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 scalable approximations—Nyström, Pivoted Cholesky, and Random Fourier Features—allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the ‘tidymodels’ ecosystem via the ‘parsnip’ model specification ‘krr_reg’, the S3 method ‘tunable.krr_reg()’, and the direct fitting helper ‘fit_krr()’.
Dependencies: Rcpp, RcppArmadillo, CVST, parsnip
This package uses CVST (GPL ≥ 2). Overall license: GPL (≥ 2).
- Authors:
- Gyeongmin Kim, Sungshin Women’s University, [email protected]
- Seyoung Lee, Sungshin Women’s University, [email protected]
- Miyoung Jang, Sungshin Women’s University, [email protected]
- Kwan-Young Bak, professor at Sungshin Women’s University, [email protected], ORCID:0000-0002-4541-160X
FastKRR is available both on CRAN (stable release) and GitHub (development version).
This installs the latest stable version. On macOS, CRAN binaries are built without OpenMP support. If you need OpenMP, see the note below about installing from source.
# CRAN binary (no OpenMP on macOS)
install.packages("FastKRR")
# CRAN source build (enables OpenMP if configured)
# install.packages("FastKRR", type="source") This installs the development version (always built from source).
# install.packages("pak")
pak::pak("kybak90/FastKRR")This is a basic example of fitting a Gaussian kernel ridge regression
estimator to a dataset
library(FastKRR)
# example data set
set.seed(1)
n = 1000
rho = 1
X = runif(n, 0, 1)
y = sin(2*pi*X^3) + rnorm(n, 0, 0.1)
# model fitting - exact
model_exact = fastkrr(X, y, kernel = "gaussian", rho = rho, opt = "exact", verbose = FALSE)
# model fitting - pivoted
model_pivoted = fastkrr(X, y, kernel = "gaussian", rho = rho, opt = "pivoted", verbose = FALSE)
# model fitting - nystrom
model_nystrom = fastkrr(X, y, kernel = "gaussian", rho = rho, opt = "nystrom", verbose = FALSE)
# model fitting - rff
model_rff = fastkrr(X, y, kernel = "gaussian", rho = rho, opt = "rff", verbose = FALSE)
# prediction
new_n = 500
new_x = matrix(runif(new_n, 0, 1), nrow = new_n)
pred_exact = predict(model_exact, new_x)
pred_pivoted = predict(model_pivoted, new_x)
pred_nystrom = predict(model_nystrom, new_x)
pred_rff = predict(model_rff, new_x)The visualization of the fitted results is shown below.
library(ggplot2)
data = data.frame(new_x)
data$pred_exact = as.numeric(pred_exact)
data$pred_pivoted = as.numeric(pred_pivoted)
data$pred_nystrom = as.numeric(pred_nystrom)
data$pred_rff = as.numeric(pred_rff)
data_train = data.frame(x = X, y = y)
ggplot(data = data) +
geom_line(aes("x" = new_x, "y" = pred_exact, color = 'opt = "exact"'), linewidth=1.2) +
geom_line(aes("x" = new_x, "y" = pred_pivoted, color = 'opt = "pivoted"'), linewidth=1.2) +
geom_line(aes("x" = new_x, "y" = pred_nystrom, color = 'opt = "nystrom"'), linewidth=1.2) +
geom_line(aes("x" = new_x, "y" = pred_rff, color = 'opt = "rff"'), linewidth=1.2) +
geom_point(data = data_train, aes(x = x, y = y), col = "gray", alpha = 0.2) +
theme_minimal()# Ensure Command Line Tools (if not already installed)
xcode-select --install || true
# Install/update Homebrew libomp
brew update
brew install libomp
Copy the block below into ~/.R/Makevars (create the file if it does
not exist).
# OpenMP on macOS with Apple Clang
# Try to detect Homebrew prefix; fallback covers both Apple Silicon & Intel.
HOMEBREW_PREFIX := $(shell brew --prefix 2>/dev/null)
ifeq ($(strip $(HOMEBREW_PREFIX)),)
HOMEBREW_PREFIX := /opt/homebrew
endif
# Include & lib paths (also add Intel path as extra fallback)
CPPFLAGS += -I$(HOMEBREW_PREFIX)/opt/libomp/include -I/usr/local/opt/libomp/include
LDFLAGS += -L$(HOMEBREW_PREFIX)/opt/libomp/lib -L/usr/local/opt/libomp/lib
# Standard R variables for OpenMP
SHLIB_OPENMP_CFLAGS = -Xpreprocessor -fopenmp
SHLIB_OPENMP_CXXFLAGS = -Xpreprocessor -fopenmp
SHLIB_OPENMP_FCFLAGS = -fopenmp
SHLIB_OPENMP_FFLAGS = -fopenmp
SHLIB_OPENMP_LIBS = -lomp
Restart R after editing
~/.R/Makevarsso the flags are picked up.
By default, CRAN binary packages on macOS are built without OpenMP, because Apple’s toolchain does not enable it when CRAN compiles binaries. To enable OpenMP, you must install from source after setting up your system:
# Install from CRAN source
install.packages("FastKRR", type = "source")
# Or install directly from GitHub (also source build)
pak::pak("kybak90/FastKRR")# example data set
set.seed(1)
n = 1000
rho = 1
X = runif(n, 0, 1)
y = sin(2*pi*X^3) + rnorm(n, 0, 0.1)
# model fitting - nystrom
model_nystrom = fastkrr(X, y, kernel = "gaussian", rho = rho, opt = "nystrom", verbose = FALSE)
model_nystrom$n_threads # >1 indicates OpenMP used by FastKRR (default 4)
#> NULLThe following code chunk inspects the dynamic libraries linked to the
compiled FastKRR shared object (.so or .dylib).
- If the output of
otool -Lcontains a line withlibomp.dylib,
then the package binary is linked against the OpenMP runtime and
parallelization via OpenMP is available. - If
libomp.dylibis absent, the package was compiled without OpenMP support.
is_macos = (Sys.info()[["sysname"]] == "Darwin")
if (is_macos) {
so_dir = system.file("libs", .Platform$r_arch, package = "FastKRR")
if (so_dir == "") so_dir = system.file("libs", package = "FastKRR")
so_files = list.files(so_dir, pattern = "\\.(so|dylib)$", full.names = TRUE)
so = if (length(so_files)) so_files[[1]] else ""
cat("FastKRR shared object:", so, "\n")
if (nzchar(so)) {
cmd = paste("otool -L", shQuote(so))
cat(system(cmd, intern = TRUE), sep = "\n")
} else {
cat("No shared object found. Is FastKRR installed with compiled code?\n")
}
} else {
cat("Skipping otool check (not macOS).\n")
}When you run this on macOS, a successful OpenMP build will show a line such as:
/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libomp.dylib
This confirms that OpenMP is enabled for FastKRR on your system.