Facilitating systematic reviews, data extraction and meta‐analysis with the metagear package for r

Summary The r package ecosystem is rich in tools for the statistics of meta‐analysis. However, there are few resources available to facilitate research synthesis as a whole. Here, I present the metagear package for r . It 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 analyse these data with the statistics of meta‐analysis. Current functionalities of metagear include the following: an abstract screener GUI to efficiently sieve bibliographic information from large numbers of candidate studies; tools to assign screening effort across multiple collaborators/reviewers and to assess inter‐reviewer reliability using kappa statistics; PDF downloader to automate the retrieval of journal articles from online data bases; automated data extractions from scatter‐plots, box‐plots and bar‐plots; PRISMA 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. with a common control, phylogenetic correlations); and finally, summaries that replicate analyses and outputs from widely used but no longer updated meta‐analysis software. Research synthesis practices are vital to many disciplines in the sciences, including ecology and evolutionary biology, and metagear aims to enrich the scope, quality and reproducibility of what can be achieved with the systematic review and meta‐analysis of research outcomes.