Is Bayesian Safe?
Bayesian — Nerq Trust Score 52.0/100 (C- grade). Based on analysis of 2 trust dimensions, it is has notable safety concerns. Last updated: 2026-07-29.
Use Bayesian with some caution. Bayesian is a Python package with a Nerq Trust Score of 52.0/100 (C-), based on 3 independent data dimensions. Below the recommended threshold of 70. Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Bayesian safe?
CAUTION — Bayesian has a Nerq Trust Score of 52.0/100 (C-). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
What is Bayesian's trust score?
Bayesian has a Nerq Trust Score of 52.0/100, earning a C- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Bayesian?
Bayesian's strongest signal is security at 90/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Bayesian and who maintains it?
| Author | BoppreH |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Bayesian
What is Bayesian?
Bayesian is a Python package — Library and utility module for Bayesian reasoning.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=Bayesian
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Bayesian has a Nerq Trust Score of 52/100 (C-) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Bayesian has a Trust Score of 52/100 (C-).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.