Package: CausalMetaR
Type: Package
Title: Causally Interpretable Meta-Analysis
Version: 0.1.3
Authors@R: c(person("Yi", "Lian", role = "aut", email = "yi.lian@pennmedicine.upenn.edu"),
             person("Guanbo", "Wang", role = "aut", email = "gwang@hsph.harvard.edu"),
             person("Sean", "McGrath", role = c("aut", "cre"), email = "sean.mcgrath514@gmail.com", 
                    comment = c(ORCID = "0000-0002-7281-3516")),
             person("Issa", "Dahabreh", role = "aut"))
Description: 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. (2025) <doi:10.1017/rsm.2025.5> for a
        detailed guide on using the package.
License: GPL (>=3)
Encoding: UTF-8
LazyData: true
Imports: glmnet, metafor, nnet, progress, SuperLearner
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
RoxygenNote: 7.3.2
URL: https://github.com/ly129/CausalMetaR,
        https://doi.org/10.1017/rsm.2025.5
BugReports: https://github.com/ly129/CausalMetaR/issues
Depends: R (>= 2.10)
Repository: https://ly129.r-universe.dev
Date/Publication: 2025-04-11 01:10:14 UTC
RemoteUrl: https://github.com/ly129/causalmetar
RemoteRef: HEAD
RemoteSha: 8ed771119451d482877e0b03e7128c19c81bd0c0
NeedsCompilation: no
Packaged: 2026-07-04 07:01:43 UTC; root
Author: Yi Lian [aut],
  Guanbo Wang [aut],
  Sean McGrath [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-7281-3516>),
  Issa Dahabreh [aut]
Maintainer: Sean McGrath <sean.mcgrath514@gmail.com>