Package: aggreCAT 1.1.0

David Wilkinson

aggreCAT: Mathematically Aggregating Expert Judgments

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.

Authors:David Wilkinson [aut, cre], Elliot Gould [aut], Aaron Willcox [aut], Charles T. Gray [aut], Rose E. O'Dea [aut], Rebecca Groenewegen [aut]

aggreCAT_1.1.0.tar.gz
aggreCAT_1.1.0.zip(r-4.7)aggreCAT_1.1.0.zip(r-4.6)aggreCAT_1.1.0.zip(r-4.5)
aggreCAT_1.1.0.tgz(r-4.6-any)aggreCAT_1.1.0.tgz(r-4.5-any)
aggreCAT_1.1.0.tar.gz(r-4.7-any)aggreCAT_1.1.0.tar.gz(r-4.6-any)
aggreCAT_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
aggreCAT/json (API)

# Install 'aggreCAT' in R:
install.packages('aggreCAT', repos = c('https://metamelb-replicats.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/metamelb-replicats/aggrecat/issues

Pkgdown/docs site:https://metamelb-replicats.github.io

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

aggregationelicitationexpertjagscpp

6.03 score 6 stars 10 scripts 281 downloads 22 exports 88 dependencies

Last updated from:e718ae9ff7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK258
source / vignettesOK270
linux-release-x86_64OK311
macos-release-arm64OK193
macos-oldrel-arm64OK279
windows-develOK163
windows-releaseOK186
windows-oldrelOK176
wasm-releaseOK212

Exports:%>%AverageWAggBayesianWAggconfidence_score_evaluationconfidence_score_heatmapconfidence_score_ridgeplotDistributionWAggExtremisationWAggIntervalWAggLinearWAggmethod_placeholderpostprocess_judgementspreprocess_judgementsReasoningWAggShiftingWAggweight_asymweight_intervalweight_nIndivIntervalweight_outlierweight_reasonweight_reason2weight_varIndivInterval

Dependencies:abindaskpassassertthatbitbit64bitopsbootcaToolscellrangerclassclicliprcodacpp11crayoncurldata.tableDescToolsdplyre1071Exactexpmfarverforcatsfsgenericsggplot2gldglueGoFKernelgplotsgridExtragtablegtoolshavenhmshttrinsightisobandjsonliteKernSmoothlabelinglatticelifecyclelmommagrittrMASSmathjaxrMatrixmimeMLmetricsmvtnormopensslpillarpkgconfigprecrecprettyunitsprogressproxypurrrR2jagsR2WinBUGSR6RColorBrewerRcppreadrreadxlrematchrjagsrlangROCRrootSolverstudioapiS7scalesstringistringrsystibbletidyrtidyselecttzdbutf8vctrsVGAMviridisLitevroomwithr

aggreCAT datasets
DARPA SCORE program and the repliCATS project | Datasets | Formatted Judgement Data | Quiz Score Data | Reasoning Data | Bayesian Prior Data | TODO | References
Rendered fromdata.Rmdusingknitr::rmarkdown

Last update: 2026-05-05
Started: 2026-05-05

Tidy Aggregation and Required Data Inputs
Rendered fromaggregation_workflow.Rmdusingknitr::rmarkdown

Last update: 2026-05-05
Started: 2026-05-05

aggreCAT: an R Package for Mathematically Aggregating Expert judgements
Introduction | Mathematically aggregating expert judgements | Box 1: The repliCATS IDEA Protocol | Demonstrating mathematical aggregation with | Box 2: Aggregation Workflow | An illustrative workflow for use in real study contexts | Summary and discussion | Acknowledgments | References | Appendix
Rendered fromaggreCAT.pdf.asisusingR.rsp::asis

Last update: 2022-09-06
Started: 2022-09-06

Readme and manuals

Help Manual

Help pageTopics
Aggregation Method: AverageWAggAverageWAgg
Aggregation Method: BayesianWAggBayesianWAgg
Confidence Score Evaluationconfidence_score_evaluation
Confidence Score Heat Mapconfidence_score_heatmap
Confidence Score Ridge Plotconfidence_score_ridgeplot
data_commentsdata_comments
Confidence Scores generated for 25 papers with 22 aggregation methodsdata_confidence_scores
Free-text justifications for expert judgementsdata_justifications
Replication outcomes for the papersdata_outcomes
P1_ratingsdata_ratings
A table of prior means, to be fed into the BayPRIORsAgg aggregation methoddata_supp_priors
A table of scores on the quiz to assess prior knowledge, to be fed into the QuizWAgg aggregation methoddata_supp_quiz
Categories of reasons provided by participants for their expert judgementsdata_supp_reasons
Aggregation Method: DistributionWAggDistributionWAgg
Aggregation Method: ExtremisationWAggExtremisationWAgg
Aggregation Method: IntervalWAggIntervalWAgg
Aggregation Method: LinearWAggLinearWAgg
Placeholder function with TA2 outputmethod_placeholder
Post-processing.postprocess_judgements
Pre-process the datapreprocess_judgements
Aggregation Method: ReasoningWAggReasoningWAgg
Aggregation Method: ShiftingWAggShiftingWAgg
Weighting method: Asymmetry of intervalsweight_asym
Weighting method: Width of intervalsweight_interval
Weighting method: Individually scaled interval widthsweight_nIndivInterval
Weighting method: Down weighting outliersweight_outlier
Weighting method: Total number of judgement reasonsweight_reason
Weighting method: Total number and diversity of judgement reasonsweight_reason2
Weighting method: Variation in individuals’ interval widthsweight_varIndivInterval