Network generation parameters are to be optimised to produce spatial networks that match a given degree distribution (discrete: negative binomial) and a given continuous distance distribution (geographic distances to network neighbours: weibull). I need a possibly normalised score [0,1] to measure the distance between the empiric distributions of produced networks and given distribution functions.
I finally opted for the integral-based Hellinger distance since it can be easily scaled to [0,1] and is suitable for discrete and continuous distributions (also the
distrEx provides an implementation). A valuable resource regarding the topic is a paper by Gibbs, A. & Su, F. 2002.