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blockwise  

Reduced Modeling for Tabular Data with Blockwise Missingness
View on CRAN: Click here


Download and install blockwise package within the R console
Install from CRAN:
install.packages("blockwise")

Install from Github:
library("remotes")
install_github("cran/blockwise")

Install by package version:
library("remotes")
install_version("blockwise", "0.1.2")



Attach the package and use:
library("blockwise")
Maintained by
Karthik Srinivasan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-24
Latest Update: 2026-06-24
Description:
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.
How to cite:
Karthik Srinivasan (2026). blockwise: Reduced Modeling for Tabular Data with Blockwise Missingness. R package version 0.1.2, https://cran.r-project.org/web/packages/blockwise. Accessed 12 Sep. 2026.
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