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

TL;DR

This paper introduces two R packages, AutoWMM and JAGStree, that simplify and automate population size estimation on relational tree-structured data using the weighted multiplier method and hierarchical Bayesian models.

Contribution

The paper presents new R tools that make implementing WMM and Bayesian models on tree data more accessible and computationally feasible.

Findings

AutoWMM simplifies WMM estimation on arbitrary tree topologies.

JAGStree automates JAGS code generation for hierarchical Bayesian models.

The packages facilitate more efficient population size estimation on 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.