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robustness-battery

Test statistical specifications with alternative samples, controls, estimators, standard errors, and functional forms.

Updated Mar 28, 2026
Search Tags:#statistical analysis#robustness testing#robustness checks#empirical research#statistical specification
One-click install
npx skills add https://github.com/bgpopescu/popescu_claude --skill robustness-battery
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: robustness-battery
Source: https://github.com/bgpopescu/popescu_claude/tree/main/.claude/skills/robustness-battery
Command: npx skills add https://github.com/bgpopescu/popescu_claude --skill robustness-battery

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires R, Python, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps researchers ensure the robustness of their statistical specifications by systematically testing for sensitivity to reasonable alternatives.

Core Features & Use Cases

  • Systematic Robustness Checks: Tests alternative samples, controls, estimators, SEs, and functional forms.
  • Summary Table and Specification Curve: Produces a comprehensive report with a summary table and a specification curve for analysis.
  • Use Case: Suppose you have a regression specification and want to ensure its robustness. Use this Skill to run a battery of robustness checks on your specification and generate a report that identifies any potential issues.

Quick Start

Run the robustness-battery skill on your main analysis script using the command: 'robustness-battery [main-script-path] [language: R|Python]'.

Frequently Asked Questions about robustness-battery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run robustness checks on a regression specification in R or Python?▼

To run robustness checks, execute the tool via command line by passing your main script path and language choice. It systematically applies alternative samples, controls, estimators, and functional forms to test your statistical specification.

What is a specification curve in empirical research?▼

A specification curve is a visualization generated by this tool to map how coefficient estimates change across different reasonable analytical choices. It accompanies a summary table to help you analyze the sensitivity of your statistical specifications.

Can I use this robustness battery tool for social science empirical research?▼

Yes, this tool is specifically designed for empirical social science research. It requires familiarity with statistical methods and R or Python to execute the scripts that generate the robustness summary report.

What is the best way to test sensitivity to alternative estimators and standard errors?▼

The best way to test sensitivity is using a systematic battery that applies alternative samples, controls, estimators, standard errors, and functional forms to your specification. This identifies potential issues by generating a comprehensive summary report.

Do I need to write my own scripts for statistical specification testing?▼

You need an existing main analysis script in R or Python to pass as input. The tool then systematically applies the robustness testing battery to your script and outputs a specification curve and summary report for analysis.

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