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), <arXiv:2002.02627>,
extending previous works by Schwartz and Zanobetti (2000)
and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.
dplyr, furrr, ggplot2, knitr, metafor, metap, purrr, rlang,
stringr, tidyr, viridis

dplyr, furrr, ggplot2, knitr, metafor, metap, purrr, rlang,
stringr, tidyr
future, multtest, mgcv, gamm4, gratia, roxygen2, rmarkdown,
devtools, covr, viridis, testthat (>= 2.1.0)

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>.
roxygen2, rmarkdown, devtools, covr, testthat (>= 2.1.0)

roxygen2, knitr, rmarkdown, devtools, covr, testthat (>=
2.1.0)

Comprehensive Research Synthesis Tools for Systematic Reviews
and Meta-Analysis
person("Marc J.", "Lajeunesse", email = "lajeunesse@usf.edu", role = c("aut", "cre"))
Functionalities for facilitating systematic reviews, data extractions, and meta-analyses.  It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter-reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; automated data extractions from scatter-plots; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software.  Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031.