Package: blockwise 0.1.2

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.

Authors:Karthik Srinivasan [aut, cre], Faiz Currim [aut], Sudha Ram [aut]

blockwise_0.1.2.tar.gz
blockwise_0.1.2.zip(r-4.7)blockwise_0.1.2.zip(r-4.6)blockwise_0.1.2.zip(r-4.5)
blockwise_0.1.2.tgz(r-4.6-any)blockwise_0.1.2.tgz(r-4.5-any)
blockwise_0.1.2.tar.gz(r-4.7-any)blockwise_0.1.2.tar.gz(r-4.6-any)
blockwise_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
blockwise/json (API)

# Install 'blockwise' in R:
install.packages('blockwise', repos = c('https://karanalytics.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/karanalytics/blockwise/issues

Datasets:
  • adult - UCI Adult income classification dataset
  • bike - Capital Bikeshare hourly demand data
  • house - King County, WA house sales

On CRAN:

Conda:

2.48 score 5 scripts 9 exports 108 dependencies

Last updated from:316b011c7d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK135
source / vignettesOK227
linux-release-x86_64OK203
macos-release-arm64OK153
macos-oldrel-arm64OK204
windows-develOK87
windows-releaseOK87
windows-oldrelOK91
wasm-releaseOK174

Exports:brmchoose_num_blockslearnerlearner_gbmlearner_glm_binomiallearner_lmlearner_rangerlearner_rpartsimulate_blockwise_missing

Dependencies:abindbackportsbbotkbootbroomcarcarDatacheckmateclassclicodetoolscolorspacecowplotcpp11data.tableDEoptimRDerivdigestdoBydplyre1071evaluatefarverforecastFormulafracdifffuturefuture.applygenericsggplot2globalsgluegtableisobandjsonlitelabelinglaekenlatticelgrlifecyclelistenvlme4lmtestmagrittrMASSMatrixMatrixModelsmatrixStatsmgcvmicrobenchmarkminqamiraimlbenchmlr3mlr3learnersmlr3measuresmlr3miscmlr3pipelinesmlr3tuningmodelrmoocorenanonextnlmenloptrnnetnumDerivpalmerpenguinsparadoxparallellypbkrtestpillarpkgconfigproxyPRROCpurrrquantregR6rangerrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrobustbaseS7scalesspSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8uuidvcdvctrsVIMviridisLitewithrxgboostzoo

BRM on the adult dataset (binary classification)
Overview | Inducing blockwise missingness | Train / test split | Fit BRM with a logistic-regression learner | Evaluate | Citation
Rendered fromadult.Rmdusingknitr::rmarkdown

Last update: 2026-06-24
Started: 2026-06-24

BRM on the bike dataset (regression)
Overview | Inducing a blockwise missing pattern | Train / test split | Fit BRM | Predict and score | Comparison to a listwise-deletion baseline | Citation
Rendered frombike.Rmdusingknitr::rmarkdown

Last update: 2026-06-24
Started: 2026-06-24

BRM on the house dataset (regression)
Overview | Induce missingness, split, fit | Citation
Rendered fromhouse.Rmdusingknitr::rmarkdown

Last update: 2026-06-24
Started: 2026-06-24