NEWS

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BATTS 0.3.0

• The defaults of boots() and batts() are now the settings of the paper's simulations. boots(): gradient boosting (use_gradient = TRUE), at most 1000 trees (num_trees_max) selected by 5-fold CV (K_CV = 5) and per-tree subsampling of one half (subsample_fraction = 0.5); depth 4, learning rate 0.01, 32 bins and margin 0.1 are unchanged. batts(): 200 trees (num_trees), 2000 burn-in iterations (size_burnin) and 1000 saved draws (size_backfitting, previously required); the other defaults are unchanged.
• Calls that relied on the previous defaults now give different results and use the random number generator for CV folds and subsamples. Supply the previous values explicitly to keep earlier behaviour. Estimation code is unchanged; fits with the same explicit settings are identical to 0.2.2.
• The paper's simulations used 0.2.2 (tag v0.2.2) with explicit settings.
• The README example and example.R use the new defaults, with a shorter MCMC run (500 burn-in iterations and 500 draws) for a quick demonstration.

BATTS 0.2.2

• Add subsample_fraction to boots() (default 1, unchanged behavior and RNG use). A value below one builds each tree from a random subsample of ceiling(subsample_fraction * m) training observations, drawn as in ada with bag.frac: one observation per group, the rest uniformly without replacement regardless of group. Splits, gradients and leaf values use the subsample; all weights are updated, and the forward-stagewise normalization and CV losses use the whole training set and held-out folds, respectively.
• The internal run_adaboost() entry point takes the per-tree subsample indices as an additional argument; batts() does not subsample.

BATTS 0.2.1

• Reject invalid burn-in and retained-draw counts before fitting (U22).
• Reject move probabilities outside the documented positive, length-three, unit-sum contract in R and C++; the sum tolerance is 1e-12 (U23).
• These guards preserve valid-input calculations and RNG consumption. Invalid inputs now fail before fitting. Inverse-Gaussian limitation U16 is unchanged.

BATTS 0.2.0

• Require a positive integer thin and validate prediction matrix dimensions and finite values before passing data to C++ (U14, U17).
• Breaking API change: remove update_lambda, lambda_prior_parameters, and alpha_cutpoint. Leaf scale stays fixed at num_trees * lambda_0; cut spacing/proposals use the previous default. Remove these named arguments from existing calls. Nondefault cut grids and scale updating are no longer supported. The C++ interface is regenerated with the reduced argument list.
• Document inverse-Gaussian cancellation under extreme parameters as known limitation U16; its numerical formula is unchanged by author decision.

BATTS 0.1.0

Official distribution: https://github.com/nawaya040/BATTS.

Fixes integrated from development

This release integrates commits 6ea6722 through 909ea357 after the previous default-branch commit 6f625ba. They correct forward-stagewise normalization and prediction constants, posterior-forest storage and guards, CV group handling, gradient CV scaling, split loss and split-boundary rules, PRUNE indices, and empty-leaf sampling and GROW moves. They add input guards and restrict Bayesian initialization to unsplit trees. They also reduce unnecessary copies, history allocation and internal-node index storage.

These existing changes can alter estimates and RNG streams. A run using 0.1.0 must record its own version and settings; numerical identity with the previous public version or every reported paper result is not asserted.

Additional release fixes

• Rename the prediction-only Node type to PredictionNode, removing its one-definition-rule collision with the fitting Node type.
• Require C++17 for compatibility with recent RcppArmadillo.
• Replace placeholder author/maintainer metadata, add API help and regression tests, and document the official release installation.

The new type-name fix preserves mathematical operations and RNG calls. Build-level equivalence and limitations are documented in inst/VALIDATION.md. No model, prior, canonical experiment setting or saved result is changed by this release preparation.