c(
person("Haotian", "Xu", email="haotian.xu@uclouvain.be", role=c("aut","cre")),
person("Oscar", "Padilla", email="oscar.madrid@stat.ucla.edu", role="aut"),
person("Daren", "Wang", email="dwang24@nd.edu", role="aut"),
person("Mengchu", "Li", email="Mengchu.Li@warwick.ac.uk", role="aut"),
person("Qin", "Wen", role="ctb")
)
Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.

Change-Point Estimation for Expensive and High-Dimensional
Models
This implements the methods developed in, L. Bybee and Y. Atchade. (2017) <arXiv:1707.04306>.  Contains a series of methods for estimating change-points given user specified black-box models.  The methods include binary segmentation for multiple change-point estimation. For estimating each individual change-point the package includes simulated annealing, brute force, and, for Gaussian graphical models, an application specific rank-one update implementation.  Additionally, code for estimating Gaussian graphical models is included.  The goal of this package is to allow for the efficient estimation of change-points in complicated models with high dimensional data.

This implements the methods developed in, L. Bybee and Y. Atchade. (2018).  Contains a series of methods for estimating change-points given user specified black-box models.  The methods include binary segmentation for multiple change-point estimation. For estimating each individual change-point the package includes simulated annealing, brute force, and, for Gaussian graphical models, an applications specific rank-one update implementation.  Additionally, code for estimating Gaussian graphical models is included.  The goal of this package is to allow for the efficient estimation of change-points in complicated models with high dimensional data.