r-package-dev

To create a CRAN-ready R package, you need to scaffold a standard structure with DESCRIPTION and NAMESPACE files, document APIs using roxygen2, validate behavior with testthat unit tests, and pass R CMD check. This process converts statistical functions into a maintainable, distributable package.

Roxygen2 documentation generates standard R manual files directly from inline code comments. It keeps function documentation consistent with the codebase by extracting parameter descriptions and examples, eliminating the need to maintain separate help files manually during package development.

The best way to add unit tests with testthat is to create a tests/ directory within your R package structure. You write test scripts validating function behavior, which can then be automatically executed during development and continuous integration to ensure code correctness.

Yes, you can interface R packages with Python for cross-language analysis using rpy2. This optional interoperability allows workflows where experiments and statistical analysis span both Python and R environments within the same package ecosystem.

To generate vignettes for an R package, you create documents within the vignettes/ directory that demonstrate your statistical methods. These provide worked examples and experimental demonstrations, helping users understand how to apply your package's functions effectively.

Yes, submitting to Bioconductor or CRAN requires a standard R package layout including R/ source, man/ docs, DESCRIPTION, and NAMESPACE files. Proper structuring with roxygen2 and testthat ensures the package meets repository checks and readiness standards.