Automating Statistical Reproducibility: A Technical Deep Dive into reprun

Statistical reproducibility remains a critical challenge in research computing, with only 25% of papers passing reproducibility verification on first attempt. This whitepaper introduces reprun, a revolutionary tool for automating reproducibility verification in Stata workflows. By detecting and preventing common sources of instability, reprun enables researchers to ensure consistent, reproducible results across multiple executions.

Key Findings

• 75% of research papers fail initial reproducibility verification

• Approximately 50% of statistical code fails to execute successfully on first attempt

• Two primary sources account for most reproducibility issues:

  • Unset random number seeds

  • Unstable sort operations

Technical Architecture

Core Detection System

// Example reprun implementation
program define reprun
    version 18.0
    syntax anything [, verbose compact]
    
    // Store initial states
    local initial_rng = c(rngstate)
    local initial_sort = c(sortstate)
    
    // Execute code and track states
    while (!eof()) {
        local current_line = line[`i']
        
        // Execute line
        `current_line'
        
        // Check states
        if "`c(rngstate)'" != "`initial_rng'" {
            display "RNG state changed at line `i'"
        }
        
        if "`c(sortstate)'" != "`initial_sort'" {
            display "Sort state changed at line `i'"
        }
        
        // Generate checksum
        checksum
    }
end

Verification Components

• State Tracking

  • Random number generator state

  • Sort order state

  • Data checksums

• Execution Monitoring

  • Line-by-line state comparison

  • Sub-do-file handling

  • Loop processing

• Reporting System

  • Verbose mode for complete diagnostics

  • Compact mode for issue-targeted reporting

  • SMCL-compatible output logs

Implementation Framework

Phase 1: Initial Setup

// Configure reprun
reprun "analysis.do", verbose

// Alternative compact configuration
reprun "analysis.do", compact

Phase 2: Issue Detection

The system monitors for critical issues:

1. RNG State Changes

   • Unset seeds in simulations

   • Random sampling without seeds

2. Sort State Changes

   • Non-unique sorts

   • Unstable sort orders

3. Data Integrity

   • Many-to-many merges

   • Unstable data transformations

Phase 3: Resolution

// Example of stable sort implementation
sort id year, stable
bysort id: generate unique_id = _n

// Example of proper merge
merge 1:m id using "dataset.dta"

Best Practices

• Code Structure

  • Implement explicit sort orders

  • Set random seeds consistently

  • Use unique identifiers for merges

  
  

• Verification Process

  • Run verification before submission

  • Address all flagged issues

  • Document resolved instabilities

• Output Management

  • Use git-compatible output formats

  • Implement version control

  • Maintain audit trails

Case Studies

Case 1: Unstable Sorting

Problem: Non-unique sort variables causing inconsistent results Solution: Implementation of stable sort with unique identifiers Result: Consistent outputs across multiple runs

Case 2: Merge Instability

Problem: Many-to-many merges causing data inconsistencies Solution: Restructuring to one-to-many relationships Result: Stable and reproducible dataset creation

Future Development

• Enhanced Integration

  • CI/CD pipeline integration

  • Automated testing frameworks

  • Cloud deployment support

• Extended Capabilities

  • Advanced debugging tools

  • Custom verification rules

  • Cross-platform compatibility

Conclusion

reprun represents a significant advancement in statistical computing reproducibility. By automating the detection and resolution of common reproducibility issues, it enables researchers to ensure the reliability and consistency of their statistical analyses. The tool's implementation significantly reduces verification time while increasing the accuracy and completeness of reproducibility checks.

Recommendations

1. Implement reprun in all research workflows

2. Use verbose mode during development

3. Run compact verification before submission

4. Document all resolved instabilities

5. Maintain version control of all scripts

Looking Ahead

This integration opens up possibilities for:

• Automated code generation

• Natural language data analysis

• Interactive documentation

• AI-assisted statistical modeling

The intersection of statistical computing and AI is just beginning. This integration demonstrates how traditional statistical tools can be enhanced with modern AI capabilities, creating more powerful and intuitive workflows for data scientists and researchers.

Get Started

Install the OpenAI Python package

1. Set up your API key

2. Save the implementation in chatgpt.ado

3. Start using AI-powered commands in your Stata workflow

The full implementation is available as a Stata package, ready to enhance your statistical computing environment with the power of GPT-3.5.

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This whitepaper is based on research presented at the 2024 Stata Conference by Benjamin Daniels and Ankriti Singh.