  A method is discussed that allows combining sets of differential or inclusive
measurements. It is assumed that at least one measurement was obtained with
simultaneously fitting a set of nuisance parameters, representing sources of
systematic uncertainties. As a result of beneficial constraints from the data
all such fitted parameters are correlated among each other. The best approach
for a combination of these measurements would be the maximisation of a combined
likelihood, for which the full fit model of each measurement and the original
data are required. However, only in rare cases this information is publicly
available. In absence of this information most commonly used combination
methods are not able to account for these correlations between uncertainties,
which can lead to severe biases as shown in this article. The method discussed
here provides a solution for this problem. It relies on the public result and
its covariance or Hessian, only, and is validated against the
combined-likelihood approach. A dedicated software package implementing this
method is also presented. It provides a text-based user interface alongside a
C++ interface. The latter also interfaces to ROOT classes for simple
combination of binned measurements such as differential cross sections.
