  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.
