🤖 AI Analysis

Final verdict: SUSPICIOUS

The package BayesProp v0.1.1.6 exhibits low risk in terms of network calls, shell execution, and obfuscation. However, the metadata quality and maintainer history are questionable, raising suspicion about its legitimacy and potential supply-chain risks.

Low effort in metadata and maintainer history
No significant detected risks except for metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal for most statistical packages like BayesProp unless it requires online updates or external data.
Shell: No shell execution patterns detected, reducing the risk of unauthorized system commands or backdoors.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low effort in metadata and maintainer history, which may indicate a less serious threat but still warrants caution.

🔬 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: nemetschek.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AVoss84/bayesProp appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 BayesProp

Develop a fully-functional mini-application named 'A/B Test Analyzer' using Python and the 'BayesProp' package. This application will enable users to perform Bayesian A/B testing on proportions, which is particularly useful in marketing and web development scenarios where comparing two versions of a webpage or advertisement is necessary. Here’s a detailed step-by-step guide on how to develop this application:

1. **Project Setup**: Start by setting up a virtual environment for your project and installing the required packages including 'BayesProp'. Ensure you have Python 3.8 or later installed.
2. **User Interface Design**: Design a simple yet intuitive user interface using a Python library like Tkinter or PyQt5. The UI should allow users to input data such as the number of successes and total trials for both groups being compared (Group A and Group B).
3. **Data Input Handling**: Implement functionality within the application to accept user inputs for the number of successes and total trials for each group. Validate these inputs to ensure they are numeric and positive.
4. **Bayesian Analysis with BayesProp**: Utilize the 'BayesProp' package to perform Bayesian A/B testing on the provided data. This involves calculating posterior distributions for the success rates of both groups and determining the probability that one group outperforms the other.
5. **Result Visualization**: Display the results of the A/B test in a visually appealing manner. Use libraries such as Matplotlib or Seaborn to plot the posterior distributions and highlight the probability of superiority between the groups.
6. **Additional Features**: Consider adding extra features such as saving the results to a CSV file, allowing users to upload their own datasets, or even integrating real-time data analysis capabilities if the application is intended for live A/B testing scenarios.
7. **Testing and Documentation**: Thoroughly test the application to ensure all functionalities work as expected. Document the setup process, usage instructions, and any troubleshooting tips in a README file included in the project directory.

By following these steps, you'll create a powerful tool for anyone looking to conduct Bayesian A/B testing efficiently and effectively.