  Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics of
multivariate time series data. They have been widely used in macroeconomics,
financial econometrics, neuroscience and functional genomics. In many
applications, the data exhibit structural changes in their autoregressive
dynamics, which correspond to changes in the transition matrices of the VAR
model that specify such dynamics. We present the R package VARDetect that
implements two classes of algorithms to detect multiple change points in
piecewise stationary VAR models. The first exhibits sublinear computational
complexity in the number of time points and is best suited for structured
sparse models, while the second exhibits linear time complexity and is designed
for models whose transition matrices are assumed to have a low rank plus sparse
decomposition. The package also has functions to generate data from the various
variants of VAR models discussed, which is useful in simulation studies, as
well as to visualize the results through network layouts.
