  We introduce a new R package useful for inference about network count time
series. Such data are frequently encountered in statistics and they are usually
treated as multivariate time series. Their statistical analysis is based on
linear or log linear models. Nonlinear models, which have been applied
successfully in several research areas, have been neglected from such
applications mainly because of their computational complexity. We provide R
users the flexibility to fit and study nonlinear network count time series
models which include either a drift in the intercept or a regime switching
mechanism. We develop several computational tools including estimation of
various count Network Autoregressive models and fast computational algorithms
for testing linearity in standard cases and when non-identifiable parameters
hamper the analysis. Finally, we introduce a copula Poisson algorithm for
simulating multivariate network count time series. We illustrate the
methodology by modeling weekly number of influenza cases in Germany.
