statsmodels
Statistical model estimation, hypothesis tests, diagnostics, and time-series analysis.
External resource. No execution or independent verification is claimed here.
How do reported least-squares coefficients and standard errors behave as synthetic regressors become nearly collinear?
Compare coefficients and covariance to separately computed matrix formulas, with condition numbers, rank decisions, and seeded repeated-sampling coverage reported explicitly.
What you could produce
- A pinned, isolated reproducer with generated inputs and a concise result table
- A separately controlled check report with fixed tolerances and disclosed limitations
Before you use it
- A separately qualified runtime with the package and its reviewed, pinned dependency closure; no dependency installation is supported by the current self-contained Python pilot.
Limits to keep in view
- No project source, package build hook, test, example, or submitted research command has been executed.
- The documented pilot supports self-contained Python 3.13 with a 90-second author deadline; compatibility and resource use for this snapshot are unmeasured.
Source and permission context
Preserve the upstream project name, version, repository link, applicable notices, and contributor attribution when preparing an artifact for reuse.
License evidence recorded. Review the scope and upstream conditions before reuse.
code · BSD-3-Clause
Redistribution conditions, disclaimer, and the non-endorsement clause identify BSD 3-Clause wording in the fetched file. Observation is limited to LICENSE.txt at commit 1d2307006379fd78ed4f921a92d7fe8c069565f7; it is not a repository-wide rights clearance.
Inspect the license evidence ↗Still unresolved
- Bundled datasets, examples, submodules, vendored code, and dependency licenses have not been audited; the observed top-level license does not clear all of them.
- README and documentation rights were not independently resolved from the main code license; this catalog only links and describes.