SIHR: Statistical Inference in High Dimensional Regression

The goal of SIHR is to provide inference procedures in the high-dimensional setting for (1) linear functionals in generalized linear regression ('Cai et al.' (2019) <doi:10.48550/arXiv.1904.12891>, 'Guo et al.' (2020) <doi:10.48550/arXiv.2012.07133>, 'Cai et al.' (2021)), (2) conditional average treatment effects in generalized linear regression, (3) quadratic functionals in generalized linear regression ('Guo et al.' (2019) <doi:10.48550/arXiv.1909.01503>). (4) inner product in generalized linear regression (5) distance in generalized linear regression.

Version: 2.0.1
Imports: CVXR, glmnet, stats
Published: 2023-04-09
Author: Prabrisha Rakshit, Zhenyu Wang, Tony Cai, Zijian Guo
Maintainer: Zijian Guo <zijguo at>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: SIHR results


Reference manual: SIHR.pdf


Package source: SIHR_2.0.1.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): SIHR_2.0.1.tgz, r-release (arm64): SIHR_2.0.1.tgz, r-oldrel (arm64): SIHR_2.0.1.tgz, r-prerel (x86_64): SIHR_2.0.1.tgz, r-release (x86_64): SIHR_2.0.1.tgz
Old sources: SIHR archive

Reverse dependencies:

Reverse imports: MaximinInfer


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