High-Performance Open-Source Archive
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.
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