Mathematically Aggregating Expert Judgments
aggreCAT: mathematically aggregating expert judgements
Aggregation Method: AverageWAgg
Aggregation Method: BayesianWAgg
Confidence Score Evaluation
Confidence Score Heat Map
Confidence Score Ridge Plot
Aggregation Method: DistributionWAgg
Aggregation Method: ExtremisationWAgg
Aggregation Method: IntervalWAgg
Aggregation Method: LinearWAgg
Placeholder function with TA2 output
Pipe operator
Post-processing.
Pre-process the data
Aggregation Method: ReasoningWAgg
Aggregation Method: ShiftingWAgg
Weighting method: Asymmetry of intervals
Weighting method: Width of intervals
Weighting method: Individually scaled interval widths
Weighting method: Down weighting outliers
Weighting method: Total number of judgement reasons
Weighting method: Total number and diversity of judgement reasons
Weighting method: Variation in individuals’ interval widths
The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. 'aggreCAT' provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) <doi:10.1371/journal.pone.0256919>. The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data.