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Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.
| Version: | 2.0-1 |
| Depends: | R (≥ 3.1.0) |
| Imports: | MASS |
| Published: | 2023-06-21 |
| Author: | Zhibing He |
| Maintainer: | Zhibing He <zhibingh at asu.edu> |
| License: | GPL-3 |
| URL: | https://doi.org/10.1214/20-EJS1751, https://doi.org/10.1214/16-AOS1458 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | dSTEM results |
| Reference manual: | dSTEM.pdf |
| Package source: | dSTEM_2.0-1.tar.gz |
| Windows binaries: | r-devel: dSTEM_2.0-1.zip, r-release: dSTEM_2.0-1.zip, r-oldrel: dSTEM_2.0-1.zip |
| macOS binaries: | r-release (arm64): dSTEM_2.0-1.tgz, r-oldrel (arm64): dSTEM_2.0-1.tgz, r-release (x86_64): dSTEM_2.0-1.tgz, r-oldrel (x86_64): dSTEM_2.0-1.tgz |
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