Package: CausalMetaR
Type: Package
Title: Causally Interpretable Meta-Analysis
Version: 0.1.1
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_mcgrath@g.harvard.edu", 
                    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> and Robertson et al. (2021) <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.  
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: glmnet, metafor, nnet, progress, SuperLearner
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
RoxygenNote: 7.2.3
URL: https://github.com/ly129/CausalMetaR
BugReports: https://github.com/ly129/CausalMetaR/issues
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-01-13 21:04:48 UTC; Sean
Author: Yi Lian [aut],
  Guanbo Wang [aut],
  Sean McGrath [aut, cre] (<https://orcid.org/0000-0002-7281-3516>),
  Issa Dahabreh [aut]
Maintainer: Sean McGrath <sean_mcgrath@g.harvard.edu>
Repository: CRAN
Date/Publication: 2024-01-15 16:30:02 UTC
Built: R 4.4.0; ; 2024-04-06 09:34:07 UTC; unix
