MetaIntegration: Ensemble Meta-Prediction Framework

An ensemble meta-prediction framework to integrate multiple regression models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) <arXiv:2010.09971>. A meta-analysis framework along with two weighted estimators as the ensemble of empirical Bayes estimators, which combines the estimates from the different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naive analysis of the internal data and other naive combinations of external estimators.

Version: 0.1.2
Depends: R (≥ 3.5.0)
Imports: Rsolnp, corpcor, MASS, knitr
Published: 2021-03-17
Author: Tian Gu [aut], Bhramar Mukherjee [aut], Michael Kleinsasser [cre]
Maintainer: Michael Kleinsasser <mkleinsa at>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: MetaIntegration results


Reference manual: MetaIntegration.pdf
Package source: MetaIntegration_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: MetaIntegration_0.1.2.tgz, r-oldrel: MetaIntegration_0.1.2.tgz
Old sources: MetaIntegration archive


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