One of the best parts of my mathematics degree is deepening my R skills—not just using R packages, but building them.
Why R for Statistical Computing
R has a unique position in statistics:
- Domain-specific: Built for statistics, not adapted to it
- Expressive: Statistical concepts map naturally to R syntax
- Ecosystem: CRAN has mature, well-tested statistical libraries
- Literate programming: RMarkdown integrates code, math, and prose
What I’m Building
I’m developing R packages for reliability analysis:
Maximum likelihood estimation for series systems with masked failure data—situations where you know the system failed but not which component caused it.
Bootstrap confidence intervals for complex reliability metrics that don’t have closed-form variance formulas.
Survival analysis tools for right-censored data with Weibull distributions.
The Philosophy
I approach package development like API design:
- Clear interfaces: Functions do one thing well
- Composability: Small functions that combine flexibly
- Documentation: Every function has examples and mathematical background
- Testing: Automated tests ensure correctness
- Reproducibility: Vignettes demonstrate complete workflows
Why Open Source Matters Here
Statistical methods are only as useful as their implementations. Publishing clean, documented R packages means:
- Researchers can reproduce my results
- Methods can be peer-reviewed and validated
- Others can build on this work
- Science moves faster
The Joy of R
There’s something satisfying about translating mathematical formulas into working code. Taking a theorem from a textbook and turning it into a function that just works.
R makes this translation natural. The language maps statistical concepts cleanly.
These packages will eventually form part of my thesis work. Building tools while learning theory is the right approach.
Discussion