Reduced Modeling for Tabular Data with Blockwise Missingness R package blockwise version 0.1.2 Supervised learning on tabular data with blockwise missing patterns, using the Blockwise Reduced Modeling (BRM) method of Srinivasan, Currim, and Ram (2025) <doi:10.1287/ijds.2022.9016>. BRM partitions the training data into overlapping subsets based on per-row feature-missing patterns, fits one user-supplied learner per subset with minimal imputation, and at prediction time routes each test instance to the best-matching subset model. The interface is learner-agnostic: any fit-and-predict pair can be plugged in, and convenience specifications are provided for linear models, tree models, random forests, and gradient boosting. Software Depends: R (>= 3.6.0) Imports: stats, VIM, withr Suggests: testthat (>= 3.0.0), knitr, rmarkdown, rpart, ranger, gbm, ggplot2 Karthik Srinivasan <karthiks@ku.edu> Comprehensive R Archive Network (CRAN) Karthik Srinivasan [aut, cre] (ORCID: <https://orcid.org/0000-0002-1608-6190>), Faiz Currim [aut], Sudha Ram [aut] GPL-3 2026-06-24 application/tgz https://CRAN.R-project.org/package=blockwise doi:10.32614/CRAN.package.blockwise en-US