SCoRES: An R Package for Simultaneous Confidence Region Estimates

TL;DR

The paper introduces the SCoRES R package, enabling non-asymptotic simultaneous confidence region estimation for inverse domain problems across various data types, with visualization tools and broad applicability demonstrated through three examples.

Contribution

It presents a new R package implementing a recent method for constructing simultaneous confidence regions, overcoming limitations of previous approaches.

Findings

Successfully estimates inverse regions with confidence guarantees

Provides visualization tools for confidence regions

Demonstrates applicability across diverse data types

Abstract

The identification of domain sets whose outcomes belong to predefined subsets can address fundamental risk assessment challenges in climatology and medicine. Existing approaches for inverse domain estimates require restrictive assumptions, including domain density and continuity of function near thresholds, and large-sample guarantees, which limit the applicability. Besides, the estimation and coverage depend on setting a fixed threshold level, which is difficult to determine. Recently, Ren et al. (2024) proved that confidence sets of multiple levels can be simultaneously constructed with the desired confidence non-asymptotically through inverting simultaneous confidence bands. Here, we present the SCoRES R package, which implements Ren's approach for both the estimation of the inverse region and the corresponding simultaneous outer and inner confidence regions, along with visualization tools. Besides, the package also provides functions that help construct SCBs for regression data, functional data and geographical data. To illustrate its broad applicability, we present three rigorous examples that demonstrate the SCoRES workflow.