Package: blockwise
Type: Package
Title: Reduced Modeling for Tabular Data with Blockwise Missingness
Version: 0.1.2
Authors@R: c(
    person("Karthik", "Srinivasan", email = "karthiks@ku.edu",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-1608-6190")),
    person("Faiz",  "Currim", role = "aut"),
    person("Sudha", "Ram",    role = "aut"))
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.
License: GPL-3
Language: en-US
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
Depends: R (>= 3.6.0)
Imports: stats, VIM, withr
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, rpart, ranger, gbm,
        ggplot2
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://github.com/KarAnalytics/blockwise
BugReports: https://github.com/KarAnalytics/blockwise/issues
NeedsCompilation: no
Packaged: 2026-06-25 08:18:00 UTC; root
Author: Karthik Srinivasan [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-1608-6190>), Faiz Currim [aut],
        Sudha Ram [aut]
Maintainer: Karthik Srinivasan <karthiks@ku.edu>
Config/pak/sysreqs: cmake make libicu-dev
Repository: https://karanalytics.r-universe.dev
Date/Publication: 2026-06-24 08:36:47 UTC
RemoteUrl: https://github.com/cran/blockwise
RemoteRef: HEAD
RemoteSha: 316b011c7d26b9a8e8ced4ba7089bec091f38adb