Performs a series of offline and/or online change-point localisation algorithms for

1. univariate mean
   • Wang, Yu and Rinaldo (2020)
   • Yu, Padilla, Wang and Rinaldo (2020)
2. univariate polynomials
   • Yu, Chatterjee and Xu (2021)
3. univariate and multivariate nonparametric settings
   • Padilla, Yu, Wang and Rinaldo (2021)
   • Padilla, Yu, Wang and Rinaldo (2021)
4. high-dimensional covariances
   • Wang, Yu and Rinaldo (2021)
5. high-dimensional networks with and without missing values
   • Wang, Yu and Rinaldo (2021)
   • Yu, Padilla, Wang and Rinaldo (2021)
   • Dubey, Xu and Yu (2021)
6. high-dimensional linear regression models
   • Rinaldo, Wang, Wen, Willett and Yu (2021)
   • Xu, Wang, Zhao, and Yu (2022)
7. high-dimensional vector autoregressive models
   • Wang, Yu, Rinaldo and Willett (2019)
8. high-dimensional self exciting point processes
   • Wang, Yu and Willett (2020)
9. dependent dynamic nonparametric random dot product graphs
   • Padilla, Yu and Priebe (2019)
10. robust univariate mean against adversarial attacks
    • Li and Yu (2021)

Installation

Users must have a (C++) compiler installed on their machine that is compatible with R (e.g. Clang). The development version of changepoints from GitHub can be installed with:

## if not installed
## Install dependencies
install.packages(c("devtools","glmnet","gglasso","ks","data.tree"))
## install.packages("devtools")
devtools::install_github("HaotianXu/changepoints")

Example

This is an example for offline univariate mean change point detection by