Changes in version 0.2.0                        

Major New Features

Differentially Private GAN Training

  - New dp_gan_trainer() function for training GANs with formal
    differential privacy guarantees using DP-SGD (Differentially Private
    Stochastic Gradient Descent)

  - Privacy mechanisms include:
    
      - Poisson subsampling for privacy amplification
      - Per-sample gradient clipping to bound sensitivity
      - Calibrated Gaussian noise addition
      - Rényi Differential Privacy (RDP) accounting for tight
        composition

  - New secure_rng parameter to choose between cryptographically secure
    RNG (OpenDP)
    
    for production use or fast torch RNG for development/testing

  - Privacy budget pre-calculation with compute_max_steps() for
    efficient training

  - New calibrate_noise_multiplier() to find optimal noise for target
    epsilon

  - New vignette: "Training GANs with Differential Privacy"

Post-GAN Boosting

  - New apply_post_gan_boosting() wrapper for easy post-processing of
    GAN samples
  - New compute_discriminator_scores() for evaluating samples across
    checkpoints
  - Checkpoint support in both gan_trainer() and dp_gan_trainer():
      - checkpoint_epochs parameter to save model states at intervals
      - checkpoint_path for disk-based storage of large training runs
  - Support for differentially private post-GAN boosting with
    exponential mechanism
  - End-to-end privacy guarantees when combining DP training with DP
    boosting
  - New vignette: "Post-GAN Boosting for Improved Synthetic Data
    Quality"

Improvements

Documentation

  - Comprehensive documentation for data_transformer class including:
      - Detailed explanation of standard vs mode-specific (GMM)
        normalization
      - Examples for mixed continuous/categorical data
      - Integration examples with RGAN workflow
  - Added CTGAN reference (Xu et al., 2019) for mode-specific
    normalization

Training

  - New track_loss parameter in gan_trainer() and dp_gan_trainer() to
    record training losses for analysis
  - Loss tracking available via trained_gan$losses$g_loss and $d_loss

Bug Fixes

  - Fixed sample_synthetic_data() compatibility with dp_gan_trainer()
    output
  - Fixed TabularGenerator method documentation
  - Fixed GAN_value_fct missing epsilon parameter documentation

Dependencies

  - OpenDP package now suggested (optional) for cryptographically secure
    DP training

                 Changes in version 0.1.1 (2022-03-29)                  

  - Minor CRAN compliance fixes
  - Added track_loss parameter to gan_trainer() and gan_update_step()

                        Changes in version 0.1.0                        

  - Added a NEWS.md file to track changes to the package.
  - This is the initial release of RGAN