The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
| Version: | 0.3.0 |
| Depends: | R (≥ 4.1) |
| Imports: | MASS, stats |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2022-04-25 |
| Author: | Ville Satopää [aut, cre, cph], Asa Palley [aut] |
| Maintainer: | Ville Satopää <ville.satopaa at gmail.com> |
| 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: metaggR_0.3.0.zip, r-release: metaggR_0.3.0.zip, r-oldrel: metaggR_0.3.0.zip |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=metaggR to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.