# HCmodelSets: An R package for specifying sets of well-fitting models in regression with a large number of potential explanatory variables

## TL;DR

This paper introduces the R package HCmodelSets, which helps identify multiple well-fitting models in high-dimensional regression, emphasizing the importance of considering alternative explanations rather than a single predictive model.

## Contribution

The paper presents an R implementation for specifying multiple plausible models in high-dimensional regression, aligning with recent theoretical ideas on model uncertainty.

## Key findings

- Effective in identifying multiple well-fitting models
- Demonstrated with simple examples and real data
- Supported by simulation experiments

## Abstract

In the context of regression with a large number of explanatory variables, Cox and Battey (2017) emphasize that if there are alternative reasonable explanations of the data that are statistically indistinguishable, one should aim to specify as many of these explanations as is feasible. The standard practice, by contrast, is to report a single model effective for prediction. The present paper illustrates the R implementation of the new ideas in the package `HCmodelSets', using simple reproducible examples and real data. Results of some simulation experiments are also reported.