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Changelog

Source: NEWS.md

arf (development version)

  • Add mirai/mori parallel backend as an alternative to foreach/doParallel (#62)
    • Shares large read-only inputs (training data, forest, learned parameters) across workers via mori, lowering memory use in adversarial_rf(), forde(), forge(), expct(), and lik()
    • Enable with active mirai daemons or options(arf.backend), see ?arf-options
    • Parallel calls now report the backend in use once per session, including when parallel = TRUE finds no backend and runs sequentially; silence with options(arf.verbose = FALSE)
  • Reduce peak memory of expct() by processing conditions in bounded blocks (16x lower on one large internal case, at 12-27% more run time; tune via options(arf.block_rows), see ?arf-options)
  • Reduce memory and dispatch overhead in forde() (fused per-tree parameter pass, per-tree coverage) and lik() (per-batch reduction)
  • Fix forge() and lik() failing under non-forking foreach adapters (doParallel PSOCK clusters, doFuture multisession) with “object not found” errors: parallel worker bodies now take all inputs as explicit arguments (#62)
  • Fix darf(), rarf() and earf() ignoring a pre-trained arf or params passed via ...: they retrained and recomputed anyway, then failed with “formal argument ‘params’ matched by multiple actual arguments”
  • Fix a crash when arf runs with parallel = TRUE inside a forked worker (future::plan(multicore), mclapply(), fork-based doParallel) while mirai daemons exist in the parent process: the child inherited the daemon connection and used it, which aborts the process since mirai is not fork-safe. Forked children now ignore the parent’s daemons and use the foreach path; forcing options(arf.backend = "mirai") there errors with an explanation

arf 0.2.5

CRAN release: 2026-09-21

  • 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

CRAN release: 2025-02-24

  • 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

CRAN release: 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

arf 0.1.3

CRAN release: 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)