Is Statsmodels Safe?
Statsmodels — Nerq Trust Score 77.0/100 (B+ grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-04-03.
Yes, Statsmodels is safe to use. Statsmodels is a Python package with a Nerq Trust Score of 77.0/100 (B+), based on 3 independent data dimensions. It is recommended for production use. Security: 90/100. Popularity: 90/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-03. Machine-readable data (JSON).
Is Statsmodels safe?
YES — Statsmodels has a Nerq Trust Score of 77.0/100 (B+). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for production use — review the full report below for specific considerations.
What is Statsmodels's trust score?
Statsmodels has a Nerq Trust Score of 77.0/100, earning a B+ grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Statsmodels?
Statsmodels's strongest signal is security at 90/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Statsmodels and who maintains it?
| Author | statsmodels Developers |
| Category | pypi |
| Source | N/A |
Statsmodels Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Statsmodels
What is Statsmodels?
Statsmodels is a Python package — Statistical computations and models for Python.
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=statsmodels
Key Safety Concerns for Python packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Statsmodels has a Nerq Trust Score of 77/100 (B+) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Statsmodels has a Trust Score of 77/100 (B+).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Privacy | 80/100 |
| Reliability | 90/100 |
| Transparency | 85/100 |
| Maintenance | 60/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Statsmodels collect?
Statsmodels is a Python package maintained by statsmodels Developers. It receives approximately 9,204,237 weekly downloads. Licensed under BSD License.
As a development package, Statsmodels does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Full analysis: Statsmodels Privacy Report · Privacy review
Is Statsmodels secure?
Security score: 90/100. Statsmodels has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under BSD License, allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Statsmodels Security Report
Statsmodels Across Platforms
Same developer/company in other registries:
How we calculated this score
Statsmodels's trust score of 77.0/100 (B+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), privacy (80/100), reliability (90/100), transparency (85/100), maintenance (60/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on April 03, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
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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.