Changes in version 0.2.4 (2025-02-24)                  

  - Let verbose=FALSE silence (some) warnings

                        Changes in version 0.2.3                        

  - Add impute() function for direct missing data imputation with ARF
  - Add one-line functions darf(), earf(), rarf()

                        Changes in version 0.2.2                        

  - Faster and vectorized conditional sampling
  - Use min.bucket argument from ranger to avoid pruning if possible
  - Option to sample NAs in generated data if original data contains NAs
  - Stepsize in forge() to reduce memory usage
  - Option for local and global finite bounds

                 Changes in version 0.2.0 (2024-01-24)                  

  - Vectorized adversarial resampling
  - Speed boost for compiling into a probabilistic circuit
  - Conditional densities and sampling
  - Bayesian solution for invariant continuous data within leaf nodes
  - New function for computing (conditional) expectations
  - Options for missing data

                 Changes in version 0.1.3 (2023-02-06)                  

  - Speed boost for the adversarial resampling step
  - Early stopping option for adversarial training
  - alpha parameter for regularizing multinomial distributions in forde
  - Unified treatment of colnames with internal semantics (y, obs, tree,
    leaf)