🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has significant obfuscation risks due to the use of eval() with dynamic input, which could potentially enable code injection. However, there are no clear signs of malicious activities such as network or shell risks, and it lacks credentials handling issues.

High obfuscation risk due to eval() usage
Low activity and new maintainer metadata suggest potential risk

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
Obfuscation: The use of eval() with dynamic input is highly suspicious and can be used for code injection, indicating potential malicious intent.
Credentials: No direct signs of credential harvesting detected.
Metadata: Low activity and new maintainer suggest potential risk, but no clear malicious indicators.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

: return df.apply(eval(Mapping),axis=1) def read_and_index(fn,indices,dvcstrea
try: idx,op = eval(v.Grouping) groups = d[v.Output].groupby(IND

✓ 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: berkeley.edu

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Ethan Ligon" 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 CFEDemands

Create a mini-application named 'FrischDemandAnalyzer' using Python that leverages the 'CFEDemands' package to estimate and compute Constant Frisch Elasticity (CFE) demands. This application should serve as a tool for economists, researchers, and students to analyze consumer behavior under varying price conditions. Here’s a step-by-step guide on how to build this application:

1. **Project Setup**: Start by setting up your Python environment and installing necessary packages such as 'CFEDemands', pandas, numpy, and matplotlib for data manipulation and visualization.
2. **Data Input**: Design a user-friendly interface where users can input or upload datasets containing information about different goods, their prices, quantities consumed, and other relevant economic variables. Ensure the data is clean and ready for analysis.
3. **Model Estimation**: Utilize the 'CFEDemands' package to estimate CFE demands based on the provided data. Implement functions to handle various types of data inputs and ensure the model can adapt to different economic scenarios.
4. **Analysis Tools**: Develop tools within the application to analyze the estimated demands. This includes calculating elasticity values, identifying substitution patterns between goods, and forecasting demand changes under hypothetical price alterations.
5. **Visualization**: Use matplotlib to visualize the results. Create graphs showing the relationship between prices and quantities demanded, elasticity curves, and any other insights derived from the analysis.
6. **Report Generation**: Allow users to generate comprehensive reports summarizing the findings of their analysis. These reports should include key metrics, visualizations, and interpretations of the data.
7. **User Interface**: Finally, design an intuitive and interactive user interface using frameworks like PyQt or Streamlit to make the application accessible and user-friendly.

Suggested Features:
- Real-time data validation and error handling for user inputs.
- Multiple models for different types of economic analyses.
- Advanced customization options for visualizations and reports.
- Integration with cloud services for data storage and sharing.

By following these steps and incorporating these features, you will create a powerful yet easy-to-use tool for analyzing consumer demand patterns using the 'CFEDemands' package.