drpop: Efficient and Doubly Robust Population Size Estimation in R

TL;DR

The paper presents drpop, an R package for flexible, doubly robust population size estimation from incomplete lists, accommodating complex covariates with fewer assumptions and demonstrating strong performance in various scenarios.

Contribution

Introducing the drpop R package that enables flexible, doubly robust population size estimation using covariates without strict parametric assumptions.

Findings

Effective in high-dimensional covariate settings

Provides accurate confidence intervals

Performs well across different scenarios

Abstract

This paper introduces the R package drpop to flexibly estimate total population size from incomplete lists. Total population estimation, also called capture-recapture, is an important problem in many biological and social sciences. A typical dataset consists of incomplete lists of individuals from the population of interest along with some covariate information. The goal is to estimate the number of unobserved individuals and equivalently, the total population size. drpop flexibly models heterogeneity using the covariate information, under the assumption that two lists are conditionally independent given covariates. This can be a much weaker assumption than full marginal independence often required by classical methods. Moreover, it can incorporate complex and high dimensional covariates, and does not require parametric models like other popular methods. In particular, our estimator is doubly robust and has fast convergence rates even under flexible non-parametric set-ups. drpop provides the user with the flexibility to choose the model for estimation of intermediate parameters and returns the estimated population size, confidence interval and some other related quantities. In this paper, we illustrate the applications of drpop in different scenarios and we also present some performance summaries.