Mathematically Aggregating Expert Judgments

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Documentation for package ‘aggreCAT’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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