The aim of the inferchange package is to make methods for inference of changes in high-dimensional linear regression available to data analysts and researchers in statistics.

You can track (and contribute to) the development of inferchange at https://github.com/tobiaskley/inferchange. If you encounter unexpected behavior while using inferchange, please let us know by writing an email or by filing an issue.

Currently, the methodology described in the following pre-print is implemented:

• Cho, H., Kley, T., and Li, H. (2024). Detection and inference of changes in high-dimensional linear regression with non-sparse structures (arXiv).

First, if you have not done so already, install R from http://www.r-project.org (click on download R, select a location close to you, and download R for your platform). Once you have the latest version of R installed and started execute the following commands on the R shell:

install.packages("devtools")
devtools::install_github("tobiaskley/inferchange")

This will first install the R package devtools and then use it to install the latest (development) version of inferchange from the GitHub repository.

Now that you have R and inferchange installed you can access all the functions available. To load the package and access the help files:

library(inferchange)
help("inferchange")

At the bottom of the online help page to the package you will find an index to all the help files available. The main functions are McScan, lope, clom and ci_delta. The respective help pages can be accessed by

help("McScan")
help("lope")
help("clom")
help("ci_delta")

A "workhorse" function, named inferchange, that wraps the steps of a full change point analysis is also available. To access the help page call

help("inferchange")