  Random networks are widely used to model complex networks and research their
properties. In order to get a good approximation of complex networks
encountered in various disciplines of science, the ability to tune various
statistical properties of random networks is very important. In this manuscript
we present an algorithm which is able to construct arbitrarily degree-degree
correlated networks with adjustable degree-dependent clustering. We verify the
algorithm by using empirical networks as input and describe additionally a
simple way to fix a degree-dependent clustering function if degree-degree
correlations are given.
