  The R package CVEK introduces a suite of flexible machine learning models and
robust hypothesis tests for learning the joint nonlinear effects of multiple
covariates in limited samples. It implements the Cross-validated Ensemble of
Kernels (CVEK)(Liu and Coull 2017), an ensemble-based kernel machine learning
method that adaptively learns the joint nonlinear effect of multiple covariates
from data, and provides powerful hypothesis tests for both main effects of
features and interactions among features. The R Package CVEK provides a
flexible, easy-to-use implementation of CVEK, and offers a wide range of
choices for the kernel family (for instance, polynomial, radial basis
functions, Mat\'ern, neural network, and others), model selection criteria,
ensembling method (averaging, exponential weighting, cross-validated stacking),
and the type of hypothesis test (asymptotic or parametric bootstrap). Through
extensive simulations we demonstrate the validity and robustness of this
approach, and provide practical guidelines on how to design an estimation
strategy for optimal performance in different data scenarios.
