This paper introduces an R package that implements Simultaneous non-Gaussian Component Analysis for data integration. SING uses a non-Gaussian measure of information to extract feature loadings and scores (latent variables) that are shared across multiple datasets. We describe and implement functions through two examples. The first example is a toy example working with images. The second example is a simulated study integrating functional connectivity estimates from a restingstate functional magnetic resonance imaging dataset and task activation maps from a working memory functional magnetic resonance imaging dataset. The SING model can produce joint components that accurately reflect information shared by multiple datasets, particularly for datasets with non-Gaussian features such as neuroimaging.
