R project for statistical computing

Mentors: Student: Weidong "William" Zhang

This project extends the gfpop R package to support time-dependent constraints. Instead of using a fixed constraint graph, the graph can change at each data point based on a user-defined rule function. This makes it possible to express models like LOPART and labeled up-down peak detection within a single framework. Deliverables include the R API extension, C++ implementation, oracle-validated LOPART model, a 6-rule up-down model with label support, and a user-facing vignette.

Mentors: Student: Srishti Dutta

The torchvision package in the mlverse ecosystem provides essential tools for computer vision workflows in R, including datasets, transformations, and model utilities. While the ecosystem has matured significantly, a key limitation remains in the performance of certain core operations, particularly in detection pipelines. Functions such as Non-Maximum Suppression, bounding box transformations, and proposal filtering are currently implemented at the R level, which introduces inefficiencies when working with larger inputs or real-time use cases. This project aims to address that gap by moving performance-critical components into a native C++ backend using torchvisionlib. The approach will focus on identifying the parts of the pipeline that are actual bottlenecks, implementing them in C++ with careful attention to memory usage and batching, and integrating them back into torchvision without changing the user-facing API. Rather than rewriting everything, the work will prioritise targeted optimisations that have a measurable impact on end-to-end performance. In addition to backend improvements, the project will include the implementation of at least one modern computer vision model family, such as YOLO, using the optimised components. If time permits, support for an additional model (e.g., RT-DETR) will also be explored. These models will be integrated into the existing ecosystem with consistent APIs and working examples. The main deliverables of this project are: a set of optimised C++ implementations for core detection operations, their seamless integration into torchvision, at least one fully functional model pipeline built on top of this backend, and supporting documentation and benchmarks demonstrating performance improvements. The goal is to make torchvision not just feature-complete, but efficient enough for practical, large-scale use.

Mentors: Student: Catherine Chung

Problem: R remains the gold standard in bioinformatics thanks to Bioconductor. However, many data scientists prefer Python for machine learning and building production. Currently, bridging tools such as rpy2 suffer a lot from stability issues. For instance, ABI symbolic linking errors and complex S4 object mapping failures, obstructing complete integration with essential tools like DESeq2. Solution: This project aims to enhance the Rosetta library by implementing a robust three-tier API architecture. This architecture includes an easy-to-use high-level Python interface, a fine-grained control layer for advanced users, and a subprocess-based fallback mechanism to prevent core crashes. I will focus on addressing deep interoperability challenges, including S4 method distribution and cross-language type conversion. Deliverables: 1.A stable, production-ready DESeq2 and edgeR Python wrapper library. 2.A three-tier API design ensuring 100% statistical consistency with native R output. 3.During periods of high load, the subroutine falls back to the backend to ensure system stability. 4.Comprehensive documentation and a Pytest-based validation suite ensure a reproducible experimental environment.

Mentors: Student: Vinit Thakur

The proposal includes detailed information about me (such as my qualifications and other relevant details), project details, my approach to the project, the timeline, and other essential aspects. project details: This project focuses on enhancing the functionality and performance of the widely-used R package, data.table, by prioritizing low-level C programming improvements and advanced performance benchmarking. The primary objective is to optimize the core functionality of data.table by implementing efficient C-level changes, ensuring faster data manipulation and analysis. I have previously been selected for Google Summer of Code 2025 with the same organization and worked on the same data.table project, which helped me gain a strong understanding of the codebase, architecture, and contribution workflow. This experience allows me to start contributing immediately and focus on high-impact improvements. I am approaching GSoC 2026 with stronger motivation, deeper technical insight, and a clear goal of making more substantial contributions. Performance benchmarking will be a key focus, utilizing tools like atime to rigorously test and validate improvements, ensuring reliability and scalability for diverse use cases. While the main emphasis is on performance optimization and functionality enhancements, the project will also include supplementary work on expanding documentation through concise vignettes or whitepapers to provide insights into the technical improvements made. This initiative aims to deliver a more robust and high-performing data.table package, empowering data analysts and statisticians with faster and more reliable tools for their workflows.

Mentors: Student: Akshat Maurya

R's standard file reading functions must ingest data into memory before any filtering can occur, creating a hard memory ceiling for large datasets. AWK evaluates records one line at a time, filtering data before it ever enters R's memory space. The awkreader package bridges these two worlds, letting R users express filter conditions in familiar R syntax while AWK handles the heavy lifting at the shell level. The existing codebase is ~80% complete for Mac/Unix. This project completes and delivers the full production-ready CRAN package by: (1) completing and verifying the full translation engine: mapping R filter expressions including comparisons, logical operators, %in%, grepl(), and is.na() to correct AWK syntax across all platforms. (2) auditing and restoring Mac/Unix correctness. (3) achieving full Windows/PC compatibility via robust shell quoting and Rtools detection. (4) formalizing the package with roxygen2 docs, a cross-platform CI test harness with regression locking, and CRAN submission, enabling R users to filter datasets on standard hardware without memory constraints.