CRAN resubmission — no code changes; fresh win-builder check to clear a stale build-timestamp note.
CRAN resubmission addressing reviewer feedback.
\dontrun{} with \donttest{} in the brm() example and
unwrapped the learner() example (it runs instantly).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.withr to Imports.First public release. Initial CRAN submission.
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.bike, adult, house — the three benchmark
datasets used in Srinivasan, Currim, and Ram (2025)
doi:10.1287/ijds.2022.9016.