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Durga: An R Package For Effect Size Estimation And Visualization.

Home
Durga: An R Package For Effect Size Estimation And Visualization.

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Durga: an R package for effect size estimation and visualization.

Md Kawsar Khan^1,2, Donald James McLean¹

¹School of Natural Sciences, Macquarie University, Sydney, NSW, Australia.

Journal of Evolutionary Biology

|June 6, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

The Durga R package enhances scientific communication by enabling easy estimation and visualization of effect sizes, moving beyond p-value reliance for clearer quantitative research findings.

Keywords:

p-value data analysis data visualization estimation statistics graphing software

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Area of Science:

Statistics
Data Visualization
Science Communication

Background:

Overreliance on p-values in statistical analysis hinders effective science communication.
Effect sizes are crucial for quantitative research clarity but often underutilized.
Current reporting of effect sizes is mainly limited to tables, lacking visual impact.

Purpose of the Study:

Introduce the Durga R package for estimating and plotting effect sizes.
Provide a user-friendly tool for both paired and unpaired group comparisons.
Facilitate more effective data analysis and science communication.

Main Methods:

Developed the Durga R package for statistical analysis.
Implemented functions to estimate unstandardized and standardized effect sizes.
Integrated bootstrapped confidence intervals for effect size estimation.
Combined effect size visualizations with traditional plotting methods.

Main Results:

Durga facilitates flexible estimation of effect sizes for various statistical methods.
The package offers extensive options for aesthetic and informative effect size plotting.
Demonstrated a workflow for estimating and plotting effect sizes using example datasets.

Conclusions:

Durga R package offers a powerful and accessible solution for effect size estimation and visualization.
Promotes enhanced clarity and effectiveness in scientific data analysis and communication.
Encourages a shift from p-value dependency towards robust effect size reporting.