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; figure and image extractions
from PDFs; web scraping of citations; automated and manual data extraction
from scatter-plot and bar-plot images; 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widely used but no longer updated meta-analysis software (i.e., metawin).
Funding for this package was supported by National Science Foundation (NSF)
grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016)
Facilitating systematic reviews, data extraction and meta-analysis with the
metagear package for R. Methods in Ecology and Evolution 7, 323-330
metagear is a comprehensive, multifunctional toolbox with capabilities aimed to cover much of the research synthesis taxonomy: from applying a systematic review approach to objectively assemble and screen the literature, to extracting data from studies, and to finally summarize and analyze these data with the statistics of meta-analysis. More information about metagear can be found at http://lajeunesse.myweb.usf.edu.
metagear has an external dependency that needs to be installed and loaded prior to use in R. This is the EBImage R package (Pau et al. 2010) available only from the Bioconductor repository: https://www.bioconductor.org. To properly install metagear, start with the following R script that loads the Bioconductor resources needed to install the EBImage (also accept all dependencies):
install.packages("BiocManager");
BiocManager::install("EBImage")
library(metagear)
This dependency sometimes results in CRAN failing to generate a binary of metagear for your OS -- which sadly happens often. In this case install from the source, for example:
install.packages("metagear_0.7.tar.gz", repos = "http://cran.us.r-project.org", type = "source", dependencies = TRUE)
library(metagear)
Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323−330. Download PDF here: http://lajeunesse.myweb.usf.edu.