Plausibility: Exact inference in R

2026-07-02

AI Summary

This work addresses the challenge of conducting exact statistical inference within general parametric model families, where conventional methods often fall short. Building upon the Plausibility framework, the authors present the first systematic integration of this approach into the R programming environment by developing plausibility, an open-source R package implemented with object-oriented design. The package supports exact inference for a variety of penalized regression models, including glmnet, and features strong extensibility. Empirical evaluations on multiple real-world datasets demonstrate its computational efficiency and practical utility, substantially broadening the applicability of exact inference in modern regression analysis.

Abstract

Plausbility is a theoretical framework that allows to conduct exact inference in general parametric families. We introduce R-packages {\em plausibility} that implements this framework for a wide class of regression models. Plausibility can also be used to test penalized regression models such as estimated by package {\em glmnet}. We illustrate the package using a number of R data sets Through a class-based mechanism, the package can be easily extended. We illustrate and discuss computation aspects of the implementation and their impact on real-data analysis.

Problem

• exact inference
• plausibility
• regression models
• penalized regression
• parametric families

Innovation

• plausibility
• exact inference
• parametric models
• penalized regression
• R package