summary

SuperLSS.summary(

*,

covariance: Literal['fixed', 'corrected'] = 'fixed',

X_train: object | EagerFrame | None = None,

) → DataFrame

Return one row per intercept and per term of every parameter.

The columns are the term’s effective degrees of freedom, its smoothing parameter where it has exactly one, the Wood (2013) statistic with its rank and p-value, and the estimate and standard error where the term holds a single coefficient. It reads the training frame the same way term_test() does, so a restored model needs X_train=.