# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "blockwise" in publications use:' type: software license: GPL-3.0-only title: 'blockwise: Reduced Modeling for Tabular Data with Blockwise Missingness' version: 0.1.2 doi: 10.1287/ijds.2022.9016 abstract: 'Supervised learning on tabular data with blockwise missing patterns, using the Blockwise Reduced Modeling (BRM) method of Srinivasan, Currim, and Ram (2025) . 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: - family-names: Srinivasan given-names: Karthik email: karthiks@ku.edu orcid: https://orcid.org/0000-0002-1608-6190 - family-names: Currim given-names: Faiz - family-names: Ram given-names: Sudha preferred-citation: type: article title: A Reduced Modeling Approach for Making Predictions With Incomplete Data Having Blockwise Missing Patterns authors: - family-names: Srinivasan given-names: Karthik email: karthiks@ku.edu orcid: https://orcid.org/0000-0002-1608-6190 - family-names: Currim given-names: Faiz - family-names: Ram given-names: Sudha journal: INFORMS Journal on Data Science year: '2025' doi: 10.1287/ijds.2022.9016 repository: https://cran.r-universe.dev repository-code: https://github.com/KarAnalytics/blockwise commit: 316b011c7d26b9a8e8ced4ba7089bec091f38adb url: https://github.com/KarAnalytics/blockwise date-released: '2026-06-24' contact: - family-names: Srinivasan given-names: Karthik email: karthiks@ku.edu orcid: https://orcid.org/0000-0002-1608-6190