test: migrate stats/incr/mmpe to ULP-based assertions

[kgryte]

Resolves a part of #11352.

Description

What is the purpose of this pull request?

This pull request:

• migrates the tests for stats/incr/mmpe from relative tolerance (EPS-scaled) assertions to ULP-based assertions using @stdlib/assert/is-almost-same-value.

Details:

• test/test.js is the only test file in the package containing computed-tolerance assertions; no test.native.js exists.
• Replaced the abs/EPS delta <= tol comparison (and the associated delta/tol locals and requires) with t.strictEqual( isAlmostSameValue( actual, expected[i], N ), true, 'returns expected value' );.
• The package contains a single computed-tolerance assertion, whose ULP bound was tightened to the measured minimum by starting from N = 64 and lowering N until the smallest integer which still passes across every fixture value:
  • the accumulator function computes a moving mean percentage error incrementally: 1 ULP (passes at 1; fails at 0 with 3 failing assertions; previous tolerance was 1.0 * EPS).
• The suite was run twice at the final bound to confirm determinism: 98/98 passing on both runs.
• make lint-javascript-tests TESTS_FILTER=".*/stats/incr/mmpe/.*" is clean, and the only changed file is lib/node_modules/@stdlib/stats/incr/mmpe/test/test.js.

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, which studied prior ULP migrations in this repository (including the sibling package stats/incr/mpe), applied the same idiom here, and empirically searched for the minimum passing ULP bound.

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@stdlib-js/reviewers

🤖 Generated with Claude Code

https://claude.ai/code/session_01AmuE4qxqzr54jsqv5pq1rk

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[stdlib-bot]

Coverage Report

Package	Statements	Branches	Functions	Lines
stats/incr/mmpe	$\\color{green}142/142$ $\\color{green}+100.00\\%$	$\\color{green}8/8$ $\\color{green}+100.00\\%$	$\\color{green}2/2$ $\\color{green}+100.00\\%$	$\\color{green}142/142$ $\\color{green}+100.00\\%$

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