# Mathematically Aggregating Expert Judgments

## Documentation for package ‘aggreCAT’ version 1.0.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

AverageWAgg | Aggregation Method: AverageWAgg
BayesianWAgg | Aggregation Method: BayesianWAgg
confidence_score_evaluation | Confidence Score Evaluation
confidence_score_heatmap | Confidence Score Heat Map
confidence_score_ridgeplot | Confidence Score Ridge Plot
data_comments | data_comments
data_confidence_scores | Confidence Scores generated for 25 papers with 22 aggregation methods
data_justifications | Free-text justifications for expert judgements
data_outcomes | Replication outcomes for the papers
data_ratings | P1_ratings
data_supp_priors | A table of prior means, to be fed into the BayPRIORsAgg aggregation method
data_supp_quiz | A table of scores on the quiz to assess prior knowledge, to be fed into the QuizWAgg aggregation method
data_supp_reasons | Categories of reasons provided by participants for their expert judgements
DistributionWAgg | Aggregation Method: DistributionWAgg
ExtremisationWAgg | Aggregation Method: ExtremisationWAgg
IntervalWAgg | Aggregation Method: IntervalWAgg
LinearWAgg | Aggregation Method: LinearWAgg
method_placeholder | Placeholder function with TA2 output
postprocess_judgements | Post-processing.
preprocess_judgements | Pre-process the data
ReasoningWAgg | Aggregation Method: ReasoningWAgg
ShiftingWAgg | Aggregation Method: ShiftingWAgg
weight_asym | Weighting method: Asymmetry of intervals
weight_interval | Weighting method: Width of intervals
weight_nIndivInterval | Weighting method: Individually scaled interval widths
weight_outlier | Weighting method: Down weighting outliers
weight_reason | Weighting method: Total number of judgement reasons
weight_reason2 | Weighting method: Total number and diversity of judgement reasons
weight_varIndivInterval | Weighting method: Variation in individuals’ interval widths