论文标题
SSNBayes：贝叶斯时空建模的R软件包在流网络上
SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks
论文作者
论文摘要
时空模型在许多研究领域广泛使用，从生态学到流行病学。但是，大多数协方差函数仅根据欧几里得距离来描述空间关系。在本文中，我们介绍了R软件包SSNBayes，以适合贝叶斯时空模型，并在分支流网络上进行预测。 SSNBayes提供了一个线性回归框架，该框架具有多个用于合并空间和时间自相关的选项。使用流距离和流相连接捕获空间依赖性，而时间自相关则使用向量自动估计方法进行建模。 SSNBayes提供了在整个网络上进行预测，计算超出概率和其他概率估计值（例如合适栖息地的比例）的功能。我们使用在美国爱达荷州收集的流温度数据集说明了软件包的功能。
Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, most covariance functions describe spatial relationships based on Euclidean distance only. In this paper, we introduce the R package SSNbayes for fitting Bayesian spatio-temporal models and making predictions on branching stream networks. SSNbayes provides a linear regression framework with multiple options for incorporating spatial and temporal autocorrelation. Spatial dependence is captured using stream distance and flow connectivity while temporal autocorrelation is modelled using vector autoregression approaches. SSNbayes provides the functionality to make predictions across the whole network, compute exceedance probabilities and other probabilistic estimates such as the proportion of suitable habitat. We illustrate the functionality of the package using a stream temperature dataset collected in Idaho, USA.
