CausalMetaR: Causally Interpretable Meta-Analysis

Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716>, Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>, and Wang et al. (2024) <doi:10.48550/arXiv.2402.02684>. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2024) <doi:10.48550/arXiv.2402.04341> for a detailed guide on using the package.

Version: 0.1.2
Depends: R (≥ 2.10)
Imports: glmnet, metafor, nnet, progress, SuperLearner
Suggests: testthat (≥ 3.0.0)
Published: 2024-06-04
DOI: 10.32614/CRAN.package.CausalMetaR
Author: Yi Lian [aut], Guanbo Wang [aut], Sean McGrath ORCID iD [aut, cre], Issa Dahabreh [aut]
Maintainer: Sean McGrath <sean_mcgrath at>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README NEWS
In views: MetaAnalysis
CRAN checks: CausalMetaR results


Reference manual: CausalMetaR.pdf


Package source: CausalMetaR_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): CausalMetaR_0.1.2.tgz, r-oldrel (arm64): CausalMetaR_0.1.2.tgz, r-release (x86_64): CausalMetaR_0.1.2.tgz, r-oldrel (x86_64): CausalMetaR_0.1.2.tgz
Old sources: CausalMetaR archive


Please use the canonical form to link to this page.