标题： AutoWMM 和 JAGStree -- 用于关系树结构数据种群大小估计的 R 包 显示英文标题

标题： AutoWMM and JAGStree -- R packages for Population Size Estimation on Relational Tree-Structured Data

摘要： 加权乘数方法（WMM）是对传统反向计算方法的扩展，用于估计目标人群的规模，该方法利用数据中固有的树状结构，综合来自目标人群多个已知数量和估计比例的子群体的可用证据。 分层贝叶斯模型为在该结构上建模人口规模提供了另一种方法，但需要非 trivial 的理论和实践知识来实现。 尽管 WMM 方法的理论可能对不同领域的研究人员更具可访问性，但执行此方法仍存在障碍，这需要大量的计算。 我们开发了两个\texttt{R}包，以帮助使用 WMM 和分层贝叶斯建模在树上进行人口规模估计；\textit{自动WMM}简化了任何一般树拓扑结构的 WMM 估计，而\textit{JAGStree}自动为这些网络创建合适的 JAGS MCMC 建模代码。 显示英文摘要

摘要： The weighted multiplier method (WMM) is an extension of the traditional method of back-calculation method to estimate the size of a target population, which synthesizes available evidence from multiple subgroups of the target population with known counts and estimated proportions by leveraging the tree-structure inherent to the data. Hierarchical Bayesian models offer an alternative to modeling population size estimation on such a structure, but require non-trivial theoretical and practical knowledge to implement. While the theory underlying the WMM methodology may be more accessible to researchers in diverse fields, a barrier still exists in execution of this method, which requires significant computation. We develop two \texttt{R} packages to help facilitate population size estimation on trees using both the WMM and hierarchical Bayesian modeling; \textit{AutoWMM} simplifies WMM estimation for any general tree topology, and \textit{JAGStree} automates the creation of suitable JAGS MCMC modeling code for these same networks.