🤖 AI Analysis

Final verdict: SAFE

The package presents minimal risks based on the analysis notes. However, the metadata risk slightly elevates the score due to the maintainer's limited presence.

Low risk in network, shell, obfuscation, and credential areas.
Metadata risk noted due to single package and lack of GitHub link.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communications.
Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package and no GitHub repository link, which may indicate a less experienced or inactive developer.

📦 Package Quality Overall: Low (2.4/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4074 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ 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

Email domain looks legitimate: 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 2.0

1 maintainer concern(s) found

Author "Romain Legrand" 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 alexandria-python

Create a Bayesian Time-Series Analysis Dashboard using the 'alexandria-python' package. This dashboard will allow users to upload their own time-series data and perform advanced Bayesian Vector Autoregression (BVAR) analysis. The app should have the following functionalities:

1. Data Upload: Users should be able to upload CSV files containing their time-series data.
2. Data Visualization: The app should display basic visualizations of the uploaded data (e.g., line charts).
3. Parameter Configuration: Allow users to configure parameters such as lag order and prior hyperparameters for the BVAR model.
4. Model Training: Use 'alexandria-python' to train a BVAR model on the uploaded dataset based on user-defined parameters.
5. Model Results: Display key results from the BVAR model including impulse response functions and forecast error variance decompositions.
6. Interactive Plots: Implement interactive plots to explore the model's predictions and confidence intervals.
7. Export Results: Provide an option to export the model results and visualizations as PDFs or PNG images.

The 'alexandria-python' package is utilized throughout the project for its capabilities in Bayesian time-series analysis. Specifically, it is used for training the BVAR model, generating forecasts, and calculating various diagnostics and visualizations. Ensure that the app is well-documented and includes explanations of the Bayesian methods used and how to interpret the results.