fix: use log1p for small p in stats/base/dists/exponential/quantile

[Abhist17]

Description

What is the purpose of this pull request?

This pull request:

• uses -log1p( -p ) instead of -ln( 1.0 - p ) in stats/base/dists/exponential/quantile (JS, factory and C).

Forming 1 - p in double precision rounds away the low-order bits of p. For p < 2^-53 it rounds to 1, so the function returns 0:

var quantile = require( '@stdlib/stats/base/dists/exponential/quantile' );

quantile( 1.0e-20, 1.0 );
// develop: 0.0
// this PR: 1.0e-20

This is also why the existing R fixture needed a 172-ULP tolerance. R's qexp uses -log1p(-p) / rate, so with this change the same fixture passes at 2 ULP.

Tests:

• The tolerance on the R fixture goes from 172 to 2 ULP in test.quantile.js, test.factory.js and test.native.js.
• There's a new test/fixtures/julia/small_p.json with p log-spaced over [1e-300, 0.1]. runner.jl evaluates the textbook formula -ln(1-p) / lambda in 2048-bit BigFloat. It passes at 1 ULP in JS and native.
• On develop, 1,085 of the 2,013 assertions in test.quantile.js fail. With this change they all pass.

Related Issues

Does this pull request have any related issues?

None. Same bug as #15762 (weibull/quantile).

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 primarily by Claude Code. I found the bug by reading the stats/base/dists quantile functions for 1 - p cancellation.

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@stdlib-js/reviewers

[stdlib-bot]

Coverage Report

Package	Statements	Branches	Functions	Lines
stats/base/dists/exponential/quantile	$\\color{green}283/283$ $\\color{green}+100.00\\%$	$\\color{green}23/23$ $\\color{green}+100.00\\%$	$\\color{green}4/4$ $\\color{green}+100.00\\%$	$\\color{green}283/283$ $\\color{green}+100.00\\%$

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

[0PrashantYadav0]

LGTM