metaggR: Calculate the Knowledge-Weighted Estimate

According to a phenomenon known as "the wisdom of the crowds," combining point estimates from multiple judges often provides a more accurate aggregate estimate than using a point estimate from a single judge. However, if the judges use shared information in their estimates, the simple average will over-emphasize this common component at the expense of the judges’ private information. Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions" <> proposes a procedure for calculating a weighted average of the judges’ individual estimates such that resulting aggregate estimate appropriately combines the judges' collective information within a single estimation problem. The authors use both simulation and data from six experimental studies to illustrate that the weighting procedure outperforms existing averaging-like methods, such as the equally weighted average, trimmed average, and median. This aggregate estimate – know as "the knowledge-weighted estimate" – inputs a) judges' estimates of a continuous outcome (E) and b) predictions of others' average estimate of this outcome (P). In this R-package, the function knowledge_weighted_estimate(E,P) implements the knowledge-weighted estimate. Its use is illustrated with a simple stylized example and on real-world experimental data.

Version: 0.3.0
Depends: R (≥ 4.1)
Imports: MASS, stats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-04-25
DOI: 10.32614/CRAN.package.metaggR
Author: Ville Satopää [aut, cre, cph], Asa Palley [aut]
Maintainer: Ville Satopää <ville.satopaa at>
License: GPL-2
Copyright: (c) Ville Satopaa
NeedsCompilation: no
Citation: metaggR citation info
Materials: README NEWS
CRAN checks: metaggR results


Reference manual: metaggR.pdf
Vignettes: Knowledge Weighted Estimate


Package source: metaggR_0.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): metaggR_0.3.0.tgz, r-oldrel (arm64): metaggR_0.3.0.tgz, r-release (x86_64): metaggR_0.3.0.tgz, r-oldrel (x86_64): metaggR_0.3.0.tgz
Old sources: metaggR archive


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