta_conf", "S1", "S1_conf")
    S = ResultDict((k, np.zeros(D)) for k in keys)
    S["names"] = problem["names"]
    try:
        for i in range(D):
            X_i = X[:, i]
            if method in ["all", "delta"]:
                S["delta"][i], S["delta_conf"][i] = bias_reduced_delta(
                    Y, Ygrid, X_i, m, num_resamples, conf_level, y_resamples
                )
            if method in ["all", "sobol"]:
                ind = np.random.randint(Y.size, size=y_resamples)
                S["S1"][i] = sobol_first(Y[ind], X_i[ind], m)
                S["S1_conf"][i] = sobol_first_conf(
                    Y, X_i, m, num_resamples, conf_level, y_resamples
                )
    except np.linalg.LinAlgError as e:
        msg = "Singular matrix detected\n"
        msg += "This may be due to the sample size ({}) being too small\n".format(
            Y.size
        )
        msg += "If this is not the case, check Y values or raise an issue with the\n"
        msg += "SALib team"
        raise np.linalg.LinAlgError(msg) from e
    if print_to_console:
        print(S.to_df())
    return S