meta-analysis-psych - Conduct psychology meta-analyses in Python
Computes effect sizes, pools studies with random-effects models, evaluates heterogeneity and publication bias, creates plots, and runs sensitivity analyses.
Tags
Updated: 2026-10-08Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Compute Cohen’s d
- Compute Hedges’ g
- Pool random-effects estimates
- Quantify study heterogeneity
- Generate forest plots
- Generate funnel plots
- Test publication bias
- Apply trim-and-fill
- Apply PET-PEESE correction
- Analyze p-curves
- Run sensitivity analyses
Inputs
- Study-level effect sizes
- Group means and standard deviations
- Group sample sizes
- Test statistics
- Study labels
- Effect-size variances
- Plot output paths
Outputs
- Effect-size estimates
- Pooled meta-analysis results
- Heterogeneity statistics
- Publication-bias results
- Forest plots
- Funnel plots
- Sensitivity-analysis results
Requirements
- Python 3.11 environment
- numpy>=1.23
- scipy>=1.9
- pandas>=1.5
- matplotlib>=3.6