  Networks representing many complex systems in nature and society share some
common structural properties like heterogeneous degree distributions and strong
clustering. Recent research on network geometry has shown that those real
networks can be adequately modeled as random geometric graphs in hyperbolic
spaces. In this paper, we present a computer program to generate such graphs.
Besides real-world-like networks, the program can generate random graphs from
other well-known graph ensembles, such as the soft configuration model, random
geometric graphs on a circle, or Erd\H{o}s-R\'enyi random graphs. The
simulations show a good match between the expected values of different network
structural properties and the corresponding empirical values measured in
generated graphs, confirming the accurate behavior of the program.
