def params_transform_univariate(params, cov_params, link=None, transform=None,
                     row_labels=None):

    from statsmodels.genmod.families import links
    if link is None and transform is None:
        link = links.Log()

    if row_labels is None and hasattr(params, 'index'):
        row_labels = params.index

    params = np.asarray(params)

    predicted_mean = link.inverse(params)
    link_deriv = link.inverse_deriv(params)
    var_pred_mean = link_deriv**2 * np.diag(cov_params)
    # TODO: do we want covariance also, or just var/se

    dist = stats.norm

    # TODO: need ci for linear prediction, method of `lin_pred
    linpred = PredictionResults(params, np.diag(cov_params), dist=dist,
                             row_labels=row_labels, link=links.identity())

    res = PredictionResults(predicted_mean, var_pred_mean, dist=dist,
                             row_labels=row_labels, linpred=linpred, link=link)

    return res
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