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  "Title": "Mathematically Aggregating Expert Judgments",
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  "Description": "The use of structured elicitation to inform decision\nmaking has grown dramatically in recent decades, however,\njudgements from multiple experts must be aggregated into a\nsingle estimate. Empirical evidence suggests that mathematical\naggregation provides more reliable estimates than enforcing\nbehavioural consensus on group estimates. 'aggreCAT' provides\nstate-of-the-art mathematical aggregation methods for\nelicitation data including those defined in Hanea, A. et al.\n(2021) <doi:10.1371/journal.pone.0256919>. The package also\nprovides functions to visualise and evaluate the performance of\nyour aggregated estimates on validation data.",
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  "Author": "David Wilkinson [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-9560-6499>),\nElliot Gould [aut] (ORCID: <https://orcid.org/0000-0002-6585-538X>),\nAaron Willcox [aut] (ORCID: <https://orcid.org/0000-0003-2536-2596>),\nCharles T. Gray [aut],\nRose E. O'Dea [aut] (ORCID: <https://orcid.org/0000-0001-8177-5075>),\nRebecca Groenewegen [aut] (ORCID:\n<https://orcid.org/0000-0001-9177-8536>)",
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