Package: metagen
Version: 1.0
Date: 2014-05
Title: Inference in Meta Analysis and Meta Regression
Description: Provides methods for making inference in the random effects meta
regression model such as point estimates and confidence intervals for the
heterogeneity parameter and the regression coefficients vector.  Inference
methods are based on different approaches to statistical inference.
Methods from three different schools are included: methods based on the
method of moments approach, methods based on likelihood, and methods based
on generalised inference.  The package also includes tools to run extensive
simulation studies in parallel on high performance clusters in a modular
way.  This allows extensive testing of custom inferential methods with all
implemented state-of-the-art methods in a standardised way.  Tools for
evaluating the performance of both point and interval estimates are
provided.  Also, a large collection of different pre-defined plotting
functions is implemented in a ready-to-use fashion.
Author: Thomas W. D. Möbius <kontakt@thomasmoebius.de>
Maintainer: Thomas W. D. Möbius <kontakt@thomasmoebius.de>
URL: http://00tau.github.io/metagen/
License: GPL-3
Imports: MASS, lhs, plyr, BBmisc, ParamHelpers, BatchJobs,
BatchExperiments, ggplot2, metafor
LazyData: yes
ByteCompile: yes
Encoding: UTF-8
Collate: 'example-bcg.R' 'methods-for-batch-distribution.R'
'methods-for-data-generation.R' 'methods-for-experiments.R'
'methods-for-inference.R' 'plot-functions.R'
'plot-interval-estimates.R' 'plot-pivotal-distributions.R'
'plot-point-estimates.R' 'zzz.R'
Packaged: 2014-05-27 12:31:33 UTC; friedrich
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-05-27 15:43:54

Title: 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"
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.
Packaged: 2021-01-07 11:39:12 UTC; ville.satopaa