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)