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LRMiss  

Linear Regression with Missing Data
View on CRAN: Click here


Download and install LRMiss package within the R console
Install from CRAN:
install.packages("LRMiss")

Install from Github:
library("remotes")
install_github("cran/LRMiss")

Install by package version:
library("remotes")
install_version("LRMiss", "0.0.1")



Attach the package and use:
library("LRMiss")
Maintained by
Benedict Risebrow
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-02-20
Latest Update: 2026-02-20
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>).
How to cite:
Benedict Risebrow (2026). LRMiss: Linear Regression with Missing Data. R package version 0.0.1, https://cran.r-project.org/web/packages/LRMiss. Accessed 24 Oct. 2026.
Previous versions and publish date:
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