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.

random_graph() now uses a modified algorithm for generation of
correlated graphs, which is more efficient. Instead of giving a function
which returns a sample of the correlated target degree, the user must
give a function which will just compute its probability. This
probability will then be used to choose the edges.

This modified several docstrings and introduces a quick-start guide.

The seed option is now correctly interpreted.

Reduce duplication of code for loading C++ modules.

Also, get rid of g.underlying_graph(), and rely on "private" variable
g._Graph__graph instead.

The whole histogram code has been redone, and the code has been
simplified. The three-point vertex-edge-vertex correlation has been
scrapped, since it's not frequently used, and would make compilation
even more expensive.

This also adds some missing files to the generation routine.