Audits R scripts for behavioural reproducibility risk. Scans
    scripts for qualified package::function calls and checks them against a
    curated database of known silent breaking changes across popular CRAN
    packages. Flags stochastic calls lacking set.seed() and detects
    locale-sensitive operations that may produce different results across
    systems. Supports baseline certification of analytical outputs so that
    silent numerical drift can be detected across package upgrades or platform
    changes. Generates human-readable audit reports suitable for academic
    submission or pharmaceutical QC workflows. For more details see
    <https://github.com/repro-stats/reproducr>.

Transform coordinates from a specified source to a specified 
 target map projection. This uses the 'PROJ' library directly, via the 'PROJ' package. 
 The 'reproj()'  function is generic, methods may be added to remove the need for an explicit 
 source definition. This is for use primarily to 
 transform generic data formats and direct leverage of the underlying
 'PROJ' library. (There are transformations that aren't possible with 'PROJ' and
 that are provided by the 'GDAL' library, a limitation which users of this 
 package should be aware of.) The 'PROJ' library is available at 
 <https://proj.org/>.

Provides teaching datasets and helper functions to support the open
    educational resource Martinez (2026) "Reproducible Research Using R"
    <doi:10.5281/zenodo.19136755>. The package includes datasets used throughout
    the book and utilities to list and copy chapter scripts shipped with the package.

Tools for diagnosing the reproducibility of statistical model
    outputs under data perturbations. Implements bootstrap, subsampling, and
    noise-based perturbation schemes and computes coefficient stability,
    p-value stability, selection stability, prediction stability, and a
    composite reproducibility index on a 0 to 100 scale. Includes
    cross-validation ranking stability for model comparison and visualization
    utilities. Optional 'backends' support robust M-estimation ('MASS') and
    penalized regression ('glmnet'). Bootstrap perturbation follows
    'Efron' and 'Tibshirani' (1993, ISBN:9780412042317); selection stability
    follows 'Meinshausen' and 'Buhlmann' (2010) <doi:10.1111/j.1467-9868.2010.00740.x>;
    reproducibility framework follows 'Peng' (2011)
    <doi:10.1126/science.1213847>.