Before using this in research
The goal is not to adopt another tool. The goal is to reduce verified research time without weakening the evidence trail.
Best for
Biomedical, medical, public health, psychology, and academic researchers who use R for statistical modeling and need a more organized workflow for interpreting and reporting results.
First step
Start with a fitted model in R, then use the relevant easystats packages—such as parameters, performance, effectsize, bayestestR, and see—to inspect, summarize, and visualize the results.
A safer workflow
- 1Use performance to assess model diagnostics, assumptions, fit indices, and potential issues before interpreting results.
- 2Use parameters to extract and format regression coefficients, confidence intervals, p values, and model summaries for clearer reporting.
- 3Use effectsize to calculate standardized effects and other effect size measures that help communicate practical or clinical relevance.
- 4Use bayestestR and see when appropriate to interpret Bayesian models and create diagnostic or explanatory visualizations for manuscripts and reports.
Watch-outs
- easystats helps organize statistical interpretation, but it does not replace study design, model selection, or domain-specific statistical judgment.
- Check whether each package supports your model class and whether the default options match your journal, field, or analysis plan.
- Do not report formatted outputs automatically; verify assumptions, coding decisions, missing data handling, and clinical meaning before publication.
Evidence checks
- Confirm that model diagnostics support the chosen regression or statistical model before presenting coefficients or predictions.
- Report effect sizes with uncertainty intervals where possible, not only p values, to support biomedical interpretation.
- For Bayesian analyses, clearly state priors, posterior summaries, uncertainty intervals, and the decision criteria used for interpretation.
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