MLR

This class represents an estimator which performs Multiple Linear Regression using xcast.linear_regression.

As in linear_regression, the cross-validated error variance is used to produce predictive probability distributions under gaussian assumptions.

Once instantiated, you need to fit mlr on two numpy-arrays, x, and y:

mlr.fit(x, y) 

you can then also make deterministic and probabilistic predictions for new data like X (X1, maybe) as follows:

deterministic_preds = mlr.predict(X1)
tercile_probabilities = mlr.predict_proba(X1) 
nonexceedance_30thquantile = mlr.predict_proba(X1, quantile=0.3) 

Pro Tip: if the standard deviation of any gridpoint in any cross validation window in X is too close to zero, an assertionerror will be thrown. to prevent this, first apply a drymask to your data.