This implements a general "blockmodel" generation / rewiring algorithm,
using Gibbs acceptence / rejection sampling (a.k.a Metropolis-Hastings).

This also implements some optimizations in the rewiring code, which
makes it more efficient on filtered graphs.

This introduces several simplifications and corrections to the graph
rewire algorithm, to guarantee unbiased sampling.

Now a move is outright rejected if it produces a
self-loop/parallel-edge, instead of retried. This also adds a
"non-sweep" mode, where edges are rewired randomly, possibly with
repetition.

The edge moves are now simplified to the target of the edges only,
since swaping sources is redundant.

The number of iterations can now be explicitly modified, so it is not
necessary to call the function more than once, and it is emphasized in
the documentation that only after sufficiently many iterations can the
graph be guaranteed to be fully mixed.

Now, if 'm' is larger than the seed graph, it is set to the size of the
seed graph, and is increased progressively until it matches the desired
value. This removes the need to start with a random seed graph of the
appropriate size.

This also includes modifications which make the code more robust
against parameter choices which don't make much sense, e.g. lead to
negative probabilites.

This fixes a leftover bug from the removal of the 'core' submodule.

Added more DOI links to references, and other cosmetic changes.

Now the meaning of the bin edges given to the several histogram
functions are the same as in numpy/scipy. This means that the last bin
edge represents an upper bound on the last bin.

Vectors are used now instead of unordered_sets, which dramatically
improves performace.

This fixes the expected values for several doctests, after
random_graph() re-factoring.

Now the degree sequence is verified with Erdös-Gallai inequalities, and
the edges are connected in a deterministic fashion. The edges then are
rewired with the random_rewire() function.

This includes also some internal refactoring, and bug fixes.

This extensively modifies the random_rewire() algorithm, so that only
either the source or the edge of each edge is rewired (not both, as
previously), and no parallel edges are created during the algorithm (if
desired).

The new version is much faster, and never gets stuck. However, more than
one run may be necessary in order to obtain a uniform shuffling.

This implements a simpler rewire strategy where the edges are randomly
placed, without preserving the degrees of the vertices.

This also includes the library CGAL as a dependency.

Small fixes of some examples in the docstrings.

This completes all the documentation up to this point.

This returns the union of two graphs, and optionally specified property
maps.

Target degrees were not being sampled correctly from their probability.