blockwise: Reduced Modeling for Tabular Data with Blockwise Missingness

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.

Version:	0.1.2
Depends:	R (≥ 3.6.0)
Imports:	stats, VIM, withr
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, rpart, ranger, gbm, ggplot2
Published:	2026-06-24
DOI:	10.32614/CRAN.package.blockwise (may not be active yet)
Author:	Karthik Srinivasan [aut, cre], Faiz Currim [aut], Sudha Ram [aut]
Maintainer:	Karthik Srinivasan <karthiks at ku.edu>
BugReports:	https://github.com/KarAnalytics/blockwise/issues
License:	GPL-3
URL:	https://github.com/KarAnalytics/blockwise
NeedsCompilation:	no
Language:	en-US
Citation:	blockwise citation info
Materials:	NEWS
CRAN checks:	blockwise results

Documentation:

Reference manual:	blockwise.html , blockwise.pdf
Vignettes:	BRM on the adult dataset (binary classification) (source, R code) BRM on the bike dataset (regression) (source, R code) BRM on the house dataset (regression) (source, R code)

Downloads:

Package source:	blockwise_0.1.2.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): blockwise_0.1.2.tgz, r-oldrel (arm64): blockwise_0.1.2.tgz, r-release (x86_64): blockwise_0.1.2.tgz, r-oldrel (x86_64): blockwise_0.1.2.tgz

Linking:

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