PSIAIMS/CAMIS
Mar 2026 – Mar 2026
1 Month active
Languages Used
MarkdownRSAS
Technical Skills
R programmingSAS programmingdata analysisdata visualizationdocumentationstatistical analysis
GriffinOyoo
PROFILE
Griffinoyoo
In March 2026, Oyo Oloo enhanced binomial testing capabilities in the PSIAIMS/CAMIS repository, focusing on robust, reproducible statistical analysis for clinical and analytical projects. Oyo developed and documented binomial test methods—including Exact, Wald, Mid-p, and Wilson—in both R and SAS, ensuring cross-language consistency and enabling direct comparison of results. The work included creating a unified reference implementation and comprehensive confidence interval documentation, supporting analysts in interpreting results across platforms. By emphasizing clear documentation, traceable commits, and reproducibility, Oyo delivered a feature-rich, maintainable solution that streamlines statistical testing and data-driven decision making for diverse analytical workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
1
Lines of code
404
Activity Months1
Your Network
33 peopleShared Repositories
33
Work History
March 2026
6 Commits • 1 Features
Mar 1, 2026March 2026 Monthly Summary (PSIAIMS/CAMIS): Focused on delivering robust binomial testing capabilities across R and SAS, with cross-language consistency, thorough documentation, and reproducibility enhancements. This work lays the groundwork for analysts to confidently compare methods and interpret confidence intervals across platforms, accelerating data-driven decision making for clinical and analytical projects.
6 Commits • 1 Features
Mar 1, 2026March 2026 Monthly Summary (PSIAIMS/CAMIS): Focused on delivering robust binomial testing capabilities across R and SAS, with cross-language consistency, thorough documentation, and reproducibility enhancements. This work lays the groundwork for analysts to confidently compare methods and interpret confidence intervals across platforms, accelerating data-driven decision making for clinical and analytical projects.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownRSAS
Technical Skills
R programmingSAS programmingdata analysisdata visualizationdocumentationstatistical analysisstatistical modelingstatistical testing
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