test: migrate stats/base/dists/planck/variance to ULP-based assertions

[kgryte]

Resolves a part of #11352.

Description

What is the purpose of this pull request?

This pull request:

• Migrates unit tests for @stdlib/stats/base/dists/planck/variance from relative tolerance checks (delta <= tol) to ULP-based assertions using @stdlib/assert/is-almost-same-value.

The converted assertions use a bound of 1 ULP in both test/test.js and test/test.native.js:

t.strictEqual( isAlmostSameValue( y, expected[ i ], 1 ), true, 'returns expected value' );

1 ULP is the measured minimum. The bound was tightened empirically, starting from 64 and walking down over the full 1000-element Python fixture set (64, 32, 16, 8, 4, 2, 1 all pass; 0 fails 103 of the 1000 cases). Independently computing ulpDifference( y, expected[ i ] ) across the fixtures gives a maximum of 1, attained at lambda = 13.503035075315534. The suite was run twice at the final bound to confirm determinism (no FMA/arch variability); both runs reported 1005/1005 passing.

Only the two test files are changed; no source, documentation, or fixture files were touched. The native tests skip in this environment, as the C addon is not built locally, so the 1 ULP bound for test.native.js mirrors the JavaScript result and the convention already established by the converted sibling packages (planck/mean, planck/stdev, planck/cdf).

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 written entirely by Claude Code, running unattended as a scheduled task. Claude selected the package, studied the idiom used by already-converted sibling packages, performed the conversion, and determined the minimum ULP bound empirically.

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@stdlib-js/reviewers

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Generated by Claude Code

[stdlib-bot]

Coverage Report

Package	Statements	Branches	Functions	Lines
stats/base/dists/planck/variance	$\\color{green}165/165$ $\\color{green}+100.00\\%$	$\\color{green}8/8$ $\\color{green}+100.00\\%$	$\\color{green}2/2$ $\\color{green}+100.00\\%$	$\\color{green}165/165$ $\\color{green}+100.00\\%$

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