v0.1.2 -- Saving, loading, and base_predictors

• Include a base_predictor in your contextualized.regression or contextualized.easy object to improve learning and restrict contextualized to estimate only non-base effects (differences in parameters/outcomes from the base model)
• Easily share your results and transfer your experiments between machines with contextualized.save and contextualized.load

v0.1.1

• Fixed classifier prediction shapes
• Added classifier demo

v0.1.0 -- Introducing Contextualized

This release introduces tools for estimating the following models with context-specific parameters:

• Linear Regression
• Logistic Regression
• Univariate (scalar) Linear and Logistic Regression
• Correlation
• Directed Acyclic Graphs (DAGs, Bayesian networks)

As well as demos and a preliminary test suite.