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Narrowest-Over-Threshold Change-Point Detection
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) <doi:10.1111/rssb.12322>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.6 |
rolling linux/jammy R-4.5 | not_1.6.tar.gz |
70.7 KiB |
1.6 |
rolling linux/noble R-4.5 | not_1.6.tar.gz |
70.4 KiB |
1.6 |
rolling source/ R- | not_1.6.tar.gz |
22.5 KiB |
1.6 |
latest linux/jammy R-4.5 | not_1.6.tar.gz |
70.7 KiB |
1.6 |
latest linux/noble R-4.5 | not_1.6.tar.gz |
70.4 KiB |
1.6 |
latest source/ R- | not_1.6.tar.gz |
22.5 KiB |
1.6 |
2026-04-26 source/ R- | not_1.6.tar.gz |
22.5 KiB |
1.6 |
2026-04-23 source/ R- | not_1.6.tar.gz |
22.5 KiB |
1.6 |
2026-04-09 windows/windows R-4.5 | not_1.6.zip |
191.3 KiB |
1.6 |
2025-04-20 source/ R- | not_1.6.tar.gz |
22.5 KiB |