Abstract

There is extensive choice in R to support meta‐analyses.Two packages in this ecosystem include meta and metafor and provide an excellent opportunity to apply a structured checklist previously developed for contrasts between R packages relevant to challenges in ecology and evolution.Meta is a direct, intuitive choice for rapid implementation of general meta‐analytical statistics. Metafor is a comprehensive package best suited for relatively more complex models.Both packages provide estimates of heterogeneity, excellent visualization tools, and functions to explore publication bias.The package metafor has a steeper learning curve but greater rewards. Reference to the learning curve and capacities of the statistical software Stata provided a benchmark outside the R ecosystem and confirmed the consistency in statistics.The usefulness of meta‐analyses is not just in the synthesis of the research but in the process of doing the scientific synthesis. Reporting of contrasts and checks for robust statistics is an important contribution to more transparent and reproducible scientific syntheses.

Highlights

  • Meta-analyses are common and powerful synthesis tools in science

  • Statistics in the fields of ecology and evolution for instance have increasingly moved to the programming language R (Lai et al 2019), and synthesis statistics are no exception

  • At least two R packages have risen to prominence for general meta-analytical statistics in the natural sciences - namely meta (Schwarzer 2019) and metafor (Viechtbauer 2017)

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Summary

Introduction

In the natural sciences, meta-analyses are used as a mechanism to describe and aggregate quantitative evidence from a set of peer-reviewed, primary research publications The terms systematic review and meta-analysis are used more interchangeable, and meta-statistics are often done on compiled randomized controlled trials or other relatively large datasets in addition to sets of data derived from peer-reviewed publications. At least two R packages have risen to prominence for general meta-analytical statistics in the natural sciences - namely meta (Schwarzer 2019) and metafor (Viechtbauer 2017).

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