Dependency Management

Maintaining a clean DESCRIPTION file is critical for CRAN compliance.

• audit_dependencies(): Scans your R/ and tests/ directories to ensure all used packages are declared in DESCRIPTION.
• scan_dependencies(): Identifies packages currently attached to your session that are not actually used in your code.
• remove_package(): Safely removes a package while checking for orphan dependencies.
• remove_user_installed_packages(): Cleans all user-installed packages while preserving base and recommended packages.

Scaffolding & Automation

Reduce boilerplate and avoid manual errors with automated generators.

• architect_release(): Interactively bumps the package version and generates a NEWS.md entry.
• architect_vignette(): Creates a CRAN-compliant RMarkdown vignette structure.
• scaffold_tests(): Generates testthat boilerplate for your functions.
• scaffold_parallel(): Generates the necessary code to set up a parallel cluster.

State Management

Ensure your scripts don’t leave the user’s environment in a messy state.

• audit_script(): Captures the state of options(), par(), and getwd() before and after a script runs, prompting you to revert changes.
• detect_masking(): Identifies when functions from different packages share the same name and helps you lock in the priority.
• export_snapshot(): Creates a script to recreate your current session’s package environment.

Reproducibility Testing

• simulate_clean_room(): Runs your script in a completely vanilla R session (--vanilla) to ensure it doesn’t rely on hidden local state.
• benchmark_branches(): Compares the execution time of a script across different Git branches to quantify performance gains.

Memory Cleanup

Prevent R from crashing during large-scale data processing.

• sweep_memory(): Interactively identifies and removes large objects from the global environment.
• hunt_zombies(): Cleans up orphaned graphics devices and temporary files.
• sweep_temp_cache(): Flushes hidden temporary caches (e.g., knitr, raster).

Safe Processing

• loop_guardian(): Wraps long loops with a memory monitor that alerts you before you hit your RAM limit.
• dispatch_checkpoints(): Implements a save-and-resume system for batch processing, protecting your work from crashes.

Anonymization

• mask_identity(): A guided workflow to scramble or drop PII columns in a dataframe while preserving statistical distributions.

Documentation

• dictate_dictionary(): Interactively generates a roxygen2 @format block for your datasets, ensuring your data dictionaries are professional and complete.