Calculate the Knowledge-Weighted Estimate R package metaggR version 0.3.0 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" <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> 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. Software Depends: R (>= 4.1) Imports: MASS, stats Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Ville Satopää <ville.satopaa@gmail.com> Comprehensive R Archive Network (CRAN) Ville Satopää [aut, cre, cph], Asa Palley [aut] GPL-2 2022-04-25 application/tgz https://CRAN.R-project.org/package=metaggR doi:10.32614/CRAN.package.metaggR