Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. 'metagam' provides functionality for removing individual participant data from models computed using the 'mgcv' and 'gamm4' packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided. The implemented methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>, extending previous works by Schwartz and Zanobetti (2000) and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.
Version: 0.4.1 Imports: mgcv, ggplot2, metafor, rlang Suggests: roxygen2, knitr, rmarkdown, devtools, covr, testthat (≥ 2.1.0) Published: 2025-07-01 DOI: 10.32614/CRAN.package.metagam Author: Oystein Sorensen [aut, cre], Andreas M. Brandmaier [aut], Athanasia Mo Mowinckel [aut] Maintainer: Oystein Sorensen <oystein.sorensen at psykologi.uio.no> BugReports: https://github.com/Lifebrain/metagam/issues License: GPL-3 URL: https://lifebrain.github.io/metagam/, https://github.com/Lifebrain/metagam NeedsCompilation: no Citation: metagam citation info Materials: README In views: MetaAnalysis CRAN checks: metagam results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=metagam to link to this page.
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