- 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
- 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
- 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)