v0.2.5 -- DAG losses, factor graphs, neighborhood selection, and sklearn-style graph baselines

[cnellington]

• Major NOTMAD upgrades: Infer Contextualized DAGs with NOTMAD using the NOTEARS, Poly, or DAGMA losses, and use factor graphs to infer high dimensional DAGs!
• More Contextualized Networks: Use ContextualizedNeighborhoodSelection in the regression module to do contextualized neighborhood selection and infer graphs using lasso regression with minimal assumptions.
• Network Baselines: Use contextualized.baselines to infer traditional correlation networks and Bayesian networks using a simple sklearn-style interface (fit, predict, measure_mses), and create "grouped" versions of these models using any grouping or discrete context (e.g. clustering, feature splits, age groups, cell types) with the GroupedNetworks class -- and follow up with ContextualizedCorrelationNetworks or ContextualizedBayesianNetworks in the easy module to see how much more accurate contextualized models can be!
• Set metamodel_type='Naive' in the regression and dags lightning_modules to remove archetypes and estimate models directly from a neural network (not yet in the sklearn-style easy models).
• Set fit_intercept=False in the regression lightning_modules to remove contextualized intercepts, and only infer models with context-varying coefficients (not yet in the sklearn-style easy models).
• Various bugfixes, including a sign-flip in easy correlation measure_mses.

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This discussion was created from the release v0.2.5 -- DAG losses, factor graphs, neighborhood selection, and sklearn-style graph baselines.