StatsPAI

v1.16.1 safe
3.0
Low Risk

Validation-tiered causal inference and econometrics workflows for Python

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all assessed categories, with no network calls, shell executions, or obfuscation patterns detected. The metadata risk is slightly elevated due to the maintainer's short author name and having only one package.

  • No network calls detected
  • Maintainer's author name is missing or very short
  • Only one package from the maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on statistical analysis.
  • Shell: No shell executions detected, consistent with an expected behavior for a stats-focused library.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing or very short and the author has only one package, indicating potential low activity or newness which could be suspicious.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: stanford.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository brycewang-stanford/statspai appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with StatsPAI 
Your task is to develop a mini-application that leverages the 'StatsPAI' Python package to perform advanced econometric analyses on economic datasets. This application will be targeted at economists and data scientists who need to conduct robust causal inference studies. Here’s a step-by-step guide on what your application should achieve and how it should utilize the 'StatsPAI' package:

1. **Project Overview**: Create a web-based application using Flask (or Django) that allows users to upload their own CSV datasets containing economic variables. The app should then perform a series of econometric analyses, including regression analysis, propensity score matching, and instrumental variable regressions.

2. **User Interface**: Design a simple yet intuitive UI where users can select which columns represent independent and dependent variables, specify control variables if any, and choose the type of econometric model they wish to apply (e.g., OLS, PSM, IV).

3. **Data Validation**: Implement data validation checks using StatsPAI to ensure the dataset meets the requirements for performing causal inference. This includes checking for multicollinearity, missing values, and outliers.

4. **Econometric Analysis**: Utilize StatsPAI’s core functionalities to perform the selected econometric analysis. Ensure the application provides visual outputs like scatter plots, histograms, and regression plots.

5. **Results Interpretation**: Display results in a user-friendly manner, including statistical significance, effect sizes, and confidence intervals. Provide explanations of the results based on the chosen econometric model.

6. **Report Generation**: Allow users to generate comprehensive reports summarizing the analysis, including tables, graphs, and textual interpretations of findings.

7. **Documentation and Support**: Include clear documentation on how to use the application and interpret the results. Also, provide a FAQ section addressing common issues and limitations.

**How to Use StatsPAI**: StatsPAI offers a suite of tools for validating data and performing econometric analyses. For instance, you can use StatsPAI’s data validation functions to check for necessary conditions before running any models. For econometric modeling, StatsPAI supports various methods such as OLS, PSM, and IV, each requiring specific inputs and configurations. Make sure to consult the official StatsPAI documentation for detailed usage instructions and examples.