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

TL;DR

The paper introduces the SIHR R package for statistical inference in high-dimensional linear and logistic regression models, enabling confidence interval construction and hypothesis testing for various data settings.

Contribution

It provides a new software tool that facilitates inference in high-dimensional generalized linear models, which was previously challenging.

Findings

Effective in constructing confidence intervals

Performs well in real data applications

Supports both one-sample and two-sample settings

Abstract

We introduce the R package \CRANpkg{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 \CRANpkg{SIHR} through numerical examples and present real data applications to demonstrate the package's performance and practicality.