R/changepoints-package.R

In changepoints: A Collection of Change-Point Detection Methods

#' changepoints-package: A Collections of Change-Point Detection Methods 
#'
#' 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 Wang et al. (2020) <arXiv:1810.09498>; Yu et al. (2020) <arXiv:2006.03283>; Yu and Chatterjee (2020) <arXiv:2007.09910>; Padilla et al. (2021) <arXiv:1905.10019>; Padilla et al. (2019) <arXiv:1910.13289>; Wang et al. (2021) <arXiv:1712.09912>; Wang et al. (2018) <arXiv:1809.09602>; Padilla et al. (2019) <arXiv:1911.07494>; Yu et al. (2021) <arXiv:2101.05477>; Rinaldo et al. (2020) <arXiv:2010.10410>; Wang et al. (2019) <arXiv:1909.06359>; Wang et al. (2020) <arXiv:2006.03572>; Dubey et al. (2021) <arXiv:2110.06450>; Li and Yu (2021) <arXiv:2105.10417>.
#'
#' @docType package
#' @aliases changepoints-package
#' @import stats gglasso glmnet ks MASS data.tree Rcpp
#' @importFrom utils setTxtProgressBar txtProgressBar
#' @useDynLib changepoints
#' @name changepoints
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