A Collection of Change-Point Detection Methods R package changepoints version 1.1.0 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>. Software Depends: R (>= 3.5.0) Imports: stats, gglasso, glmnet, ks, MASS, data.tree, Rcpp LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, abind, DiagrammeR, rmarkdown Haotian Xu <haotian.xu@uclouvain.be> Comprehensive R Archive Network (CRAN) Haotian Xu [aut, cre], Oscar Padilla [aut], Daren Wang [aut], Mengchu Li [aut], Qin Wen [ctb] GPL (>= 3) 2022-09-04 application/tgz https://CRAN.R-project.org/package=changepoints doi:10.32614/CRAN.package.changepoints