Is Assayinganomalies Safe?

Assayinganomalies — Nerq Trust Score 48.2/100 (D grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-25

Assayinganomalies is a Python package with a Nerq Trust Score of 48.2/100 (D), based on 3 independent data dimensions. Last analyzed: 2026-03-25 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-25. Machine-readable data (JSON).

Safety Guide: Assayinganomalies

What is Assayinganomalies?

Assayinganomalies is a Python package — This library is a Python implementation of the MATLAB Toolkit that accompanies Novy-Marx and Velikov (2023) and is to be used for empirical academic asset pricing research, particularly focused on stu.

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=AssayingAnomalies

Key Safety Concerns for Python package

When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.

Measured Signals

Assayinganomalies has a Nerq Trust Score of 48/100 (D). This score is a composite of automated measurements of security, maintenance, community, and quality signals.

Key Takeaways

• Assayinganomalies has a Trust Score of 48/100 (D).
• 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.

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.