https://metamelb-replicats.r-universe.devRecent package updates in metamelb-replicatsR-universehttps://github.com/metamelb-replicats.pnghttps://metamelb-replicats.r-universe.devThu, 25 Jun 2026 04:58:37 GMTdavid.wilkinson.research@gmail.com (David Wilkinson)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.https://github.com/r-universe/metamelb-replicats/actions/runs/28149573961Thu, 25 Jun 2026 04:58:37 GMTaggreCAT1.1.0successhttps://metamelb-replicats.r-universe.devhttps://github.com/metamelb-replicats/aggrecatdata.Rmddata.htmlaggreCAT datasets2026-05-05 08:34:162026-05-05 08:34:16aggreCAT.pdf.asisaggreCAT.pdfaggreCAT: an R Package for Mathematically Aggregating Expert judgements2022-09-06 06:03:402022-09-06 06:03:40aggregation_workflow.Rmdaggregation_workflow.htmlTidy Aggregation and Required Data Inputs2026-05-05 08:34:162026-05-05 08:34:16