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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.
Getting started
Browse package contents
Package details |
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| Author | Xingchi Li [aut, cre, cph] (<https://orcid.org/0009-0006-2493-0853>), Xianyang Zhang [aut, cph] |
| Maintainer | Xingchi Li <anthony.li@stat.tamu.edu> |
| License | GPL (>= 3) |
| Version | 0.14.3 |
| URL | https://fastcpd.xingchi.li https://github.com/doccstat/fastcpd |
| Package repository | View on CRAN |
| Installation |
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