Linear Regression with Missing Data R package LRMiss version 0.0.1 Provides methods for linear regression in the presence of missing data, including missingness in covariates and responses. The package implements two estimators: oss_estimator(), a low-dimensional semi-supervised method, and dantzig_missing(), a high-dimensional approach. The tuning parameter can be selected automatically via cv_dantzig_missing(). See Risebrow and Berrett (2026) <doi:10.48550/arXiv.2602.13729>. Optional support for the 'gurobi' optimizer via the 'gurobi' R package (available from Gurobi, see <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html>). Software Imports: MASS, stats, Rglpk, fastDummies, Rdpack Suggests: gurobi Benedict Risebrow <Benedict.risebrow@warwick.ac.uk> Comprehensive R Archive Network (CRAN) Benedict Risebrow [aut, cre], Thomas Berrett [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=LRMiss/LICENSE) 2026-02-20 application/tgz https://CRAN.R-project.org/package=LRMiss doi:10.32614/CRAN.package.LRMiss