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
<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.
| 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  [aut,
cre, cph],
Xianyang Zhang [aut, cph],
Trisha Dawn [aut, cph] |
| Maintainer: |
Xingchi Li <anthony.li at stat.tamu.edu> |
| BugReports: |
https://github.com/doccstat/fastcpd/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://fastcpd.xingchi.li, https://github.com/doccstat/fastcpd |
| NeedsCompilation: |
yes |
| Citation: |
fastcpd citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
fastcpd results |
Documentation:
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