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:
aggreCAT_1.1.0.tar.gz
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✨
card.svg |card.png
aggreCAT/json (API)
NEWS
| # Install 'aggreCAT' in R: |
| install.packages('aggreCAT', repos = c('https://cran.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
- jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
- c++– GNU Standard C++ Library v3
- 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
Last updated from:f261c70dc6. Checks:4 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 238 | ||
| source / vignettes | OK | 280 | ||
| linux-release-x86_64 | OK | 234 | ||
| wasm-release | OK | 173 |
Exports:%>%AverageWAggBayesianWAggconfidence_score_evaluationconfidence_score_heatmapconfidence_score_ridgeplotDistributionWAggExtremisationWAggIntervalWAggLinearWAggmethod_placeholderpostprocess_judgementspreprocess_judgementsReasoningWAggShiftingWAggweight_asymweight_intervalweight_nIndivIntervalweight_outlierweight_reasonweight_reason2weight_varIndivInterval
Dependencies:abindaskpassassertthatbitbit64bitopsbootcaToolscellrangerclassclicliprcodacpp11crayoncurldata.tableDescToolsdplyre1071Exactexpmfarverforcatsfsgenericsggplot2gldglueGoFKernelgplotsgridExtragtablegtoolshavenhmshttrinsightisobandjsonliteKernSmoothlabelinglatticelifecyclelmommagrittrMASSmathjaxrMatrixmimeMLmetricsmvtnormopensslpillarpkgconfigprecrecprettyunitsprogressproxypurrrR2jagsR2WinBUGSR6RColorBrewerRcppreadrreadxlrematchrjagsrlangROCRrootSolverstudioapiS7scalesstringistringrsystibbletidyrtidyselecttzdbutf8vctrsVGAMviridisLitevroomwithr
aggreCAT datasets
Rendered fromdata.Rmdusingknitr::rmarkdownon Jun 03 2026.Last update: 2026-05-04
Started: 2026-05-04
aggreCAT: an R Package for Mathematically Aggregating Expert judgements
Rendered fromaggreCAT.pdf.asisusingR.rsp::asison Jun 03 2026.Last update: 2025-05-28
Started: 2025-05-28
Tidy Aggregation and Required Data Inputs
Rendered fromaggregation_workflow.Rmdusingknitr::rmarkdownon Jun 03 2026.Last update: 2026-05-04
Started: 2026-05-04
