DebiasInfer: Efficient Inference on High-Dimensional Linear Model with Missing Outcomes

A statistically and computationally efficient debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes. The reference paper is Zhang, Giessing, and Chen (2023) <doi:10.48550/arXiv.2309.06429>.

Version: 0.2
Imports: CVXR, caret, stats
Suggests: MASS, glmnet
Published: 2023-10-09
Author: Yikun Zhang ORCID iD [aut, cre], Alexander Giessing ORCID iD [aut], Yen-Chi Chen ORCID iD [aut]
Maintainer: Yikun Zhang <yikunzhang at foxmail.com>
BugReports: https://github.com/zhangyk8/Debias-Infer/issues
License: MIT + file LICENSE
URL: https://github.com/zhangyk8/Debias-Infer/
NeedsCompilation: no
CRAN checks: DebiasInfer results

Documentation:

Reference manual: DebiasInfer.pdf

Downloads:

Package source: DebiasInfer_0.2.tar.gz
Windows binaries: r-devel: DebiasInfer_0.2.zip, r-release: DebiasInfer_0.2.zip, r-oldrel: DebiasInfer_0.2.zip
macOS binaries: r-release (arm64): DebiasInfer_0.2.tgz, r-oldrel (arm64): DebiasInfer_0.2.tgz, r-release (x86_64): DebiasInfer_0.2.tgz
Old sources: DebiasInfer archive

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