Linear Regression with Missing Data
Overview
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).
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK12 OK2026-04-25
Show 1 earlier changeHide 1 earlier change
- NOTE11 OK, 3 NOTE2026-03-10
Documentation
- Help pages
- 4
- Examples that run
- 100%4 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 75% with a bibliography file
Downloads
Repository
Checks run against github.com/benrisebrow/lrmiss on 2026-10-11.
No development-tooling practices detected in the upstream repository.
Dependencies
Installing LRMiss adds 18 packages (12 with compiled code, 22 MB of source tarballs). Heaviest: stringi 11 MB, data.table 5.7 MB, rbibutils 1.1 MB.
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- 0.0.1Latest2026-02-20 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-02-20
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 32 KB
Cite
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