The FastKRR implements its core computational operations in C++ via RcppArmadillo, enabling faster performance than pure R, improved numerical stability, and parallel execution with OpenMP where available. On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with CVST to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three widely used scalable approximations—Nyström, Pivoted Cholesky, and Random Fourier Features—allowing analyses with substantially larger sample sizes than are feasible with exact KRR while retaining strong predictive performance. This combination of a compiled backend and scalable algorithms addresses limitations of packages that rely solely on exact computation, which is often impractical for large n. It also integrates with the tidymodels ecosystem via the parsnip model specification krr_reg, and the S3 method tunable.krr_reg() (exposes tunable parameters to dials/tune); see their help pages for usage.
R/: High-level R functions and user-facing API
src/: C++ sources (kernel computation, fitting, prediction)
This package links against Rcpp and RcppArmadillo (via LinkingTo). It uses CVST, parsnip, and the tidymodels ecosystem through their public R APIs.
Maintainer: Kwan-Young Bak kybak@sungshin.ac.kr (ORCID) (Sungshin Women's University) [copyright holder]
Authors:
Gyeongmin Kim rlarudals0824@gmail.com (Sungshin Women's University)
Seyoung Lee sudang0404@gmail.com (Sungshin Women's University)
Miyoung Jang miyoung9072@gmail.com (Sungshin Women's University)