argonx

v0.1.5 safe
3.0
Low Risk

Bayesian decision engine for A/B testing

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscation techniques observed. The metadata suggests it may be a newer project with limited activity.

  • Low network and shell risk
  • No signs of obfuscation or credential harvesting
  • Single package from maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no direct system command risks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a single package and lacks PyPI classifiers, indicating potential low effort or newness.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_math.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8395 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

  • 112 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

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ 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 "Souradeep Roy" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with argonx 
Create a fully-functional mini-application called 'A/B Test Analyzer' that leverages the 'argonx' Python package for conducting Bayesian A/B testing analysis. This application will allow users to input data from two different groups (A and B) and perform real-time A/B testing analysis to determine which group performs better based on the Bayesian decision engine provided by argonx.

### Features:
- **User Interface**: Develop a simple yet intuitive user interface using Streamlit or Flask for data input and visualization.
- **Data Input**: Users should be able to upload CSV files containing the performance metrics of both groups (e.g., conversion rates).
- **Real-Time Analysis**: Implement real-time analysis capabilities where the application updates the analysis as more data is added.
- **Visualization**: Include visualizations such as bar charts and line graphs to compare the performance of groups A and B over time.
- **Decision Engine**: Utilize the Bayesian decision engine from 'argonx' to calculate posterior probabilities and make informed decisions about which group is performing better.
- **Report Generation**: Allow users to generate PDF reports summarizing the A/B test results, including key statistics and visualizations.

### How to Use 'argonx':
- Import the necessary modules from 'argonx' to set up the Bayesian model for A/B testing.
- Define the prior distributions for the conversion rates of both groups.
- Use the 'argonx' functions to update the model with new data as it comes in from the uploaded CSV files.
- Extract posterior probabilities to determine the likelihood that one group outperforms the other.
- Integrate these functionalities into your application to provide users with real-time insights and actionable recommendations based on the Bayesian analysis.

💬 Discussion Feed

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