aggreCAT: An R Package for Mathematically Aggregating Expert Judgments

Structured elicitation protocols, such as the IDEA protocol, are used to elicit probabilistic judgements from multiple domain experts about uncertain events across fields including ecology, biosecurity risk assessment, and metascience. Individual expert judgements must subsequently be mathematically aggregated into a single group forecast. While the simplest case involves combining a set of point-estimates from multiple individuals, this process is further complicated when judgements include uncertainty bounds, or when elicitation is conducted across multiple rounds. This paper presents aggreCAT, an open-source R package that provides 29 aggregation methods for combining individual expert judgements into a single probabilistic estimate, accommodating designs ranging from single-round point estimates to multi-round three-point elicitation. The package follows tidy data principles, enabling straightforward integration with existing R workflows for application at scale. Methods range from unweighted arithmetic combinations to performance-weighted schemes and Bayesian models, with weights derived from uncertainty intervals, shifts in judgements between elicitation rounds, and breadth of expert reasoning. We provide worked examples illustrating the mechanics of representative aggregation methods, a general workflow for batch aggregation across multiple forecasts and methods, and built-in functions for evaluating and visualising forecast performance against known outcomes. aggreCAT fills a substantive gap in open software for mathematically aggregating expert judgement, and is intended to support researchers and decision analysts in rapidly and rigorously synthesising outputs from structured elicitation exercises.