ambric

v0.0.6 suspicious
4.0
Medium Risk

Augmented Mixed-frequency Bayesian Regional Inference with Constraints

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential typosquatting targeting 'fabric', and lacks maintainer information, raising concerns about its legitimacy and intent.

  • Potential typosquatting
  • Lack of maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is expected to communicate with external services.
  • Shell: The use of subprocess.run suggests potential execution of external commands, which could be legitimate if documented behavior, but warrants further investigation to ensure it's not being misused.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential typosquatting and lacks maintainer information, indicating a higher risk.
  • ⚠ Typosquatting target: fabric

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_integrated.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (12378 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 66 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • "{major}.{minor}.{patch}" subprocess.run( [ "uvx", "--from=toml-cli",
✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: fabric

  • "ambric" is 2 edit(s) from "fabric"
✓ Registered Email Domain

Email domain looks legitimate: anon.gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 ambric 
Your task is to develop a Python-based mini-application that leverages the 'ambric' package to perform regional economic forecasting using mixed-frequency data. This application will be targeted at economists and financial analysts who need to make informed decisions based on complex regional economic indicators. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up a virtual environment and installing the necessary packages, including 'ambric'. Make sure to include other dependencies such as pandas, numpy, and matplotlib for data manipulation and visualization.

2. **Data Collection**: Collect or simulate mixed-frequency regional economic data. This data should include various economic indicators like GDP, employment rates, inflation, etc., collected at different frequencies (e.g., monthly, quarterly, annually).

3. **Preprocessing**: Use pandas to clean and preprocess your dataset. Handle missing values, normalize the data if necessary, and ensure all data points are aligned properly for analysis.

4. **Modeling with Ambric**: Apply the 'ambric' package to model the relationships between different economic indicators across regions. Utilize its capabilities for Bayesian inference and mixed-frequency data handling to forecast future economic trends.

5. **Visualization**: Implement visualizations using matplotlib to display the original data alongside the forecasted outcomes. Highlight any significant trends or patterns identified by your model.

6. **Interactive Features**: Add interactive elements to your application using a web framework like Flask or Dash. Allow users to input their own data or select different regions to analyze, and provide real-time feedback based on the 'ambric' model's predictions.

7. **Documentation and Testing**: Ensure thorough documentation of your code and conduct rigorous testing to validate the accuracy and reliability of your forecasts. Include clear instructions on how to use the application and interpret the results.

**Suggested Features**:
- A user-friendly interface for uploading custom datasets.
- Detailed visualizations showing historical data and predicted trends.
- Real-time feedback and adjustments based on new data inputs.
- Comparative analysis tools to assess differences between regions.
- Comprehensive documentation explaining the methodology and assumptions behind the model.

💬 Discussion Feed

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