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")