kDGLM: a R package for Bayesian analysis of Generalized Dynamic Linear Models
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AI 根据所提供的论文材料归类：摘要核心是广义动态线性模型对时间序列的贝叶斯序贯拟合与平滑，直接对应时间序列方法，未限定宏观数据。
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kDGLM：广义动态线性模型贝叶斯分析的 R 包
- 研究问题
- 如何为单变量及多变量指数族时间序列提供计算高效的贝叶斯动态建模工具，并支持拟合、平滑、监控和前馈干预？
- 方法与思路
- 作者介绍 kDGLM，将广义动态线性模型的序贯推断与动态模型中的折扣策略、自回归和传递函数等技术结合，利用滤波与平滑计算。
- 材料陈述的贡献
- 摘要称，该包支持多种观测分布及多序列联合建模，计算耗时随序列长度线性扩展，并以巴西住院数据展示拟合速度。
- 阅读关注点
- 应核对序贯更新和近似步骤、支持分布的参数限制、状态维数对复杂度的影响，以及速度案例的硬件环境与可复现设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要中的计时仅反映所述案例，未说明硬件与比较基准，软件当前状态也未核验。
原文摘要
This paper introduces kDGLM, an R package designed for Bayesian analysis of Generalized Dynamic Linear Models (GDLM), with a primary focus on both uni- and multivariate exponential families. Emphasizing sequential inference for time series data, the kDGLM package provides comprehensive support for fitting, smoothing, monitoring, and feed-forward interventions. The methodology employed by kDGLM, as proposed in Alves et al. (2024), seamlessly integrates with well-established techniques from the literature, particularly those used in (Gaussian) Dynamic Models. These include discount strategies, autoregressive components, transfer functions, and more. Leveraging key properties of the Kalman filter and smoothing, kDGLM exhibits remarkable computational efficiency, enabling virtually instantaneous fitting times that scale linearly with the length of the time series. This characteristic makes it an exceptionally powerful tool for the analysis of extended time series. For example, when modeling monthly hospital admissions in Brazil due to gastroenteritis from 2010 to 2022, the fitting process took a mere 0.11s. Even in a spatial-time variant of the model (27 outcomes, 110 latent states, and 156 months, yielding 17,160 parameters), the fitting time was only 4.24s. Currently, the kDGLM package supports a range of distributions, including univariate Normal (unknown mean and observational variance), bivariate Normal (unknown means, observational variances, and correlation), Poisson, Gamma (known shape and unknown mean), and Multinomial (known number of trials and unknown event probabilities). Additionally, kDGLM allows the joint modeling of multiple time series, provided each series follows one of the supported distributions. Ongoing efforts aim to continuously expand the supported distributions.