O'Connell Lab at Stanford University
Official@laurenoconnelllab · United States of America
Lauren O'Connell Lab at Stanford University
Agent Skills by O'Connell Lab at Stanford University
Showing 8 vetted skills indexed across 1 GitHub repositories.
laurenoconnelllabr-oop
Guide selection and implementation of R OOP systems like S3, S4, S7, and vctrs.
laurenoconnelllabr-bayes
Run Bayesian regression models and validate causal DAGs in R.
laurenoconnelllabtidyverse-patterns
Teach modern tidyverse patterns for writing, optimizing, and migrating R code.
laurenoconnelllabrlang-patterns
Explains how to use @, !!, and related tools for R metaprogramming and tidy evaluation.
laurenoconnelllabr-style-guide
Guide R code structure, styling, and documentation for consistency.
laurenoconnelllabr-performance
Profile, benchmark, and optimize R code with profvis and bench.
laurenoconnelllabr-package-development
Guide R package development with best practices for testing, documentation, and structure.
laurenoconnelllabtdd-workflow
Guide R function development through test-driven workflows with testthat and usethis.
Frequently Asked Questions About O'Connell Lab at Stanford University
FAQPage SchemaWhat specific statistical and architectural tasks does the O'Connell Lab support?▼
The lab supports implementing advanced object-oriented systems like S3, S4, and S7, alongside executing Bayesian regression models. It provides expertise in causal DAG validation, metaprogramming via tidy evaluation, and optimizing code performance through systematic benchmarking and profiling.
Which technical personas benefit from these R-centric capabilities?▼
These capabilities are designed for computational biologists, data scientists, and research software engineers working within the R ecosystem. The resources specifically assist those needing to transition from ad-hoc analysis to robust, maintainable, and high-performance research software packages.
What are the primary dependencies for implementing these development patterns?▼
Implementation relies on core R ecosystem packages including testthat for unit testing, usethis for project structure, profvis for performance profiling, and bench for benchmarking. Users should have a foundational understanding of R syntax and the tidyverse ecosystem to effectively apply these architectural patterns.