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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | blockwise_0.1.2.tar.gz |
697.0 KiB |
0.1.2 |
rolling linux/noble R-4.5 | blockwise_0.1.2.tar.gz |
696.9 KiB |
0.1.2 |
rolling source/ R- | blockwise_0.1.2.tar.gz |
473.3 KiB |
0.1.2 |
latest linux/jammy R-4.5 | blockwise_0.1.2.tar.gz |
697.0 KiB |
0.1.2 |
latest linux/noble R-4.5 | blockwise_0.1.2.tar.gz |
696.9 KiB |
0.1.2 |
latest source/ R- | blockwise_0.1.2.tar.gz |
473.3 KiB |
0.1.2 |
2026-04-23 source/ R- | blockwise_0.1.2.tar.gz |
0 B |