Linear Regression with Missing Data

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) . 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>).