fabisearch: A package for change point detection in and visualization of the network structure of multivariate high-dimensional time series in R

In this work, we introduce the R package fabisearch, available on the Comprehensive R Archive Network (CRAN), which implements an original change point detection method for multivariate high-dimensional time series data and a new interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. Change point detection is a commonly used technique in time series analysis, capturing the dynamic nature in which many real -world processes function. With the ever increasing troves of multivariate high-dimensional time series data, especially in neuroimaging and finance, there is a clear need for scalable and data -driven change point detection methods. Currently, change point detection methods for multivariate high-dimensional data are scarce, with even less available in high-level, easily accessible software packages. fabisearch, which implements the factorized binary search (FaBiSearch) methodology, is a novel statistical method for detecting change points in the network structure of multivariate high-dimensional time series which employs non -negative matria factorization (NMF), an unsupervised dimension reduction and clustering technique. We utilize a new binary search algorithm to efficiently identify multiple change points and provide a new method for network estimation for data between change points. We show the functionality of the package and the practicality of the method by applying it to a neuroimaging and a finance data set. We also introduce an interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership (if applicable). The output is a network laid over a cortical surface, which can be rotated in 3-dimensional space.