We introduce a new R package for analysis and inference of network count time series. Such data arise frequently in statistics and epidemiology and are modelled as multivariate time series by employing either linear or log-linear models. However, nonlinear models have also been successful in several fields but often excluded from the analysis due to their relative fitting complexity. In this paper, we offer users the flexibility to study and specify non-linear network count time series models by providing them with a toolkit that copes with computational issues. In addition, new estimation tools for (log-)linear network autoregressive models of count time series are also developed. We illustrate the methodology to the weekly number of influenza A & B cases in the 140 districts of the two Southern German states Bavaria and Baden-Wuerttemberg, for the years 2001 to 2008. This dataset is publicly available, so that the analysis is easily reproducible.
翻译:我们引入了一个新的R包,用于分析和推断网络计时时间序列。这些数据经常出现在统计和流行病学中,并且通过使用线性模型或线性线性模型作为多变时间序列的模式。然而,非线性模型在一些领域也取得了成功,但由于相对的复杂性,往往被排除在分析之外。在本文中,我们为用户提供了研究和指定非线性网络计时时间序列模型的灵活性,为他们提供了一个处理计算问题的工具包。此外,还开发了(log-线性网络)计时序列自动递增模型的新估算工具。我们用2001年至2008年两个南德州巴伐利亚州和巴登-韦特姆贝格州140个地区的甲型和乙型流感病例的周数来说明该方法。这一数据集是公开提供的,因此该分析很容易被重新引用。