Improvements to inverse_covariance

• New RepeatedKFold cross-validation class which generates multiple re-shuffled k-fold datasets. This technique is now used by default in QuicGraphLassoCV. Read about the new options here: https://github.com/skggm/skggm/blob/0.2.0/inverse_covariance/quic_graph_lasso.py#L402-L410

Major update to the inverse_covariance.profiling submodule

Includes new initial tools for profiling methods. Specifically:

1. MonteCarloProfile: A workshop to measure the performance of an estimator on multivariate normal samples, given a graph generator (that generates covariance, precision, and adjacency matrices), and a set of metrics to compute in each trial.
2. Graph: Base class and utilities to build common sparse graphs
3. Specific graph generator classes: LatticeGraph, ClusterGraph, and ErdosRenyiGraph,
4. Set of common metrics for profiling in inverse_covariance.profiling.metrics

An example usage can be found in examples/profiling_example.py or in inverse_covariance/profiling/tests.