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The R package CVEK introduces a suite of flexible machine learning models androbust hypothesis tests for learning the joint nonlinear effects of multiplecovariates in limited samples. It implements the Cross-validated Ensemble ofKernels (CVEK)(Liu and Coull 2017), an ensemble-based kernel machine learningmethod that adaptively learns the joint nonlinear effect of multiple covariatesfrom data, and provides powerful hypothesis tests for both main effects offeatures and interactions among features. The R Package CVEK provides aflexible, easy-to-use implementation of CVEK, and offers a wide range ofchoices for the kernel family (for instance, polynomial, radial basisfunctions, 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). Throughextensive simulations we demonstrate the validity and robustness of thisapproach, and provide practical guidelines on how to design an estimationstrategy for optimal performance in different data scenarios.

CVEK: Robust Estimation and Testing for Nonlinear Effects using Kernel Machine Ensemble