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>).

Version:	0.0.1
Imports:	MASS, stats, Rglpk, fastDummies, Rdpack
Suggests:	gurobi
Published:	2026-02-20
DOI:	10.32614/CRAN.package.LRMiss (may not be active yet)
Author:	Benedict Risebrow [aut, cre], Thomas Berrett [aut]
Maintainer:	Benedict Risebrow <Benedict.risebrow at warwick.ac.uk>
BugReports:	https://github.com/benrisebrow/LRMiss/issues
License:	MIT + file LICENSE
URL:	https://github.com/benrisebrow/LRMiss
NeedsCompilation:	no
CRAN checks:	LRMiss results

Documentation:

Reference manual:

LRMiss.html , LRMiss.pdf

Downloads:

Package source:	LRMiss_0.0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): LRMiss_0.0.1.tgz, r-oldrel (arm64): LRMiss_0.0.1.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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