skggm 0.2.7: A scikit-learn compatible package for Gaussian and related Graphical Models

In the last decade, learning networks that encode conditional independence relationships has become an important problem in machine learning and statistics. For many important probability distributions, such as multivariate Gaussians, this amounts to estimation of inverse covariance matrices. Inverse covariance estimation is now used widely in infer gene regulatory networks in systems biology and neural interactions in the neuroscience. However, many statistical advances and best practices in fitting such models to data are not yet widely adopted and not available in common python packages for machine learning. Furthermore, inverse covariance estimation is an active area of research where researchers continue to improve algorithms and estimators. With skggm we seek to provide these new developments to a wider audience, and also enable researchers to effectively benchmark their methods in regimes relevant to their applications of interest. While skggm is currently geared toward Gaussian graphical models, we hope to eventually evolve it to support General graphical models. Read more [here](https://skggm.github.io/skggm/tour) New in this version: python3 support Adds alternatives to np.corrcoef and np.cov to initialize sample covariance that employ rank correlation estimators like spearman rank correlation and kendall's tau concordance correlation Config for Travis continuous integration testing on rep