The R package rts2 provides data manipulation and model fitting tools for Log Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease and other types of surveillance, and provide a means of predicting risk across an area of interest based on spatially-referenced and time-stamped case data. However, these models can be difficult to specify and computationally demanding to estimate. For many surveillance scenarios we require results in near real-time using routinely available data to guide and direct policy responses, or due to limited availability of computational resources. There are limited software implementations available for this real-time context with reliable predictions and quantification of uncertainty. The rts2 package provides a range of modern Gaussian process approximations and model fitting methods to fit the LGCP, including estimation of covariance parameters, using both Bayesian and stochastic Maximum Likelihood methods. The package provides a suite of data manipulation tools. We also provide a novel implementation to estimate the LGCP when case data are aggregated to an irregular grid such as census tract areas.

翻译：R语言包rts2提供了对数高斯Cox过程（LGCP）模型的数据操作与模型拟合工具。LGCP模型是疾病及其他类型监测的关键方法，能够基于空间参考和时间标记的病例数据预测研究区域的风险分布。然而，这类模型不仅难以精确设定，且估计过程计算量巨大。在众多监测场景中，我们需要利用常规可用数据实现近实时结果输出，以指导政策响应，或受限于计算资源有限性。当前针对实时场景的软件实现有限，难以同时保证可靠预测与不确定性量化。rts2包提供了多种现代高斯过程近似方法与模型拟合技术来拟合LGCP模型，包括通过贝叶斯方法和随机最大似然方法估计协方差参数。该软件包配备了一套完整的数据操作工具。此外，我们创新性地实现了病例数据聚合至不规则网格（如人口普查区）时LGCP模型的估计方法。