Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel provides methods to combine multiple kernels for unsupervised exploratory analysis or to select features in a kernel for unsupervised analysis or kernel-output prediction. For the multiple kernel problem, different solutions have been implemented to obtain a consensus kernel or a kernel that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a nonlinear space and from the multiple source point of view. Functions to assess and display important variables using permutations are also provided in the package.