R: Kernel Ridge Regression using 'RcppArmadillo'

# Kernel Ridge Regression using 'RcppArmadillo'

## Documentation for package ‘FastKRR’ version 0.1.2

- DESCRIPTION file
- Package README

## Help Pages

FastKRR-package | Kernel Ridge Regression using the 'RcppArmadillo' Package
approx_kernel | Compute low-rank approximations(Nyström, Pivoted Cholesky, RFF)
coef.krr | Coef method for fitted Kernel Ridge Regression models
error | Compute Model Error (Generic)
error.default | Compute Model Error (Generic)
error.krr | Compute Model Error for Kernel Ridge Regression Models
FastKRR | Kernel Ridge Regression using the 'RcppArmadillo' Package
fastkrr | Fit kernel ridge regression using exact or approximate methods
krr_reg | Kernel Ridge Regression
make_kernel | Kernel matrix K construction for given datasets
param | Extract/print hyperparameters of fitted models
param.default | Extract/print hyperparameters of fitted models
param.krr | Param method for fitted Kernel Ridge Regression models
plot.krr | Plot method for fitted Kernel Ridge Regression (KRR) models
predict.krr | Predict responses for new data using fitted KRR model
print.approx_kernel | Print method for approximated kernel matrices
print.kernel_matrix | Print method for kernel matrices
print.krr | Print method for fitted Kernel Ridge Regression models
summary.krr | Summary method for fitted Kernel Ridge Regression models
tunable.krr_reg | Expose tunable parameters for '"krr_reg"'