LRMiss - 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 the associated methodology paper for details.