fastcpd: Fast Change Point Detection via Sequential Gradient Descent

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 <>. 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.

Version: 0.9.0
Imports: DescTools, fastglm, glmnet, Matrix, methods, Rcpp (≥ 0.11.0), stats, utils
LinkingTo: Rcpp, RcppArmadillo, testthat
Suggests: abind, forecast, ggplot2, knitr, mockthat, mvtnorm, rmarkdown, testthat (≥ 3.0.0), xml2
Published: 2023-10-19
Author: Xingchi Li ORCID iD [aut, cre, cph], Xianyang Zhang [aut, cph], Trisha Dawn [aut, cph]
Maintainer: Xingchi Li < at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: fastcpd citation info
Materials: README NEWS
In views: TimeSeries
CRAN checks: fastcpd results


Reference manual: fastcpd.pdf


Package source: fastcpd_0.9.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): fastcpd_0.9.0.tgz, r-oldrel (arm64): fastcpd_0.9.0.tgz, r-release (x86_64): fastcpd_0.9.0.tgz, r-oldrel (x86_64): fastcpd_0.9.0.tgz
Old sources: fastcpd archive


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