EDAForge vs KRLS

KRLS fits kernel regularized least squares, a method whose exact form requires an n-by-n kernel matrix and therefore stops being usable well before modern sample sizes. Three releases shipped within 33 minutes of each other addressed exactly that: a Nystrom approximation mode with conditional approximate inference, kmeans landmark selection with an accessor for reusing landmarks across fits, and GCV as an alternative to leave-one-out for choosing lambda. The default path remains the exact one, and existing calls are unchanged.

The package is being modernized on two tracks that reinforce each other. The interface track — a formula method, broom extractors, autoplot, summary and glance diagnostics — makes the estimator fit contemporary R workflows without touching the algorithm, and the notes are explicit that existing matrix-interface calls remain bit-identical. The performance track removes the reasons it could not be run at all: the Nystrom mode for the kernel matrix, and an average-marginal-effects variance computation rewritten via a row-sum identity to quadratic per-predictor cost. Everything is added as opt-in, which suggests the goal is reaching new users without disturbing replication of published results.

With approximation, landmark reuse, and a second lambda criterion now in place, the remaining gap is guidance on when to trust them; the scaling vignette shipped alongside GCV points to more empirical validation rather than new estimation machinery.