R packages like *meta* and *metafor* are widely praised for their flexibility and power, but this very flexibility masks profound usability and epistemic challenges.

• The steep learning curve is insurmountable for non-experts, locking out clinicians, policymakers, and many researchers who lack advanced programming skills.
• The complexity leads to inconsistent usage, mistakes, and poor reproducibility among less experienced users.
• Lack of standardization in code means identical analyses can yield subtly different results depending on user coding style and package versions.

• These packages provide few guardrails or warnings to prevent misuse of statistical models.
• Users can easily run inappropriate models (e.g., fixed effects vs. random effects) without clear guidance.
• There is no built-in methodological advisory system to flag data quality issues or model assumptions violations.

This can result in statistically invalid or misleading meta-analyses being produced and published.

• R packages operate as standalone statistical tools without integration into literature screening, data extraction, or bias assessment.
• Critical upstream processes remain manual and error-prone, undermining the quality of input data.
• There is no seamless connection with AI tools or databases to streamline evidence synthesis.

• Although R supports scripting, inconsistent documentation, versioning, and environment management often impair true reproducibility.
• Lack of standard templates or protocols leads to fragmented workflows and difficulties in peer review.
• Reproducible research requires additional tooling (e.g., R Markdown, Docker), increasing technical burden.

• The requirement for coding expertise effectively excludes non-technical researchers and clinicians.
• This perpetuates a digital divide, where only well-resourced teams can perform advanced meta-analyses.
• User errors from insufficient training may introduce bias and erode trust in published syntheses.

R meta-analysis packages like *meta* and *metafor* offer immense power in the hands of experts but are epistemic minefields for the uninitiated. Their complexity, lack of safeguards, and disconnected workflows risk producing irreproducible, invalid, or misleading results.

Recommendation: Only use these tools with rigorous statistical training, standardized protocols, and comprehensive workflow management. For broader accessibility and reliability, consider GUI-based or integrated platforms with built-in methodological guidance.

• ✅ Intuitive graphical interface, no coding required
• ✅ Supports rich statistical models, subgroup and sensitivity analyses
• ✅ Built-in methodological guidance reduces user errors
• ➕ Why better than R packages:

Accessible to non-programmers with powerful features and guided workflows

• ✅ Free and open-source GUI software
• ✅ Supports both frequentist and Bayesian meta-analysis methods
• ✅ Integrates with R backend but hides complexity from users
• ➕ Why better than R packages:

User-friendly interface with reproducible output ideal for teaching and exploration

• ✅ Automates literature screening, data extraction, and risk of bias assessment
• ✅ Reduces manual workload and potential human error
• ➕ Why better than R packages:

Automates upstream processes typically manual in R workflows

• ✅ Comprehensive platforms managing screening, extraction, bias assessment, and export
• ✅ Collaboration-friendly with version control and audit trails
• ➕ Why better than R packages:

Supports the entire systematic review lifecycle, not just meta-analysis

• Use CMA or JASP for powerful, code-free meta-analysis.
• Use Elicit and RobotReviewer to streamline data extraction and quality assessment.
• Use Covidence or DistillerSR to manage the entire systematic review process.
• Use R packages only if you have advanced coding skills and need full customization.