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.