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

Abstract

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.