Is Bayesase Safe?
Bayesase — Nerq Trust Score 53.8/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Bayesase is a Python package with a Nerq Trust Score of 53.8/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-20 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 Bayesase safe?
Trust Score Breakdown — Bayesase has a Nerq Trust Score of 53.8/100 (C-). Measured across 2 independent trust signals (as of 2026-03-20).
What is Bayesase's trust score?
Bayesase has a Nerq Trust Score of 53.8/100, 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 Bayesase?
Bayesase's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Bayesase and who maintains it?
| Author | McIntyre Lab, Universify of Florida |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Bayesase
What is Bayesase?
Bayesase is a Python package — Bayesian analysis of allele specific expression.
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=BayesASE
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Bayesase has a Nerq Trust Score of 54/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Bayesase has a Trust Score of 54/100 (C-).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.