  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. To this end, we
introduce the R package fabisearch, available on the Comprehensive R Archive
Network (CRAN), which implements the factorized binary search (FaBiSearch)
methodology. FaBiSearch is a novel statistical method for detecting change
points in the network structure of multivariate high-dimensional time series
which employs non-negative matrix factorization (NMF), an unsupervised
dimension reduction and clustering technique. Given the high computational cost
of NMF, we implement the method in C++ code and use parallelization to reduce
computation time. Further, we also 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. Lastly, we provide 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 an elegantly displayed network laid over a cortical
surface, which can be rotated in the 3-dimensional space.
