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 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>).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0.1 |
rolling linux/jammy R-4.5 | LRMiss_0.0.1.tar.gz |
73.4 KiB |
0.0.1 |
rolling linux/noble R-4.5 | LRMiss_0.0.1.tar.gz |
73.3 KiB |
0.0.1 |
rolling source/ R- | LRMiss_0.0.1.tar.gz |
32.7 KiB |
0.0.1 |
latest linux/jammy R-4.5 | LRMiss_0.0.1.tar.gz |
73.4 KiB |
0.0.1 |
latest linux/noble R-4.5 | LRMiss_0.0.1.tar.gz |
73.3 KiB |
0.0.1 |
latest source/ R- | LRMiss_0.0.1.tar.gz |
32.7 KiB |
0.0.1 |
2026-04-26 source/ R- | LRMiss_0.0.1.tar.gz |
32.7 KiB |
0.0.1 |
2026-04-23 source/ R- | LRMiss_0.0.1.tar.gz |
32.7 KiB |
0.0.1 |
2026-04-09 windows/windows R-4.5 | LRMiss_0.0.1.zip |
75.7 KiB |