SIHR: Statistical Inference in High-Dimensional Linear and Logistic Regression Models

We introduce the R package SIHR for statistical inference in high-dimensional generalized linear models with continuous and binary outcomes. The package provides functionalities for constructing confidence intervals and performing hypothesis tests for low-dimensional objectives in both one-sample and two-sample regression settings. We illustrate the usage of SIHR through simulated examples and present real data applications to demonstrate the package's performance and practicality.

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Publication Details

Journal
The R Journal
Published
2025-05-20
DOI
https://doi.org/10.32614/rj-2024-028
Primary Topic
Advanced Statistical Methods and Models
Type
article
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article

SIHR: Statistical Inference in High-Dimensional Linear and Logistic Regression Models

Zijian Guo, Prabrisha Rakshit, Zhenyu Wang, Tommaso Cai
The R Journal
Advanced Statistical Methods and Models
article

SIHR: Statistical Inference in High-Dimensional Linear and Logistic Regression Models

Zijian Guo, Prabrisha Rakshit, Zhenyu Wang, Tommaso Cai
article en

Abstract

We introduce the R package SIHR for statistical inference in high-dimensional generalized linear models with continuous and binary outcomes. The package provides functionalities for constructing confidence intervals and performing hypothesis tests for low-dimensional objectives in both one-sample and two-sample regression settings. We illustrate the usage of SIHR through simulated examples and present real data applications to demonstrate the package's performance and practicality.

The R JournalVol. 16(3)
Rutgers, The State University of New Jersey (US), California University of Pennsylvania (US)
Openalex Percentile: Top 86%
Advanced Statistical Methods and Models
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