Package: changepoints
Type: Package
Title: A Collection of Change-Point Detection Methods
Version: 1.1.0
Date: 2022-08-25
Authors@R: 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")
    )
Maintainer: Haotian Xu <haotian.xu@uclouvain.be>
Description: 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>.
Depends: R (>= 3.5.0)
Imports: stats, gglasso, glmnet, ks, MASS, data.tree, Rcpp
Suggests: knitr, abind, DiagrammeR, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
License: GPL (>= 3)
RoxygenNote: 7.2.1
Encoding: UTF-8
URL: https://github.com/HaotianXu/changepoints
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-08-25 15:59:18 UTC; haotianxu
Author: Haotian Xu [aut, cre],
  Oscar Padilla [aut],
  Daren Wang [aut],
  Mengchu Li [aut],
  Qin Wen [ctb]
Repository: CRAN
Date/Publication: 2022-09-04 14:30:08 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 16:24:13 UTC; windows
Archs: i386, x64
