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Bachelor of Technology in Computer Science & Engineering
Expected graduation
May 1, 2028
Short biography
I am currently a sophomore undergraduate and will be entering my penultimate year of study by the time the coding period starts. My technical skill set includes programming languages like C/C++, JavaScript/TypeScript, Python, Rust, Java, and various modern web technologies used for full-stack development. Apart from these, I am also skilled in using Git and Docker.
In my freshman year, I had two very important courses on mathematics- Computational Mathematics and Calculus & Matrices, which helped me in developing an advanced mathematics foundation. In my sophomore year, I had a variety of courses focused on the domain of Computer Science, namely Computer Organization & Architecture, Relational Database Management Systems, Operating Systems, Data Structures & Algorithms, Design & Analysis of Algorithms, and High Performance Computing.
My general interest lies in developing full-stack end-user applications for web and mobile devices, contributing to open-source projects, and learning new technologies. I am currently exploring the domain of Agentic AI and developing projects around it to strengthen my skills in that.
My preferred code editor is VSCode. I like its user interface, keyboard shortcuts, integrated terminal, and presence of various extensions that help in boosting productivity. It also provides efficient code formatting, which helps me in maintaining well-formatted code throughout the projects. I also use the integrated version control a lot of times, as it gives me all the info I need in a visually informative way, without me having to open a terminal window.
Programming experience
I have been programming since high school. In the beginning, I simply developed landing pages for various non-existent products using HTML and CSS. Later on, I learned about various modern-day web technologies like React, Node, Express, etc. Once I had enough skills in these domains, I started freelancing, delivering full-stack web applications to individual clients. It was due to the various demands of some clients that I had to learn new skills, which included mobile app development, DevOps, web scraping, etc.
Once I finished high school, I started to explore more domains in programming, like machine learning, AI, systems programming, etc. Simultaneously, I was honing my skills in technologies like Git, GitHub Actions, AWS, Supabase, and Firebase. I also started practicing various coding problems in LeetCode and CodeForces to develop my problem-solving skills.
Throughout my journey, I built numerous projects. Some of the projects I really enjoyed working on are:
SecureX: A mobile based crypto-wallet, which allows users to simply send and receive any cryptocurrency. The objective behind building this project was to offer true decentralization to users with the highest level of abstraction and simplicity. This was built using React Native and Ethers.js.
VimRust: A modern terminal text editor, which offers numerous features on top of what's provided by existing popular terminal-based text editors, like an advanced undo/redo system with 1000-operation history, real-time syntax highlighting, and many more. Made using Rust, I learnt about developing tools using Rust, which is really fast and offers amazing memory safety.
WalletWise: A mobile application that uses AI to provide detailed analytics on the spending patterns of individuals. The transactions made by the user are recorded from the user's payment app. Based on the notes provided by the user, transactions are classified into various categories. These patterns are then used for analysis and providing suggestions to increase savings. Made using React Native, TypeScript, Node, PostgreSQL, and Langchain.js.
Over time, I have been constantly exploring new domains. This has made me flexible to learn and adapt to new technologies and expand my skillset.
JavaScript experience
I have been predominantly working with JavaScript for most of the projects I have developed and delivered. My most recent work in JavaScript includes the contributions I made for stdlib, where I developed some special mathematical function packages which are part of the math/base/special namespace. Apart from my contributions at stdlib, I also have experience in developing end-user applications in JavaScript using React, React Native, Express, etc.
The best thing about JavaScript, in my opinion, is its extensive support for various libraries and frameworks. In today's time, I can build a mobile app, web frontend, backend, desktop application, and many more in JavaScript. This versatility is something I really like about JavaScript.
The least favourite aspect, in my opinion, is that as JavaScript was designed around asynchronous, event-driven I/O, this creates friction for CPU-intensive numerical work. Heavy computations can block the event loop, and there is no native story for parallelism in numerical computing. This is something I noticed while working on stdlib, and it is exactly why stdlib's approach of using C implementations backed by typed arrays makes so much sense to me. It gets the performance-critical math off the JS engine's interpretive path entirely.
Node.js experience
I was introduced to Node.js early in my programming journey, around the same time I started learning React and Express. Over time, as I took on more freelancing projects, it became a constant, and almost every project I made had it in some capacity, whether for building REST APIs, handling authentication flows, or managing database interactions.
Even today, I find myself reaching for Node.js in most projects due to its maturity and the sheer size of the npm ecosystem. My contributions to stdlib also run on Node.js, which gave me a deeper appreciation for how the runtime handles typed arrays and native addons under the hood.
C/Fortran experience
C is the primary language used in most of my core Computer Science courses, including Data Structures & Algorithms, Design & Analysis of Algorithms, and High Performance Computing. This has given me a strong foundation in C, and I am very comfortable working with it. My contributions to stdlib have further strengthened this, where I wrote C implementations for various mathematical functions in the math/base/special namespace.
As for Fortran, my knowledge is limited. But since I am flexible with learning new technologies, I am sure that I would be able to pick it up quickly if the project requires it.
Interest in stdlib
Before I came across stdlib, whenever I needed to perform complex mathematical computations in my web projects, I had to rely on Python and Numpy, and spin up a separate server just to handle that. It always felt like an unnecessary overhead for something that should have been straightforward.
When I first discovered stdlib, I was totally amazed. The idea that I could do the same thing directly in JavaScript, without maintaining a separate Python server, was a game-changer for me. As I explored more, I got to see the depth of the project, the rigor behind each implementation, and the overall vision of bringing numerical computing to JavaScript. That is what got me really interested and eventually led me to start contributing.
My math professor showed interest in having something students could actually watch, not just follow on paper. I built a small web app that steps through four root-finding methods (Bisection, Regula Falsi, Newton-Raphson, Secant) one iteration at a time, with the plot and convergence table updating side by side.
The math pulls from stdlib: @stdlib/math/base/special/pow, cos, sin, exp, and abs, rather than the browser's built-in Math object. When students are watching a method converge, the last thing I want is to be chasing down floating-point inconsistencies. stdlib handled that.
Four functions are built in (cubic, transcendental, exponential, quadratic), and the app also accepts custom expressions. For Newton-Raphson on a custom function, the derivative is computed via central difference, so students don't need to work it out themselves. The canvas draws method-specific overlays and fades out previous iteration points, so the convergence path stays visible as the animation runs.
React, TypeScript, Vite, Tailwind. It's a small project. But it was the first time I used stdlib in something I was building rather than contributing to, and that shift in perspective was useful in ways I didn't expect.
Goals
The primary goals of this project are:
To implement all the missing independent and dependent math functions, which are prioritized on the basis of their cross-functional usage.
To move towards ULP difference-based testing using the @stdlib/assert/is-almost-same-value in the implementation of math functions.
Below is a diagram illustrating the entire dependency tree of all the remaining math/base/special functions (includes all merged and non-merged math functions which are currently missing from stdlib).
Core Objectives:
I found about 92 math functions that are missing. Out of these:
Functions that don't have any PRs: 49
Functions that have an open PR, but not merged yet: 35
Functions that have draft PR: 8
My primary focus would be to implement the missing math functions in order of their priority. The priority of a math function is defined by its dependency on some other math function(s) and its cross-package usability. The independent functions with higher cross-package usability will be given higher priority. I will refer to the numerical references of the math functions in order to implement them and write test cases, as they provide reliable and robust implementations, along with test references.
While implementing these math functions, I will be ensuring the use of @stdlib/assert/is-almost-same-value for ULP difference-based testing. For the functions that are already present in stdlib, the ULP different test methodology will be implemented if time permits, with beta and gamma families in priority for this.
Additional Objectives:
Peer review the active PRs of math functions, in the order of their dependency (independent math functions with the most dependent functions will be given the highest priority in this case). This would include checking the implementation, tests, code standards, documentation, etc. If required and time persists, I will rework on the PRs.
Add tests to verify IEEE-754 compliance for applicable math functions. This is in reference to this discussion
Why this project?
I'd been using scipy.special for a while before I came across stdlib. Finding out that a JavaScript equivalent existed, covering the same ground with actual precision, was a genuine surprise. JS math libraries have a reputation for being loose with floats. stdlib wasn't.
What got me digging deeper was the C native add-on layer. The actual computation happens in C, surfaced through native bindings. For functions like the ones in math/base/special, that's not a minor detail. Transcendental functions accumulate floating-point error fast, and a reliable C core gives reliable results.
When I started contributing, math/base/special felt like the right place. I already knew these functions from scipy, understood what they were actually for, and could see the gap: many of them have no C implementation. Pure JavaScript only, which means no native performance path, no C/C++ interoperability, no real route to WebAssembly. For a namespace doing scipy.special's job in a JS environment, that's a meaningful hole and one I'm well placed to work on.
Qualifications
Contributing to stdlib has probably been the steepest part of my learning so far, and it is the most relevant thing I can point to as a qualification. I came in with C basics from coursework and some feel for numerical functions from scipy.special. What I didn't have was any real sense of what it takes to get one of these functions right inside a production library.
math/base/special/sici was where that changed. The floating-point behavior kept breaking at domain boundaries. Fixing it wasn't about adjusting constants. Rather, I had to actually trace where the error was coming from. It was then that I understood the idea of numerical vs. mathematical correctness. Working through that implementation made it real.
The review cycle with maintainers added something I didn't anticipate. Getting pushback on implementation decisions, not just style or formatting, meant I had to think about choices I had made somewhat on instinct. That forced a different kind of understanding than just reading about how things should be done.
The coursework in Computational Mathematics, Calculus & Matrices, and Probability & Statistics gave me the math foundation. The contributions gave me the practical side on how stdlib structures these functions, what reviewers actually look for, and where the edge cases hide. At this point, I know the math/base/special codebase well enough that I'm not going to spend GSoC figuring out how things work. I can just do the work.
Prior art
Functions in math/base/special, such as Gamma, Bessel, incomplete beta, gamma, etc., are part of decades-old mathematics with corresponding implementations. Libraries like Cephes, FreeBSD, Boost, Go, Julia, and SciPy have all solved these problems already, and their solutions are openly available. stdlib already references them for implementations and test validation, so the groundwork for what "correct" looks like is well established.
Inside stdlib itself, this isn't the first time someone has tried to close this gap. Issue #649 tracks the broader effort, and two previous GSoC contributors, Gunj Joshi and Karan Anand, have already merged substantial work under it. Going through their PRs while doing my own contributions showed me what the bar actually looks like, which is not just about the code, but the tests, the documentation, and the review conversation around them.
Commitment
My end-of-semester exams finish on 4th May, so by the time the bonding period starts, I already have three free weeks. I want to use that time to get into the functions I'll be implementing. I will be reading reference implementations in detail and finding where the hard parts are. The first day of coding shouldn't also be the first day of figuring out where to start.
From 25th May, 35-40 hours a week. Summer is genuinely free, which means no classes and nothing else competing for the same time. The hours will be there. What matters more to me is finishing with work that's actually merged, not a stack of open PRs that drag through review after the program ends.
I'll keep contributing after August. stdlib-js/stdlib#649 is what this project is scoped to, but stdlib is a much larger codebase, and there's no shortage of things to work on once that gap is closed. There are still other namespaces, other open issues to be worked on. I've been contributing long enough to know there's always a next thing worth doing here.
Schedule
The schedule runs on two tracks. One is personal implementation, where I write functions from scratch. The other is peer review, where I look at functions that already have open or draft PRs, then I read the implementation, test against reference values, check edge cases, and leave detailed comments. There is no point re-doing work that's already in progress; better to keep it moving.
All open PRs for in-scope functions get triaged during the bonding period, with at least a first-pass review on each before coding starts.
Assuming a 12 week schedule,
Community Bonding Period: Exams end on 4th May, so the bonding period starts with free time already available. Those three weeks go toward reading and analyzing reference implementations in detail, triaging open PRs, and spotting dependency risks before they become schedule problems. Week 1 should not be the first day I'm figuring out where to start.
Week 1: Implement the foundation functions that unblock the largest downstream chains: gammalnf (unblocks gammaincf, betalnf, gamma1pm1f, gamma-delta-ratiof), riemann-zetaf (unblocks dirichlet-etaf), factorialf (unblocks falling-factorialf, rising-factorialf), besselj1f (unblocks bessely1f), and the ceil rounding batch (ceilnf, ceilbf, ceilsdf) as independent buffer work. Peer review: erff#10218, gamma-lanczos-sumf#6094, kernel-betainc#10279, digammaf#10265.
Week 2: Implement gammaincf (needs gammalnf from Week 1), betalnf, acoshf, asinhf, and the floor rounding batch (floornf, floorbf, floorsdf) as independent buffer work. Peer review: gammaf#10805, gammaincinv#9982, expf#3318, expm1f#10657.
Week 3: Implement erfinvf (needs erff open PR), binetf, log1pmxf, xlog1pyf, fresnelcf, fresnelsf, and sicif. Peer review: erfcf#9450, betainc C impl #4037, atanhf#8968, tanhf#10217.
Week 4: Implement gammaincinvf (needs gammaincf from Week 2), gamma1pm1f, gamma-delta-ratiof, kernel-betaincinv (needs betainc C impl open PR), and the trunc rounding batch (truncnf, truncbf, truncsdf) as independent buffer work. Peer review: erfcxf#10525, betaincinv#6547, sqrt1pm1f#10318, powm1f#10315. Milestone: Gamma CDF and quantile (float) complete.
Week 5: Implement erfcinvf (needs erfcf open PR), sinhf, coshf, expitf, log1pexpf, sicif (needs sici from Week 3), fresnelf (needs fresnelcf and fresnelsf from Week 3), and roundnf, roundsdf as independent buffer work. Peer review: besselj0f#10038, fresnelf#10844. Milestone: All rounding variants submitted.
Week 6(midterm): Implement kernel-betaincf (needs kernel-betainc open PR and betalnf from Week 2), polygammaf (needs digammaf open PR), logaddexpf, cothf, cschf, and acothf. The gamma chain is closed at float precision, the error function chain is done, the hyperbolic family is underway, and all rounding variants are in. The hard dependency work is behind us going into the second half.
Week 7: Implement betaf, binomcoeflnf, falling-factorialf, rising-factorialf, bessely0f (needs besselj0f open PR), and bessely1f (needs bessely0f this week and besselj1f from Week 1). Peer review: exp2f#3366, exp10f#7387, log2f#4179, log10f#10277.
Week 8: Implement betaincf (needs kernel-betaincf from Week 6), expm1relf, and log1mexpf. Milestone: Beta, Binomial, t, and F double-precision chain complete. Normal CDF and quantile chain complete. Hyperbolic family complete. ML activation functions complete.
Week 9: Implement kernel-betaincinvf (needs betaincf from Week 8 and kernel-betaincinv from Week 4), then betaincinvf sequentially within the same week. These two sit at the end of the longest chain in the schedule and are the most algorithmically complex. The full week is theirs. Milestone: Beta, Binomial, t, and F float CDF and quantile complete.
Week 10: Implement cceilnf, croundnf, ellipkf, ellipef, and ellipjf. Run an integration pass against the stats/base/dists test suite. Update the #649 checklist.
Week 11: Code freeze. This week covers two things: refactoring the test suites of already-merged packages to replace absolute tolerance checks with ULP difference-based testing via @stdlib/assert/is-almost-same-value, and working through reviewer feedback that piled up in Weeks 9 and 10. The most likely candidates for lingering review cycles are betaincinvf and kernel-betaincinvf, given where they sit in the dependency chain.
Week 12: Final review pass. Documentation consistency, benchmarks, no half-finished PRs. If the buffer is clear, implement the remaining lower-priority functions: boxcox1pf, boxcox1pinvf, boxcoxinvf, ccisf, sincosdf, sincospif.
Final Week: Submit the project. Write the final report covering what was implemented, what was peer reviewed, what slipped, and what remains open in #649.
Conservative target: ~45 functions personally implemented and ~25 peer reviewed.
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.
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.
Full name
Navyansh Kesarwani
University status
Yes
University name
Manipal University, Jaipur
University program
Bachelor of Technology in Computer Science & Engineering
Expected graduation
May 1, 2028
Short biography
I am currently a sophomore undergraduate and will be entering my penultimate year of study by the time the coding period starts. My technical skill set includes programming languages like C/C++, JavaScript/TypeScript, Python, Rust, Java, and various modern web technologies used for full-stack development. Apart from these, I am also skilled in using Git and Docker.
In my freshman year, I had two very important courses on mathematics- Computational Mathematics and Calculus & Matrices, which helped me in developing an advanced mathematics foundation. In my sophomore year, I had a variety of courses focused on the domain of Computer Science, namely Computer Organization & Architecture, Relational Database Management Systems, Operating Systems, Data Structures & Algorithms, Design & Analysis of Algorithms, and High Performance Computing.
My general interest lies in developing full-stack end-user applications for web and mobile devices, contributing to open-source projects, and learning new technologies. I am currently exploring the domain of Agentic AI and developing projects around it to strengthen my skills in that.
Timezone
Indian Standard Time ( IST ), UTC+05:30
Contact details
email:navyanshkesarwani@gmail.com,navyansh.2427030652@muj.manipal.edu
Platform
Mac
Editor
My preferred code editor is VSCode. I like its user interface, keyboard shortcuts, integrated terminal, and presence of various extensions that help in boosting productivity. It also provides efficient code formatting, which helps me in maintaining well-formatted code throughout the projects. I also use the integrated version control a lot of times, as it gives me all the info I need in a visually informative way, without me having to open a terminal window.
Programming experience
I have been programming since high school. In the beginning, I simply developed landing pages for various non-existent products using HTML and CSS. Later on, I learned about various modern-day web technologies like React, Node, Express, etc. Once I had enough skills in these domains, I started freelancing, delivering full-stack web applications to individual clients. It was due to the various demands of some clients that I had to learn new skills, which included mobile app development, DevOps, web scraping, etc.
Once I finished high school, I started to explore more domains in programming, like machine learning, AI, systems programming, etc. Simultaneously, I was honing my skills in technologies like Git, GitHub Actions, AWS, Supabase, and Firebase. I also started practicing various coding problems in LeetCode and CodeForces to develop my problem-solving skills.
Throughout my journey, I built numerous projects. Some of the projects I really enjoyed working on are:
SecureX: A mobile based crypto-wallet, which allows users to simply send and receive any cryptocurrency. The objective behind building this project was to offer true decentralization to users with the highest level of abstraction and simplicity. This was built using React Native and Ethers.js.
VimRust: A modern terminal text editor, which offers numerous features on top of what's provided by existing popular terminal-based text editors, like an advanced undo/redo system with 1000-operation history, real-time syntax highlighting, and many more. Made using Rust, I learnt about developing tools using Rust, which is really fast and offers amazing memory safety.
WalletWise: A mobile application that uses AI to provide detailed analytics on the spending patterns of individuals. The transactions made by the user are recorded from the user's payment app. Based on the notes provided by the user, transactions are classified into various categories. These patterns are then used for analysis and providing suggestions to increase savings. Made using React Native, TypeScript, Node, PostgreSQL, and Langchain.js.
Over time, I have been constantly exploring new domains. This has made me flexible to learn and adapt to new technologies and expand my skillset.
JavaScript experience
I have been predominantly working with JavaScript for most of the projects I have developed and delivered. My most recent work in JavaScript includes the contributions I made for stdlib, where I developed some special mathematical function packages which are part of the
math/base/specialnamespace. Apart from my contributions at stdlib, I also have experience in developing end-user applications in JavaScript using React, React Native, Express, etc.The best thing about JavaScript, in my opinion, is its extensive support for various libraries and frameworks. In today's time, I can build a mobile app, web frontend, backend, desktop application, and many more in JavaScript. This versatility is something I really like about JavaScript.
The least favourite aspect, in my opinion, is that as JavaScript was designed around asynchronous, event-driven I/O, this creates friction for CPU-intensive numerical work. Heavy computations can block the event loop, and there is no native story for parallelism in numerical computing. This is something I noticed while working on stdlib, and it is exactly why stdlib's approach of using C implementations backed by typed arrays makes so much sense to me. It gets the performance-critical math off the JS engine's interpretive path entirely.
Node.js experience
I was introduced to Node.js early in my programming journey, around the same time I started learning React and Express. Over time, as I took on more freelancing projects, it became a constant, and almost every project I made had it in some capacity, whether for building REST APIs, handling authentication flows, or managing database interactions.
Even today, I find myself reaching for Node.js in most projects due to its maturity and the sheer size of the npm ecosystem. My contributions to stdlib also run on Node.js, which gave me a deeper appreciation for how the runtime handles typed arrays and native addons under the hood.
C/Fortran experience
C is the primary language used in most of my core Computer Science courses, including Data Structures & Algorithms, Design & Analysis of Algorithms, and High Performance Computing. This has given me a strong foundation in C, and I am very comfortable working with it. My contributions to stdlib have further strengthened this, where I wrote C implementations for various mathematical functions in the
math/base/specialnamespace.As for Fortran, my knowledge is limited. But since I am flexible with learning new technologies, I am sure that I would be able to pick it up quickly if the project requires it.
Interest in stdlib
Before I came across stdlib, whenever I needed to perform complex mathematical computations in my web projects, I had to rely on Python and Numpy, and spin up a separate server just to handle that. It always felt like an unnecessary overhead for something that should have been straightforward.
When I first discovered stdlib, I was totally amazed. The idea that I could do the same thing directly in JavaScript, without maintaining a separate Python server, was a game-changer for me. As I explored more, I got to see the depth of the project, the rigor behind each implementation, and the overall vision of bringing numerical computing to JavaScript. That is what got me really interested and eventually led me to start contributing.
Version control
Yes
Contributions to stdlib
Merged Pull requests: https://github.com/stdlib-js/stdlib/pulls?q=is%3Apr+author%3Anavyansh007+is%3Amerged+
Open Pull requests: https://github.com/stdlib-js/stdlib/pulls/navyansh007
Issues: https://github.com/stdlib-js/stdlib/issues?q=is%3Aissue%20state%3Aopen%20author%3Anavyansh007
stdlib showcase
My math professor showed interest in having something students could actually watch, not just follow on paper. I built a small web app that steps through four root-finding methods (Bisection, Regula Falsi, Newton-Raphson, Secant) one iteration at a time, with the plot and convergence table updating side by side.
Repository: stdlib-showcase
The math pulls from stdlib:
@stdlib/math/base/special/pow,cos,sin,exp, andabs, rather than the browser's built-in Math object. When students are watching a method converge, the last thing I want is to be chasing down floating-point inconsistencies. stdlib handled that.Four functions are built in (cubic, transcendental, exponential, quadratic), and the app also accepts custom expressions. For Newton-Raphson on a custom function, the derivative is computed via central difference, so students don't need to work it out themselves. The canvas draws method-specific overlays and fades out previous iteration points, so the convergence path stays visible as the animation runs.
React, TypeScript, Vite, Tailwind. It's a small project. But it was the first time I used stdlib in something I was building rather than contributing to, and that shift in perspective was useful in ways I didn't expect.
Goals
The primary goals of this project are:
@stdlib/assert/is-almost-same-valuein the implementation of math functions.Below is a diagram illustrating the entire dependency tree of all the remaining
math/base/specialfunctions (includes all merged and non-merged math functions which are currently missing from stdlib).flowchart TD classDef done fill:#6b7280,color:#fff,stroke:#4b5563 classDef tier0 fill:#dc2626,color:#fff,stroke:#991b1b classDef tier1 fill:#ea580c,color:#fff,stroke:#c2410c classDef tier2 fill:#d97706,color:#000,stroke:#b45309 classDef tier3 fill:#16a34a,color:#fff,stroke:#15803d classDef tier4 fill:#2563eb,color:#fff,stroke:#1d4ed8 classDef tier5 fill:#7c3aed,color:#fff,stroke:#6d28d9 %% ── Already merged ────────────────────────────────────────── gammainc_d(["gammainc\n✓ double"]):::done digamma_d(["digamma\n✓ double"]):::done %% ── Tier 0 ─────────────────────────────────────────────────── expf([expf]):::tier0 expm1f([expm1f]):::tier0 erff([erff]):::tier0 GLS(["gamma-lanczos-sumf"]):::tier0 gammalnf([gammalnf]):::tier0 digammaf([digammaf]):::tier0 KB(["kernel-betainc\n★ Tier 0 key insight"]):::tier0 GI(["gammaincinv\n★ Tier 0 key insight"]):::tier0 riemannzetaf([riemann-zetaf]):::tier0 factorialf([factorialf]):::tier0 besselj0f([besselj0f]):::tier0 besselj1f([besselj1f]):::tier0 atanhf([atanhf]):::tier0 %% ── Tier 1 ─────────────────────────────────────────────────── erfcf([erfcf]):::tier1 erfinvf([erfinvf]):::tier1 gammaf([gammaf]):::tier1 gammaincf([gammaincf]):::tier1 betalnf([betalnf]):::tier1 betainc_c(["betainc\n(C impl)"]):::tier1 sinhf([sinhf]):::tier1 coshf([coshf]):::tier1 tanhf([tanhf]):::tier1 expitf([expitf]):::tier1 log1pexpf([log1pexpf]):::tier1 bessely0f([bessely0f]):::tier1 polygamma([polygamma]):::tier1 polygammaf([polygammaf]):::tier1 dirichlet_etaf(["dirichlet-etaf"]):::tier1 acothf([acothf]):::tier1 %% ── Tier 2 ─────────────────────────────────────────────────── erfcxf([erfcxf]):::tier2 erfcinvf([erfcinvf]):::tier2 gammaincinvf([gammaincinvf]):::tier2 gamma1pm1f([gamma1pm1f]):::tier2 GDR(["gamma-delta-ratiof"]):::tier2 KBI(["kernel-betaincinv"]):::tier2 KBCF(["kernel-betaincf"]):::tier2 betaf([betaf]):::tier2 cothf([cothf]):::tier2 cschf([cschf]):::tier2 logaddexpf([logaddexpf]):::tier2 binomcoeflnf([binomcoeflnf]):::tier2 falling_f(["falling-factorialf"]):::tier2 rising_f(["rising-factorialf"]):::tier2 bessely1f([bessely1f]):::tier2 %% ── Tier 3 ─────────────────────────────────────────────────── betaincinv([betaincinv]):::tier3 betaincf([betaincf]):::tier3 %% ── Tier 4 ─────────────────────────────────────────────────── KBICF(["kernel-betaincinvf"]):::tier4 %% ── Tier 5 ─────────────────────────────────────────────────── betaincinvf([betaincinvf]):::tier5 %% ── Edges: Chain 1 — Error Function ───────────────────────── erff --> erfcf erff --> erfinvf erfcf --> erfcxf erfcf --> erfcinvf %% ── Edges: Chain 2 — Gamma ─────────────────────────────────── GLS --> gammaf gammalnf --> gammaf gammalnf --> gammaincf expf --> gammaincf gammaincf --> gammaincinvf gammalnf --> gamma1pm1f gammaf --> gamma1pm1f gammalnf --> GDR gammalnf --> betalnf gammainc_d --> GI %% ── Edges: Chain 3 — Beta ──────────────────────────────────── KB --> betainc_c betainc_c --> KBI betainc_c --> betaincinv KBI --> betaincinv KB --> KBCF betalnf --> KBCF KBCF --> betaincf betaincf --> KBICF KBI --> KBICF KBICF --> betaincinvf betaincf --> betaincinvf %% ── Edges: Hyperbolic & ML ─────────────────────────────────── expf --> sinhf expf --> coshf expf --> tanhf expf --> expitf expm1f --> log1pexpf log1pexpf --> logaddexpf tanhf --> cothf sinhf --> cschf atanhf --> acothf %% ── Edges: Combinatorics ───────────────────────────────────── betalnf --> betaf expf --> betaf betalnf --> binomcoeflnf gammaf --> falling_f factorialf --> falling_f gammaf --> rising_f factorialf --> rising_f %% ── Edges: Bessel & Series ─────────────────────────────────── besselj0f --> bessely0f besselj1f --> bessely1f bessely0f --> bessely1f riemannzetaf --> dirichlet_etaf %% ── Edges: Polygamma ───────────────────────────────────────── digamma_d --> polygamma digammaf --> polygammaf#6b7280done#dc2626Tier 0#ea580cTier 1#d97706Tier 2#16a34aTier 3#2563ebTier 4#7c3aedTier 5betaincinvfonly)Core Objectives:
I found about 92 math functions that are missing. Out of these:
My primary focus would be to implement the missing math functions in order of their priority. The priority of a math function is defined by its dependency on some other math function(s) and its cross-package usability. The independent functions with higher cross-package usability will be given higher priority. I will refer to the numerical references of the math functions in order to implement them and write test cases, as they provide reliable and robust implementations, along with test references.
While implementing these math functions, I will be ensuring the use of
@stdlib/assert/is-almost-same-valuefor ULP difference-based testing. For the functions that are already present in stdlib, the ULP different test methodology will be implemented if time permits, with beta and gamma families in priority for this.Additional Objectives:
Why this project?
I'd been using scipy.special for a while before I came across stdlib. Finding out that a JavaScript equivalent existed, covering the same ground with actual precision, was a genuine surprise. JS math libraries have a reputation for being loose with floats. stdlib wasn't.
What got me digging deeper was the C native add-on layer. The actual computation happens in C, surfaced through native bindings. For functions like the ones in
math/base/special, that's not a minor detail. Transcendental functions accumulate floating-point error fast, and a reliable C core gives reliable results.When I started contributing,
math/base/specialfelt like the right place. I already knew these functions from scipy, understood what they were actually for, and could see the gap: many of them have no C implementation. Pure JavaScript only, which means no native performance path, no C/C++ interoperability, no real route to WebAssembly. For a namespace doing scipy.special's job in a JS environment, that's a meaningful hole and one I'm well placed to work on.Qualifications
Contributing to stdlib has probably been the steepest part of my learning so far, and it is the most relevant thing I can point to as a qualification. I came in with C basics from coursework and some feel for numerical functions from scipy.special. What I didn't have was any real sense of what it takes to get one of these functions right inside a production library.
math/base/special/siciwas where that changed. The floating-point behavior kept breaking at domain boundaries. Fixing it wasn't about adjusting constants. Rather, I had to actually trace where the error was coming from. It was then that I understood the idea of numerical vs. mathematical correctness. Working through that implementation made it real.The review cycle with maintainers added something I didn't anticipate. Getting pushback on implementation decisions, not just style or formatting, meant I had to think about choices I had made somewhat on instinct. That forced a different kind of understanding than just reading about how things should be done.
The coursework in Computational Mathematics, Calculus & Matrices, and Probability & Statistics gave me the math foundation. The contributions gave me the practical side on how stdlib structures these functions, what reviewers actually look for, and where the edge cases hide. At this point, I know the
math/base/specialcodebase well enough that I'm not going to spend GSoC figuring out how things work. I can just do the work.Prior art
Functions in
math/base/special, such as Gamma, Bessel, incomplete beta, gamma, etc., are part of decades-old mathematics with corresponding implementations. Libraries like Cephes, FreeBSD, Boost, Go, Julia, and SciPy have all solved these problems already, and their solutions are openly available. stdlib already references them for implementations and test validation, so the groundwork for what "correct" looks like is well established.Inside stdlib itself, this isn't the first time someone has tried to close this gap. Issue #649 tracks the broader effort, and two previous GSoC contributors, Gunj Joshi and Karan Anand, have already merged substantial work under it. Going through their PRs while doing my own contributions showed me what the bar actually looks like, which is not just about the code, but the tests, the documentation, and the review conversation around them.
Commitment
My end-of-semester exams finish on 4th May, so by the time the bonding period starts, I already have three free weeks. I want to use that time to get into the functions I'll be implementing. I will be reading reference implementations in detail and finding where the hard parts are. The first day of coding shouldn't also be the first day of figuring out where to start.
From 25th May, 35-40 hours a week. Summer is genuinely free, which means no classes and nothing else competing for the same time. The hours will be there. What matters more to me is finishing with work that's actually merged, not a stack of open PRs that drag through review after the program ends.
I'll keep contributing after August. stdlib-js/stdlib#649 is what this project is scoped to, but stdlib is a much larger codebase, and there's no shortage of things to work on once that gap is closed. There are still other namespaces, other open issues to be worked on. I've been contributing long enough to know there's always a next thing worth doing here.
Schedule
The schedule runs on two tracks. One is personal implementation, where I write functions from scratch. The other is peer review, where I look at functions that already have open or draft PRs, then I read the implementation, test against reference values, check edge cases, and leave detailed comments. There is no point re-doing work that's already in progress; better to keep it moving.
All open PRs for in-scope functions get triaged during the bonding period, with at least a first-pass review on each before coding starts.
Assuming a 12 week schedule,
Community Bonding Period: Exams end on 4th May, so the bonding period starts with free time already available. Those three weeks go toward reading and analyzing reference implementations in detail, triaging open PRs, and spotting dependency risks before they become schedule problems. Week 1 should not be the first day I'm figuring out where to start.
Week 1: Implement the foundation functions that unblock the largest downstream chains:
gammalnf(unblocksgammaincf,betalnf,gamma1pm1f,gamma-delta-ratiof),riemann-zetaf(unblocksdirichlet-etaf),factorialf(unblocksfalling-factorialf,rising-factorialf),besselj1f(unblocksbessely1f), and the ceil rounding batch (ceilnf,ceilbf,ceilsdf) as independent buffer work. Peer review:erff#10218,gamma-lanczos-sumf#6094,kernel-betainc#10279,digammaf#10265.Week 2: Implement
gammaincf(needsgammalnffrom Week 1),betalnf,acoshf,asinhf, and the floor rounding batch (floornf,floorbf,floorsdf) as independent buffer work. Peer review:gammaf#10805,gammaincinv#9982,expf#3318,expm1f#10657.Week 3: Implement
erfinvf(needserffopen PR),binetf,log1pmxf,xlog1pyf,fresnelcf,fresnelsf, andsicif. Peer review:erfcf#9450,betaincC impl #4037,atanhf#8968,tanhf#10217.Week 4: Implement
gammaincinvf(needsgammaincffrom Week 2),gamma1pm1f,gamma-delta-ratiof,kernel-betaincinv(needsbetaincC impl open PR), and the trunc rounding batch (truncnf,truncbf,truncsdf) as independent buffer work. Peer review:erfcxf#10525,betaincinv#6547,sqrt1pm1f#10318,powm1f#10315. Milestone: Gamma CDF and quantile (float) complete.Week 5: Implement
erfcinvf(needserfcfopen PR),sinhf,coshf,expitf,log1pexpf,sicif(needssicifrom Week 3),fresnelf(needsfresnelcfandfresnelsffrom Week 3), androundnf,roundsdfas independent buffer work. Peer review:besselj0f#10038,fresnelf#10844. Milestone: All rounding variants submitted.Week 6 (midterm): Implement
kernel-betaincf(needskernel-betaincopen PR andbetalnffrom Week 2),polygammaf(needsdigammafopen PR),logaddexpf,cothf,cschf, andacothf. The gamma chain is closed at float precision, the error function chain is done, the hyperbolic family is underway, and all rounding variants are in. The hard dependency work is behind us going into the second half.Week 7: Implement
betaf,binomcoeflnf,falling-factorialf,rising-factorialf,bessely0f(needsbesselj0fopen PR), andbessely1f(needsbessely0fthis week andbesselj1ffrom Week 1). Peer review:exp2f#3366,exp10f#7387,log2f#4179,log10f#10277.Week 8: Implement
betaincf(needskernel-betaincffrom Week 6),expm1relf, andlog1mexpf. Milestone: Beta, Binomial, t, and F double-precision chain complete. Normal CDF and quantile chain complete. Hyperbolic family complete. ML activation functions complete.Week 9: Implement
kernel-betaincinvf(needsbetaincffrom Week 8 andkernel-betaincinvfrom Week 4), thenbetaincinvfsequentially within the same week. These two sit at the end of the longest chain in the schedule and are the most algorithmically complex. The full week is theirs. Milestone: Beta, Binomial, t, and F float CDF and quantile complete.Week 10: Implement
cceilnf,croundnf,ellipkf,ellipef, andellipjf. Run an integration pass against thestats/base/diststest suite. Update the #649 checklist.Week 11: Code freeze. This week covers two things: refactoring the test suites of already-merged packages to replace absolute tolerance checks with ULP difference-based testing via
@stdlib/assert/is-almost-same-value, and working through reviewer feedback that piled up in Weeks 9 and 10. The most likely candidates for lingering review cycles arebetaincinvfandkernel-betaincinvf, given where they sit in the dependency chain.Week 12: Final review pass. Documentation consistency, benchmarks, no half-finished PRs. If the buffer is clear, implement the remaining lower-priority functions:
boxcox1pf,boxcox1pinvf,boxcoxinvf,ccisf,sincosdf,sincospif.Final Week: Submit the project. Write the final report covering what was implemented, what was peer reviewed, what slipped, and what remains open in #649.
Conservative target: ~45 functions personally implemented and ~25 peer reviewed.
Stretch target: ~55 functions personally implemented.
Notes:
Related issues
#34
stdlib-js/stdlib#649
stdlib-js/stdlib#365
Checklist
[RFC]:and succinctly describes your proposal.