# blockwise 0.1.2

CRAN resubmission — no code changes; fresh win-builder check to clear a
stale build-timestamp note.

# blockwise 0.1.1

CRAN resubmission addressing reviewer feedback.

## Documentation

* Replaced `\dontrun{}` with `\donttest{}` in the `brm()` example and
  unwrapped the `learner()` example (it runs instantly).

## Internal changes

* Removed hardcoded `set.seed()` calls inside `choose_num_blocks()` and
  `best_kmeans()`; these were unnecessary and modified the caller's RNG
  state.
* `simulate_blockwise_missing()` no longer calls `set.seed()` directly.
  The `seed` argument now defaults to `NULL` (use the caller's RNG); when
  supplied, the seed is applied locally via `withr::with_seed()` so the
  caller's RNG state is preserved.
* Added `withr` to `Imports`.

# blockwise 0.1.0

First public release. Initial CRAN submission.

## Features

* `brm()` — fit a Blockwise Reduced Modeling ensemble (S3 class `"brm"`).
* `predict.brm()` — route test instances to their best-matching subset model.
* `choose_num_blocks()` — elbow heuristic for the number of blocks.
* `learner()` — learner-agnostic fit/predict specification; convenience
  builders for linear models (`learner_lm`, `learner_glm_binomial`),
  trees (`learner_rpart`), random forests (`learner_ranger`), and
  gradient boosting (`learner_gbm`).
* `simulate_blockwise_missing()` — mask complete data with a blockwise
  missing pattern for benchmarking.
* Bundled datasets: `bike`, `adult`, `house` — the three benchmark
  datasets used in Srinivasan, Currim, and Ram (2025)
  <doi:10.1287/ijds.2022.9016>.
