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.

翻译：本文介绍了一个新的R包，该包用于网络计数时间序列的统计推断。此类数据在统计学中频繁出现，通常被作为多元时间序列处理，其统计分析基于线性或对数线性模型。在多个研究领域已成功应用的非线性模型，因计算复杂性而在此类应用中常被忽略。我们为R用户提供了拟合与研究非线性网络计数时间序列模型的灵活性，这些模型包括截距项中的漂移或机制切换机制。我们开发了多种计算工具，包括各类计数网络自回归模型的估计，以及用于标准情形下线性检验和存在不可识别参数阻碍分析时的快速计算算法。最后，我们引入了一种Copula Poisson算法用于模拟多元网络计数时间序列。我们通过建模德国每周流感病例数对该方法进行了说明。