This also improves some of the existing shuffling strategies with a fast
alias sampling. The parameter names of the random_graph() and
random_rewire() functions have been made more consistent.

The object may be globally reset via the seed_rng() function.

This makes the code more efficient if the input parameter combinations
are much larger than the graph itself.

This changes stlightly the information passed to the auxiliary functions
provided by the user, so that more arbitrary structures can be
constructed more easily.

Images are now converted to png a posteriori for html output, but are
used as pdf for latex.

This fixes a bug in the "correlated" mode in random_rewire().

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.