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> and Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>. 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.

Version:	0.1.1
Depends:	R (≥ 2.10)
Imports:	glmnet, metafor, nnet, progress, SuperLearner
Suggests:	testthat (≥ 3.0.0)
Published:	2024-01-15
Author:	Yi Lian [aut], Guanbo Wang [aut], Sean McGrath [aut, cre], Issa Dahabreh [aut]
Maintainer:	Sean McGrath <sean_mcgrath at g.harvard.edu>
BugReports:	https://github.com/ly129/CausalMetaR/issues
License:	GPL (≥ 3)
URL:	https://github.com/ly129/CausalMetaR
NeedsCompilation:	no
Materials:	README
In views:	MetaAnalysis
CRAN checks:	CausalMetaR results

Documentation:

Reference manual:

CausalMetaR.pdf

Downloads:

Package source:	CausalMetaR_0.1.1.tar.gz
Windows binaries:	r-devel: CausalMetaR_0.1.1.zip, r-release: CausalMetaR_0.1.1.zip, r-oldrel: CausalMetaR_0.1.1.zip
macOS binaries:	r-release (arm64): CausalMetaR_0.1.1.tgz, r-oldrel (arm64): CausalMetaR_0.1.1.tgz, r-release (x86_64): CausalMetaR_0.1.1.tgz, r-oldrel (x86_64): CausalMetaR_0.1.1.tgz

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