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arf 0.2.5

• Behavior change: New mtry argument for adversarial_rf() with default max(2, floor(sqrt(p))) instead of ranger’s floor(sqrt(p)), which gave mtry = 1 for fewer than 4 features (#59)
• Export sample_from_leaves() for intra-leaf marginal sampling
• Avoid fractional recycling of factor column indices in sample_from_leaves() (#63)
• Fix nomatch = "force" fallback in forge() and expct() with evidence_row_mode = "separate" when evidence rows match no leaf (requires finite_bounds != "no" in forde()): errored with data.table input, and expct() silently filled impossible rows with misaligned values and returned NA-padded evidence columns for valid rows (#67)

arf 0.2.4

• Let verbose=FALSE silence (some) warnings

arf 0.2.3

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

arf 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

arf 0.2.0

• 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

arf 0.1.3

• 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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.