[RFC]: Develop native C implementations for probability distribution functions

[rautelaKamal]

Full name

Kamal Singh Rautela

University status

Yes

University name

Bennett University

University program

B.Tech in Computer Science

Expected graduation

2026

Short biography

I’m a final-year Computer Science student at Bennett University who loves building end-to-end software. I started out doing a lot of fast-paced, product-driven development. Between competing in college hackathons and leading our campus coding club, I got used to prototyping and shipping things quickly. That’s how I ended up building an Excalidraw-style collaborative web canvas and an AI Chrome extension that gives contextual definitions while you read.

But getting involved in open-source completely changed how I approach coding. Instead of just trying to "make things work," I started caring about making things mathematically correct and performant. Working on stdlib has been a massive learning curve in the best way possible. Moving from high-level web dev to writing memory-safe C addons, handling log-space arithmetic, and validating ULP-level accuracy taught me what real engineering rigor looks like. I’m applying for GSoC because I want to spend my summer combining my ability to ship code quickly with the deep technical standards required by the stdlib ecosystem.

Timezone

Indian Standard Time (UTC +5:30)

Contact details

email: kamalrautela5@gmail.com, github: rautelaKamal, linkedin: https://www.linkedin.com/in/kamal-rautela-0495b8245/

Platform

Mac

Editor

VS Code — I use it for its built-in Git integration, C/JavaScript debugging, and extension support for linting and testing.

Programming experience

I have practical experience with JavaScript, C, Node.js, Python, and Rust. Key projects:

• agnix Core Contribution (PR #318): Authored a feature addition in Rust for the agent-sh/agnix project, extending the root type system (LayerType enum) to support new local categorization environments.
• stdlib-numerical-demo: A comprehensive web + CLI application showcasing stdlib's numerical computing capabilities — ULP accuracy analysis across 20,000+ test points, IEEE 754 edge-case testing (24 automated tests), interactive function plotting (22 functions), and performance benchmarking. Uses 30+ stdlib packages including special functions, statistical distributions, assertions, and constants.
• F-Distribution PDF C-Port (PR #11201): End-to-end native C addon using log-space computation with betaln, handling multiple edge cases (d1<2, d1=2, d1>2 at x=0), achieving ~1e-15 relative errors against Julia & Boost C++ reference fixtures.
• 59 merged PRs into stdlib spanning blas/, stats/, and math/ — demonstrating consistent contribution velocity and deep familiarity with stdlib conventions.

JavaScript experience

I started with JavaScript through web development and quickly moved to its use in numerical computing contexts. Working with stdlib gave me appreciation for JavaScript's nuances — from typed arrays and their role in scientific computing to the subtleties of IEEE 754 floating-point arithmetic (e.g., distinguishing +0 from -0, handling NaN propagation). I have significant experience with Node.js module patterns, especially stdlib's CommonJS structure with lazy require patterns. I particularly value JavaScript's flexibility for prototyping mathematical functions before porting them to C for performance.

Node.js experience

My Node.js experience is deeply tied to stdlib's ecosystem. I work with N-API for building native C addons, require-based module resolution, and the project's test/benchmark infrastructure. I understand how stdlib's build system works — from binding.gyp and include.gypi for native compilation to manifest.json for dependency tracking. I've built native addons that expose C functions through N-API, including proper error handling and type coercion between JavaScript Number values and C double precision.

C/Fortran experience

My C experience comes primarily from implementing native addons for stdlib's probability distribution functions. I have hands-on experience with:

• Implementing mathematical functions in C using log-space arithmetic to avoid overflow (e.g., F-distribution PDF)
• Writing N-API addon wrappers (addon.c) that interface between JavaScript and C
• Using stdlib's internal C primitives (stdlib_base_ln, stdlib_base_betaln, etc.)
• Compiler-specific considerations for node-gyp builds
• IEEE 754 edge-case handling at the C level (checking for NaN, infinity, zero)

I do not have Fortran experience but am willing to learn if needed.

Interest in stdlib

stdlib is where I made my first open-source contributions, and it continues to be the project I'm most invested in. What draws me to stdlib is its uncompromising focus on numerical correctness — every function needs to handle edge cases properly, achieve ULP-level accuracy against reference implementations, and conform to IEEE 754 semantics. This level of rigor is rare in the JavaScript ecosystem and is what makes stdlib invaluable for scientific computing.

I'm particularly interested in the C-port effort because it directly addresses stdlib's performance bottleneck: JavaScript-only implementations cannot match the throughput needed for production numerical workloads. By providing native C addons with N-API bindings, we can offer users the same mathematical guarantees at significantly higher performance — without changing the public API at all.

Version control

Yes

Contributions to stdlib

Merged Work (59 PRs)

My contributions span three categories:

1. Native C Implementations:

   • #10196: C implementation for math/base/special/heaviside — Merged

2. Systematic Benchmark Refactoring (57 PRs):
   Comprehensive refactoring of benchmark files across blas/, stats/, and math/ ecosystems to use modern string interpolation and comply with CI/CD linting. Examples:

   • #11220, #11219, #11218, #11208, #11207 (BLAS resolver packages)
   • #10832 through #10404 (stats, blas, math packages)

3. Maintenance:

   • #10406: Fixed EditorConfig lint errors across test fixtures

Open C-Port PRs (7 PRs — all CI clean, awaiting review):

• #11201: F-Distribution PDF — Native C addon with log-space computation (Proof of Concept)
• #10883: Lognormal CDF C implementation
• #10882: Lognormal LogCDF C implementation
• #10881: Lognormal LogPDF C implementation
• #10874: Poisson Entropy C implementation
• #10719: Erlang LogPDF C implementation
• #10554: Erlang PDF C implementation

Code Reviews & Community Engagement:

• #10360: Technical validation for Hypergeometric kurtosis
• #10805: Pinpointing ESLint fixes to unblock a contributor
• #10806: Identifying integer-overflow edge cases in C implementations

stdlib showcase

stdlib Numerical Accuracy Explorer — Source code: https://github.com/rautelaKamal/stdlib-numerical-demo

The showcase uses 30+ stdlib packages across core math, statistical distributions, assertions, and constants. It includes:

• Interactive web explorer with real-time function plotting (22 functions), ULP accuracy scatter analysis, IEEE 754 edge-case table, and performance benchmarks
• CLI tool with accuracy comparisons across 20,000+ test points, 24 automated edge-case tests, and ASCII visualizations
• Packages used include: math/base/special/exp, ln, sqrt, erf, erfc, erfcx, log1p, heaviside, abs, floor, round, max, min, stats/base/dists/lognormal/cdf, lognormal/logcdf, lognormal/logpdf, poisson/entropy, erlang/pdf, math/base/assert/is-nan, is-infinite, is-positive-zero, is-negative-zero, constants/float64/eps, pinf, ninf, max, smallest-normal, array/linspace, and more.

Goals

The goal of this project is to develop native C implementations with N-API bindings for probability distribution functions in stats/base/dists. Each C-port will include:

• Pure C implementation (src/main.c) using stdlib's internal math primitives
• C header file (include/stdlib/stats/base/dists/<dist>/<func>.h)
• N-API addon wrapper (src/addon.c)
• Build configuration (binding.gyp, include.gypi, manifest.json)
• Native JavaScript dispatcher (lib/native.js)
• Native benchmarks (benchmark/benchmark.native.js)
• Native tests (test/test.native.js) with exact parity against JS reference
• Log-space computation where numerically necessary to prevent overflow/underflow

Target distributions (prioritized by unblocked dependencies first):

Distribution	Sub-functions	RFC Tracking	Status
Fréchet	pdf, cdf, quantile, mean, variance, skewness, kurtosis, entropy	#3564	Unblocked
Laplace	pdf, cdf, quantile, logpdf, logcdf, mgf, entropy, mean, variance, skewness, kurtosis	#3691	Unblocked
Logistic	pdf, cdf, quantile, logpdf, logcdf, entropy, mean, variance, skewness, kurtosis	#3692	Unblocked
Rayleigh	pdf, cdf, quantile, logpdf, logcdf, entropy, mean, variance, skewness, kurtosis	#3687	Unblocked
Student's t	pdf, cdf, quantile, mean, variance, skewness, kurtosis, entropy	#3852	Partially blocked (cdf/quantile need betainc)
Binomial	pmf, cdf, quantile, mean, variance, skewness, kurtosis, entropy	#3464	Blocked (betainc C impl needed)

The total estimated deliverable count across these 6 distribution families is ~50-60 individual C-port packages.

Why this project?

I chose this project because it aligns perfectly with the work I've already been doing and demonstrates the most impactful path forward for stdlib's numerical computing capabilities.

1. I've already proven the workflow end-to-end. My F-Distribution PDF C-port (#11201) is one of the more complex distributions to implement — it requires log-space computation using betaln to avoid overflow, handles three distinct edge cases at x=0 depending on d1 values, and achieves ~1e-15 relative errors against Julia and Boost C++ reference fixtures. This wasn't a simple transliteration — it required understanding the numerical analysis behind the formula. By contrast, distributions like Laplace and Rayleigh have simple closed-form expressions (e.g., (1/2b)·exp(-|x-μ|/b)), so the F-distribution PoC demonstrates that I can handle the hardest class of implementations, not just the straightforward ones.

2. Unblocking downstream work. Many higher-level stdlib APIs (strided hypothesis tests, statistical aggregations) will eventually need native C performance from the distribution layer. Each C-port I complete moves the ecosystem closer to full native performance parity.

3. I understand the dependency chain. Several distributions (Beta, Binomial, Student's t quantile) are blocked by missing C implementations of betainc, kernel-betaincinv, and gammaincinv. My timeline accounts for this by front-loading unblocked distributions and contributing to special function C-ports during community bonding. I will not schedule blocked distributions for weeks where their dependencies aren't yet complete.

Qualifications

• 7 working C-port PRs (all passing CI) demonstrate I can execute the full workflow: C implementation → N-API addon → build config → native tests → native benchmarks
• 59 merged PRs show consistency, reliability, and deep familiarity with stdlib's codebase conventions
• F-Distribution PoC proves I can handle numerically complex implementations (log-space arithmetic, multi-branch edge cases, ULP-level accuracy validation)
• stdlib showcase demonstrates understanding of numerical computing concepts (ULP analysis, IEEE 754 semantics, catastrophic cancellation)
• Code review participation shows I can evaluate others' implementations, catch edge-case errors, and contribute to the review process

Prior art

The C implementations will reference and validate against:

• Julia Distributions.jl: Primary reference for mathematical formulas and for generating test fixtures via runner.jl files
• Boost C++ Math Library: Reference for numerically stable algorithms, especially continued fraction expansions and asymptotic approximations
• SciPy.stats: Secondary reference; will use runner.py for fixture generation when Julia doesn't provide a function
• Wikipedia: Mathematical definitions, parameter constraints, and domain/range specifications
• Existing stdlib JS implementations: Each C-port starts from the corresponding JavaScript implementation in stats/base/dists/*/lib/

Commitment

I do not have significant external commitments during the summer period and can dedicate 35-40 hours per week to the project. I am comfortable working additional hours during critical review cycles to keep momentum. My timezone (IST, UTC+5:30) has good overlap with maintainer availability.

Schedule

Assuming a 12 week schedule,

• Community Bonding Period (3 weeks):

  • Finalize and get my 7 open C-port PRs merged (Lognormal suite, Erlang suite, Poisson entropy, F-Distribution PDF)
  • Contribute to special function C-port efforts by collaborating with existing PR authors: specifically #10279 (kernel-betainc by @nirmaljb) and #4037 (betainc by @Neerajpathak07) — these are the key blockers for Student's t and Binomial distributions
  • Study existing reference implementations (Julia, Boost) for all target distributions
  • Set up Valgrind/ASan validation workflow for all native addon builds to catch memory safety issues early

• Week 1: Begin Fréchet distribution — implement pdf, cdf, quantile C-ports. Fréchet has closed-form expressions making it an ideal starting distribution.

• Week 2: Complete Fréchet — implement mean, variance, skewness, kurtosis, entropy. Submit all Fréchet PRs for review.

• Week 3: Begin Laplace distribution — implement pdf, cdf, quantile, logpdf, logcdf.

• Week 4: Complete Laplace — implement mgf, entropy, mean, variance, skewness, kurtosis. Address any review feedback from Weeks 1-2.

• Week 5: Begin Logistic distribution — implement pdf, cdf, quantile, logpdf, logcdf. Address review feedback from Weeks 3-4.

• Week 6 (Midterm): Complete Logistic — entropy, mean, variance, skewness, kurtosis. Prepare midterm submission. At this point: 3 complete distribution families (~30 packages).

• Week 7: Begin Rayleigh distribution — implement pdf, cdf, quantile, logpdf, logcdf.

• Week 8: Complete Rayleigh — entropy, mean, variance, skewness, kurtosis. Run Valgrind/ASan on all submitted C implementations to validate memory safety.

• Week 9: Begin Student's t distribution — implement pdf, mean, variance, skewness, kurtosis, entropy. If betainc C implementation is available, also begin cdf.

• Week 10: Continue Student's t — implement cdf, quantile if dependencies are available. If blocked, use this as buffer/review week.

• Week 11: Buffer week — address all pending review feedback across all open branches.

• Week 12: Stretch goals — If ahead of schedule, begin Binomial pmf, mean, variance (unblocked sub-functions) or implement dagum.

• Final Week: Final polishing — ensure all submitted PRs have addressed review feedback. Documentation review. Finalize ULP testing suite. Submit final GSoC project.

Stretch Goals (if ahead of schedule):

• Begin Binomial distribution (if betainc dependency is resolved)
• Contribute C implementations for betaprime or dagum distributions
• Explore ULP-difference-based testing as default for native addon tests

Notes:

• The community bonding period is a 3 week period built into GSoC to help you get to know the project community and participate in project discussion. This is an opportunity for you to setup your local development environment, learn how the project's source control works, refine your project plan, read any necessary documentation, and otherwise prepare to execute on your project project proposal.
• Usually, even week 1 deliverables include some code.
• By week 6, you need enough done at this point for your mentor to evaluate your progress and pass you. Usually, you want to be a bit more than halfway done.
• By week 11, you may want to "code freeze" and focus on completing any tests and/or documentation.
• During the final week, you'll be submitting your project.

Related issues

GSoC Idea: #2 — implement a broader range of statistical distributions

Tracking RFCs for target distributions:

• #3564 (Fréchet)
• #3691 (Laplace)
• #3692 (Logistic)
• #3687 (Rayleigh)
• #3852 (Student's t)
• #3464 (Binomial)

Checklist

• I have read and understood the Code of Conduct.
• I have read and understood the application materials found in this repository.
• I understand that plagiarism will not be tolerated, and I have authored this application in my own words.
• I have read and understood the patch requirement which is necessary for my application to be considered for acceptance.
• I have read and understood the stdlib showcase requirement which is necessary for my application to be considered for acceptance.
• The issue name begins with [RFC]: and succinctly describes your proposal.
• I understand that, in order to apply to be a GSoC contributor, I must submit my final application to https://summerofcode.withgoogle.com/ before the submission deadline.