src/estimagic/inference/ml_covs.py
def se_from_cov(cov):
    """Standard deviation of parameter estimates based on the function of choice.

    Args:
        cov (numpy.ndarray): Covariance matrix

    Returns:
        standard_errors (numpy.ndarray): 1d array with standard errors

    """
    standard_errors = np.sqrt(np.diag(cov))

    if isinstance(cov, pd.DataFrame):
        standard_errors = pd.Series(standard_errors, index=cov.index)

    return standard_errors
Standard deviation of parameter estimates based on the function of choice.

    Args:
        cov (numpy.ndarray): Covariance matrix

    Returns:
        standard_errors (numpy.ndarray): 1d array with standard errors

    
Standard deviation of parameter estimates based on the function of choice.
Standard deviation of parameter estimates based on the function of choice .
def se_from_cov(cov):
    standard_errors = np.sqrt(np.diag(cov))
    if isinstance(cov, pd.DataFrame):
        standard_errors = pd.Series(standard_errors, index=cov.index)
    return standard_errors
def se_from_cov ( cov ) : standard_errors = np . sqrt ( np . diag ( cov ) ) if isinstance ( cov , pd . DataFrame ) : standard_errors = pd . Series ( standard_errors , index = cov . index ) return standard_errors
Standard deviation of parameter estimates based on the function of choice.
Standard deviation of parameter estimates based on the function of choice .
"""Standard deviation of parameter estimates based on the function of choice.

    Args:
        cov (numpy.ndarray): Covariance matrix

    Returns:
        standard_errors (numpy.ndarray): 1d array with standard errors

    """
standard_errors (numpy.ndarray): 1d array with standard errors
standard_errors ( numpy . ndarray ) : 1d array with standard errors
cov
None