test: migrate stats/base/dists/erlang/mgf to ULP-based assertions

[Planeshifter]

Resolves a part of #11352.

Description

What is the purpose of this pull request?

This pull request:

• migrates stats/base/dists/erlang/mgf tests (test.mgf.js, test.factory.js, test.native.js) from EPS-scaled tolerance checks to isAlmostSameValue ULP-based assertions.
• uses a named ULP constant per test, tightened to the measured minimum that passes over the full fixture set (verified deterministic over two runs; ULP - 1 fails):
  • large k and lambda (both_large): 734
  • large k (large_shape): 2050
  • large lambda (large_rate): 851

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.

The bounds were measured against the JavaScript implementation (main and factory). The native addon could not be built in the authoring environment, so the same bounds were applied to test.native.js without being run. The native implementation uses the same pow algorithm, but please verify. eslint could also not be run (dependency install failed), though the files pass node --check.

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 primarily by Claude Code, running as an automated scheduled routine.

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@stdlib-js/reviewers

🤖 Generated with Claude Code

https://claude.ai/code/session_01KwbNRSFvk4SqKZUY9iZBX7

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Generated by Claude Code

[stdlib-bot]

Coverage Report

Package	Statements	Branches	Functions	Lines
stats/base/dists/erlang/mgf	$\\color{green}312/312$ $\\color{green}+0.00\\%$	$\\color{green}21/21$ $\\color{green}+0.00\\%$	$\\color{green}4/4$ $\\color{green}+0.00\\%$	$\\color{green}312/312$ $\\color{green}+0.00\\%$

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

[kgryte]

LGTM.

@Planeshifter Fairly consistently, your Claude routines are much more likely to make mistakes and necessitate PR clean-up. Not clear why.