Package: IDetect 0.1.0

Andreas Anastasiou

IDetect: Isolate-Detect Methodology for Multiple Change-Point Detection

Provides efficient implementation of the Isolate-Detect methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the "deterministic + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>. Currently implemented scenarios are: piecewise-constant signal with Gaussian noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.

Authors:Andreas Anastasiou [aut, cre], Piotr Fryzlewicz [aut]

IDetect_0.1.0.tar.gz
IDetect_0.1.0.tar.gz(r-4.5-noble)IDetect_0.1.0.tar.gz(r-4.4-noble)
IDetect_0.1.0.tgz(r-4.4-emscripten)IDetect_0.1.0.tgz(r-4.3-emscripten)
IDetect.pdf |IDetect.html✨
IDetect/json (API)

# Install 'IDetect' in R:
install.packages('IDetect', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

17 exports 0.61 score 0 dependencies 1 dependents 1 mentions 24 scripts 149 downloads

Last updated 7 years agofrom:d0c271590d. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 11 2024
R-4.5-linuxOKSep 11 2024

Exports:cplm_iccplm_thest_signalht_ID_cplmht_ID_pcmIDID_cplmID_pcmnormalisepcm_icpcm_thresid_IDs_e_pointssol_path_cplmsol_path_pcmwin_cplm_thwin_pcm_th

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Multiple change-point detection in a continuous piecewise-linear signal via minimising an information criterioncplm_ic
Multiple change-point detection in a continuous, piecewise-linear signal via thresholdingcplm_th
Estimate the signalest_signal
Apply the Isolate-Detect methodology for multiple change-point detection in a continuous, piecewise-linear vector with non Gaussian noiseht_ID_cplm
Apply the Isolate-Detect methodology for multiple change-point detection in the mean of a vector with non Gaussian noiseht_ID_pcm
Multiple change-point detection in piecewise-constant or continuous, piecewise-linear signals using the Isolate-Detect methodologyID
Multiple change-point detection for a continuous, piecewise-linear signal using the Isolate-Detect methodologyID_cplm
Multiple change-point detection in the mean of a vector using the Isolate-Detect methodologyID_pcm
IDetect: Multiple generalised change-point detection using the Isolate-Detect methodologyIDetect-package IDetect
Transform the noise to be closer to the Gaussian distributionnormalise
Multiple change-point detection in the mean via minimising an information criterionpcm_ic
Multiple change-point detection in the mean via thresholdingpcm_th
Calculate the residuals related to the estimated signalresid_ID
Derives a subset of integers from a given sets_e_points
The solution path for the case of continuous piecewise-linear signalssol_path_cplm
The solution path for the case of piecewise-constant signalssol_path_pcm
A windows-based approach for multiple change-point detection in a continuous, piecewise-linear signal via thresholdingwin_cplm_th
A windows-based approach for multiple change-point detection in the mean via thresholdingwin_pcm_th