Changes in version 0.1.0 (2025-11-27)                  

Initial CRAN Release

This is the first release of tidydp, a tidy-style interface for applying
differential privacy to data frames in R.

Core Features

  - Differential Privacy Mechanisms
    
      - Laplace mechanism for pure epsilon-differential privacy
      - Gaussian mechanism for (epsilon, delta)-differential privacy
      - Automatic sensitivity calculations based on data bounds

  - Tidy-Style API Functions
    
      - dp_add_noise(): Add calibrated noise to numeric columns with
        pipe support
      - dp_count(): Compute differentially private counts with optional
        grouping
      - dp_mean(): Compute differentially private means with optional
        grouping
      - dp_sum(): Compute differentially private sums with optional
        grouping

  - Privacy Budget Management
    
      - new_privacy_budget(): Create and initialize privacy budgets
      - check_privacy_budget(): Verify sufficient budget before
        operations
      - Automatic budget tracking with basic composition
      - Print method for budget status visualization

Technical Details

  - All functions support the magrittr pipe operator (%>%)
  - Flexible mechanism selection (Laplace or Gaussian)
  - Support for grouped operations using group_by parameter
  - Comprehensive error handling and input validation
  - Built from scratch without external differential privacy
    dependencies

Documentation

  - Complete function documentation with examples
  - Comprehensive README with usage examples
  - Full test suite with >95% code coverage
  - Example script demonstrating common workflows

Notes

  - This package implements differential privacy mechanisms from first
    principles
  - Suitable for statistical analysis with formal privacy guarantees
  - Compatible with the tidyverse ecosystem
  - Designed for CRAN submission standards