Description: Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) <doi:10.18637/jss.v058.i03> ).

Title: Change Point Tests for Joint Distributions and Copulas
Authors@R: c( person(c("Bouchra","R"), "Nasri", role = c("aut"), email = "bouchra.nasri@umontreal.ca"),
person(c("Bruno","N"), "Remillard", role = c("aut", "cre","cph"),email = "bruno.remillard@hec.ca"))
Maintainer: Bruno N Remillard <bruno.remillard@hec.ca>
Description: Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers <doi:10.1016/j.jmva.2014.07.012> and Nasri & Remillard <doi:10.1016/j.jmva.2019.03.002>.
Packaged: 2021-07-26 15:07:08 UTC; Utilisateur

Title: A Collection of Change-Point Detection Methods
Description: Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean; 2) univariate polynomials; 3) univariate and multivariate nonparametric settings; 4) high-dimensional covariances; 5) high-dimensional networks with and without missing values; 6) high-dimensional linear regression models; 7) high-dimensional vector autoregressive models; 8) high-dimensional self exciting point processes; 9) dependent dynamic nonparametric random dot product graphs; 10) univariate mean against adversarial attacks. For more informations, see <arXiv:1810.09498>; <arXiv:2006.03283>; <arXiv:2007.09910>; <arXiv:1905.10019>; <arXiv:1910.13289>; <arXiv:1712.09912>; <arXiv:1809.09602>; <arXiv:1911.07494>; <arXiv:2101.05477>; <arXiv:2010.10410>; <arXiv:1909.06359>; <arXiv:2006.03572>; <arXiv:2110.06450>; <arXiv:2105.10417>.
Imports: stats, gglasso, glmnet, ks, MASS, data.tree, Rcpp
Suggests: knitr, abind, DiagrammeR, rmarkdown

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>.
Packaged: 2022-08-25 15:59:18 UTC; haotianxu