Use EPS instead of 0.1 in C benchmark files

[HWKDS]

Replace hardcoded 0.1 constant with STDLIB_CONSTANT_FLOAT64_EPS in statistical distribution benchmark files for non-zero positive lower bounds.

Ref: #10174

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Resolves #10174.

Description

What is the purpose of this pull request?

This pull request:

• Replaces hardcoded 0.1 constant with STDLIB_CONSTANT_FLOAT64_EPS in C benchmark files for statistical distributions
• Adds #include "stdlib/constants/float64/eps.h" to affected benchmark files
• Updates 47 benchmark files across 13 statistical distributions (Chi, Chi-square, Inverse Gamma, Kumaraswamy, Levy, Lognormal, Negative Binomial, Normal, Pareto Type 1, Planck, Poisson, Student's t, and Weibull)

Related Issues

Does this pull request have any related issues?

This pull request has the following related issues:

• [RFC] Use EPS instead of 0.1 as lower limit in non-zero positive constants present in C benchmark files #10174

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 changes follow a consistent pattern across all files:

• Added the EPS header include after other stdlib includes
• Replaced random_uniform(0.1, max) with random_uniform(STDLIB_CONSTANT_FLOAT64_EPS, max) for distribution parameters that must be strictly positive
• Did not modify parameters that can legitimately be zero (e.g., location parameters)

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.".

I consulted GitHub Copilot to understand the issue requirements, contributing guidelines, and Git workflow. The code changes were implemented manually by me following the RFC specifications. Copilot assisted with documentation and PR formatting.

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[HWKDS]

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[HWKDS]

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[kgryte]

See comment in #10174 (comment).

[HWKDS]

Sure, my bad> See comment in #10174 (comment).