[kgryte]

Resolves a part of #11352.

Description

What is the purpose of this pull request?

This pull request:

• migrates stats/base/dists/chisquare/median tests from relative tolerance testing to ULP difference testing.
• replaces var delta/var tol tolerance math (previously tol = 25.0 * EPS * abs( expected[ i ] )) and the associated t.ok( delta <= tol, ... ) assertion with t.strictEqual( isAlmostSameValue( y, expected[ i ], 39 ), true, 'returns expected value' );.
• adds the @stdlib/assert/is-almost-same-value require and drops the now-unused @stdlib/math/base/special/abs and @stdlib/constants/float64/eps requires.

Only test/test.js is modified; the package has no test/test.native.js.

Final ULP constant: 39 for the test/test.js fixture loop (test/fixtures/julia/data.json, 50 values).

Measured minimum: 39 is the tightest bound. Running the package test suite at 38 fails one assertion (worst case: k = 0.20785020415982736, returned 0.0015467191018913129 vs. expected 0.0015467191018913044); 39 passes all 55 assertions. The suite was run twice at 39 to confirm the result is deterministic on this machine.

Related Issues

Does this pull request have any related issues?

This pull request has the following related issues:

• [RFC]: Migrate math/base/special packages from relative tolerance testing to ULP difference testing (tracking issue) #11352

Questions

Any questions for reviewers of this pull request?

No.

Other

Any other information relevant to this pull request? This may include screenshots, references, and/or implementation notes.

No.

Checklist

Please ensure the following tasks are completed before submitting this pull request.

• Read, understood, and followed the contributing guidelines.

AI Assistance

When authoring the changes proposed in this PR, did you use any kind of AI assistance?

• Yes
• No

If you answered "yes" above, how did you use AI assistance?

• Code generation (e.g., when writing an implementation or fixing a bug)
• Test/benchmark generation
• Documentation (including examples)
• Research and understanding

Disclosure

If you answered "yes" to using AI assistance, please provide a short disclosure indicating how you used AI assistance. This helps reviewers determine how much scrutiny to apply when reviewing your contribution. Example disclosures: "This PR was written primarily by Claude Code." or "I consulted ChatGPT to understand the codebase, but the proposed changes were fully authored manually by myself.".

This PR was authored by Claude Code running as an unattended scheduled task: it selected the package, studied already-migrated packages to mirror the project's idiom, applied the test changes, and searched for the minimum passing ULP bound by running the package test suite.

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Generated by Claude Code

[stdlib-bot]

Coverage Report

Package	Statements	Branches	Functions	Lines
stats/base/dists/chisquare/median	$\\color{green}100/100$ $\\color{green}+100.00\\%$	$\\color{green}3/3$ $\\color{green}+100.00\\%$	$\\color{green}1/1$ $\\color{green}+100.00\\%$	$\\color{green}100/100$ $\\color{green}+100.00\\%$

The above coverage report was generated for the changes in this PR.