Type: Package
Package: fastcpd
Title: Fast Change Point Detection via Sequential Gradient Descent
Version: 0.9.0
Authors@R: c(
    person("Xingchi", "Li", , "anthony.li@stat.tamu.edu", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0009-0006-2493-0853")),
    person("Xianyang", "Zhang", , "zhangxiany@stat.tamu.edu", role = c("aut", "cph")),
    person("Trisha", "Dawn", , "trisha@stat.tamu.edu", role = c("aut", "cph"))
  )
Description: Implements fast change point detection algorithm based on the
    paper "Sequential Gradient Descent and Quasi-Newton's Method for
    Change-Point Analysis" by Xianyang Zhang, Trisha Dawn
    <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm
    is based on dynamic programming with pruning and sequential gradient
    descent. It is able to detect change points a magnitude faster than
    the vanilla Pruned Exact Linear Time(PELT). The package includes examples
    of linear regression, logistic regression, Poisson regression, penalized
    linear regression data, and whole lot more examples with custom cost
    function in case the user wants to use their own cost function.
License: GPL (>= 3)
URL: https://fastcpd.xingchi.li, https://github.com/doccstat/fastcpd
BugReports: https://github.com/doccstat/fastcpd/issues
Imports: DescTools, fastglm, glmnet, Matrix, methods, Rcpp (>= 0.11.0),
        stats, utils
Suggests: abind, forecast, ggplot2, knitr, mockthat, mvtnorm,
        rmarkdown, testthat (>= 3.0.0), xml2
LinkingTo: Rcpp, RcppArmadillo, testthat
Config/testthat/edition: 3
Encoding: UTF-8
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2023-10-18 22:16:15 UTC; doccstat
Author: Xingchi Li [aut, cre, cph] (<https://orcid.org/0009-0006-2493-0853>),
  Xianyang Zhang [aut, cph],
  Trisha Dawn [aut, cph]
Maintainer: Xingchi Li <anthony.li@stat.tamu.edu>
Repository: CRAN
Date/Publication: 2023-10-19 07:00:13 UTC
