Package: LRMiss
Type: Package
Title: Linear Regression with Missing Data
Version: 0.0.1
Authors@R: c(
    person("Benedict", "Risebrow",
        email = "Benedict.risebrow@warwick.ac.uk",
        role = c("aut", "cre")),
    person("Thomas", "Berrett",
        role = "aut")
    )
Description: 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>).
Imports: MASS, stats, Rglpk, fastDummies, Rdpack
Suggests: gurobi
RdMacros: Rdpack
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2.9000
URL: https://github.com/benrisebrow/LRMiss
BugReports: https://github.com/benrisebrow/LRMiss/issues
NeedsCompilation: no
Packaged: 2026-02-17 10:26:59 UTC; u5646697
Author: Benedict Risebrow [aut, cre],
  Thomas Berrett [aut]
Maintainer: Benedict Risebrow <Benedict.risebrow@warwick.ac.uk>
Repository: CRAN
Date/Publication: 2026-02-20 08:10:10 UTC
Built: R 4.6.0; ; 2026-04-26 08:31:05 UTC; unix
