https://karanalytics.r-universe.devRecent package updates in karanalyticsR-universehttps://github.com/karanalytics.pnghttps://karanalytics.r-universe.devWed, 24 Jun 2026 08:36:47 GMTkarthiks@ku.edu (Karthik Srinivasan)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.https://github.com/r-universe/karanalytics/actions/runs/28156448241Wed, 24 Jun 2026 08:36:47 GMTblockwise0.1.2successhttps://karanalytics.r-universe.devhttps://github.com/cran/blockwiseadult.Rmdadult.htmlBRM on the adult dataset (binary classification)2026-06-24 08:36:472026-06-24 08:36:47bike.Rmdbike.htmlBRM on the bike dataset (regression)2026-06-24 08:36:472026-06-24 08:36:47house.Rmdhouse.htmlBRM on the house dataset (regression)2026-06-24 08:36:472026-06-24 08:36:47